before blowing, thanks to an infra-red pyrometer calibrated for each grade. In fact, different process temperatures associated to different heating power of infra-red lamps were explored: the temperature varying between 85 °C (above the glass transition temperature) and 115 °C (before cold crystallization). The injection pressure of nitrogen gas was set at 6.5 bars, for a duration of 200 ms. A cooling with air was applied at the end of the blowing so as to simulate the contact of the material on an industrial cold mold. For the different blowing temperatures, the five grades were processed, and the volume induced were measured, after the cooling of the bubble. The Figure 3 presents the evolution of the volumes induced as a function of the process temperature.

<figure><img src="image_2581.png" /><figcaption>Fig. 3: Volume variation of blown preforms, as a function of blowing temperature, produced with ISBM process, for virgin, 50R1, 100R1, 50R2 and 100R2 PETs.</figcaption></figure>

For the same blowing temperature, the behaviour of each grade does not appear equivalent. The volumes induced with the 100R1 grade are globally higher than the volumes of 100R2 that are higher than the virgin one. This comparison tends to comfort stretch ability of these materials for which the ability in developing SIC appeared easier in the virgin case. The fact that this material is able to crystallize easier will stop the deformation development, leading to lower blown volumes. The solicitations modes associated to ISBM process are mostly bi-axial tensile ones, but the tendency observed in uni-axial tensile mode was relevant enough to class the different materials in their ability to develop SIC.

An optimization of ISBM process could be to find a compromise between a later α-transition temperature and a sooner cold-crystallization temperatures, for recycled PET. Moreover, as recycled PET is less efficient in developing strain-induced crystallization compared to virgin PET, the process should be improved by choosing lower blowing temperature or higher strain rates.

## Conclusion

As a conclusion, efforts were made on understanding the mechanical behaviour of recycled materials and their ability in being stretched above the glass transition temperature. The forming range of each material was identified and appears to be slightly reduced in the case of recycled PET according to an earlier cold crystallization. In the large deformations domain, mechanical behaviour was analysed with the same couple of temperature and strain rate, associated to the same equivalent strain rate ($\dot{\varepsilon}_{eq}$). All PETs present very similar visco-hyperelastic responses, ruled by the initial physical state of the amorphous PET, ie their gap from α-transition temperature. The main discrepancies are obvious when the polyesters start strain-hardening, developing strain-induced crystallization (SIC). The natural draw ratios (NDR) are different from a material to another which means that the ability to developing mesophase or “organised” phase are not completely equivalent. As a result, both microstructures issuing from stretching or ISBM process
before blowing, thanks to an infra-red pyrometer calibrated for each grade. In fact, different process temperatures associated to different heating power of infra-red lamps were explored: the temperature varying between 85 °C (above the glass transition temperature) and 115 °C (before cold crystallization). The injection pressure of nitrogen gas was set at 6.5 bars, for a duration of 200 ms. A cooling with air was applied at the end of the blowing so as to simulate the contact of the material on an industrial cold mold. For the different blowing temperatures, the five grades were processed, and the volume induced were measured, after the cooling of the bubble. The Figure 3 presents the evolution of the volumes induced as a function of the process temperature.

<figure><img src="image_2574.png" /><figcaption>Fig. 3: Volume variation of blown preforms, as a function of blowing temperature, produced with ISBM process, for virgin, 50R1, 100R1, 50R2 and 100R2 PETs.</figcaption></figure>

For the same blowing temperature, the behaviour of each grade does not appear equivalent. The volumes induced with the 100R1 grade are globally higher than the volumes of 100R2 that are higher than the virgin one. This comparison tends to comfort stretch ability of these materials for which the ability in developing SIC appeared easier in the virgin case. The fact that this material is able to crystallize easier will stop the deformation development, leading to lower blown volumes. The solicitations modes associated to ISBM process are mostly bi-axial tensile ones, but the tendency observed in uni-axial tensile mode was relevant enough to class the different materials in their ability to develop SIC.

An optimization of ISBM process could be to find a compromise between a later α-transition temperature and a sooner cold-crystallization temperatures, for recycled PET. Moreover, as recycled PET is less efficient in developing strain-induced crystallization compared to virgin PET, the process should be improved by choosing lower blowing temperature or higher strain rates.

## Conclusion

As a conclusion, efforts were made on understanding the mechanical behaviour of recycled materials and their ability in being stretched above the glass transition temperature. The forming range of each material was identified and appears to be slightly reduced in the case of recycled PET according to an earlier cold crystallization. In the large deformations domain, mechanical behaviour was analysed with the same couple of temperature and strain rate, associated to the same equivalent strain rate ($\dot{\varepsilon}_{eq}$). All PETs present very similar visco-hyperelastic responses, ruled by the initial physical state of the amorphous PET, ie their gap from α-transition temperature. The main discrepancies are obvious when the polyesters start strain-hardening, developing strain-induced crystallization (SIC). The natural draw ratios (NDR) are different from a material to another which means that the ability to developing mesophase or “organised” phase are not completely equivalent. As a result, both microstructures issuing from stretching or ISBM process
should not be totally equivalent in terms crystalline organization definitions and amorphous phase mobility.

## References

[1] PlasticsEurope, Plastics – the Facts 2020, 2020.

[2] Directive (EU) 2019/904 of the European Parliament and of the Council of 5 June 2019 on "the reduction of the impact of certain plastic products on the environment", (Text with EEA relevance) PE/11/2019/REV/1, OJ L 155, 12.6.2019: 1–19, *Juin 2019*.

[3] MacArthur DE. The New Plastics Economy: Rethinking the future of plastics, 2016.

[4] Majumdar A, Shukla S, Singh AA, Arora S. Circular fashion: Properties of fabrics made from mechanically recycled poly-ethylene terephthalate (PET) bottles. Resour Conserv Recycl 2020;161:104915. https://doi.org/10.1016/j.resconrec.2020.104915

[5] Quentin J-P. PET ou polyéthylènetéréphtalate 2004:15. https://doi.org/10.51257/a-v1-j6488

[6] Wu H, Lv S, He Y, Qu J-P. The study of the thermomechanical degradation and mechanical properties of PET recycled by industrial-scale elongational processing. Polym Test 2019;77:105882. https://doi.org/10.1016/j.polymertesting.2019.04.029

[7] Aizenshtein EM. Bottle Wastes – to Textile Yarns. Fibre Chem 2016;47:343–7. https://doi.org/10.1007/s10692-016-9691-8

[8] Awaja F, Pavel D. Injection stretch blow moulding process of reactive extruded recycled PET and virgin PET blends 2005. https://doi.org/10.1016/j.eurpolymj.2005.05.036

[9] Badia JD, Strömberg E, Karlsson S, Ribes-Greus A. The role of crystalline, mobile amorphous and rigid amorphous fractions in the performance of recycled poly (ethylene terephthalate) (PET). Polym Degrad Stab 2012;97:98–107. https://doi.org/10.1016/j.polymdegradstab.2011.10.008
should not be totally equivalent in terms crystalline organization definitions and amorphous phase mobility.

References

[1] PlasticsEurope, Plastics - the Facts 2020, 2020.

[2] Directive (EU) 2019/904 of the European Parliament and of the Council of 5 June 2019 on "the reduction of the impact of certain plastic products on the environment", (Text with EEA relevance) PE/11/2019/REV/1, OJ L 155, 12.6.2019: 1–19, *Juin 2019*.

[3] MacArthur DE. The New Plastics Economy: Rethinking the future of plastics, 2016.

[4] Majumdar A, Shukla S, Singh AA, Arora S. Circular fashion: Properties of fabrics made from mechanically recycled poly-ethylene terephthalate (PET) bottles. Resour Conserv Recycl 2020;161:104915. https://doi.org/10.1016/j.resconrec.2020.104915

[5] Quentin J-P. PET ou polyéthylènetéréphtalate 2004:15. https://doi.org/10.51257/a-v1-j6488

[6] Wu H, Lv S, He Y, Qu J-P. The study of the thermomechanical degradation and mechanical properties of PET recycled by industrial-scale elongational processing. Polym Test 2019;77:105882. https://doi.org/10.1016/j.polymertesting.2019.04.029

[7] Aizenshtein EM. Bottle Wastes – to Textile Yarns. Fibre Chem 2016;47:343–7. https://doi.org/10.1007/s10692-016-9691-8

[8] Awaja F, Pavel D. Injection stretch blow moulding process of reactive extruded recycled PET and virgin PET blends 2005. https://doi.org/10.1016/j.eurpolymj.2005.05.036

[9] Badia JD, Strömberg E, Karlsson S, Ribes-Greus A. The role of crystalline, mobile amorphous and rigid amorphous fractions in the performance of recycled poly (ethylene terephthalate) (PET). Polym Degrad Stab 2012;97:98–107. https://doi.org/10.1016/j.polymdegradstab.2011.10.008
# Injection molding control parameter assessment by nested Taguchi design of simulation experiments: a case study

ILIOPOULOU Vasiliki¹,ª and VOSNIAKOS George-Christopher¹,ᵇ*

¹National Technical University of Athens, School of Mechanical Engineering, Manufacturing Technology Laboratory, Heroon Politehniou 9, 15773 Athens, Greece

ªiliopoulouvasiliki@gmail.com, ᵇvosniak@central.ntua.gr

**Keywords:** Polypropylene, Thin-Wall, Injection Molding, Taguchi Methods, Simulation

**Abstract.** Injection molding of a polypropylene (PP) food packaging container is studied using a dedicated simulation program. Three successive Taguchi designs of experiments (DoE) are performed to identify the influence of control factors and to adjust their levels for optimal result. These are nested, each one narrowing down the decision space of its predecessor. In the first design (L27), seven control factors were considered: polymer material in terms of MFI, melt and mold temperature, maximum injection pressure, filling time, maximum packing pressure and packing time. Part weight, clamping force and a fictitious factor related to premature solidification of the material during packing were quality factors. In the next DoE (L27 again), for fixed material, the four most important process factors determined before are examined in more detail, adding cycle time as an extra quality factor. Dependence of results on melt temperature proved to be strong, so a third DoE (L9), examined a narrower temperature range. Calculation of S/N ratios and analysis of variance (ANOVA) led to optimization of control parameters through a weighted objective function.

**Introduction**

Injection molding is the best-established manufacturing processes for producing thermoplastic parts [1]. The process starts with the plasticizing phase where the material is heated above glass transition temperature $T_g$ and is pushed towards the injection nozzles by a ram or transfer screw. The filling phase involves feeding of the material through sprue, runners and gates to fill the cavity. The filling time and injection pressure have to ensure complete filling of the mold and avoidance of premature solidification. As the material starts solidifying it shrinks, thus additional pressure needs to be applied at a switch-off point of the nozzle and the injection pressure signifying the beginning of the packing phase. Packing pressure should cease when its influence becomes negligible in order to reduce residual stresses in the part. The cooling phase ensures that no material flows towards the nozzles and that temperature falls below $T_g$ to obtain rigid parts without distortions as fast as possible. During filling, packing and cooling the mold is kept closed by a clamping force. This is released only to allow ejection of the part from the mold. [2]

Material behavior during molding, mold design, as well as process parameters have been studied extensively through simulation [3]. Simulation software of the injection molding cycle is probably the most advanced compared with other manufacturing process platforms [4]. Yet, due to inherent complexity, optimization of mold design and process planning is quite difficult. A particular requirement is prediction of a variety of defects associated with feeding system design and process control parameters, e.g. short shots, sink marks (especially at thick sections), weld lines (where flow fronts meet), warpage (associated with residual stresses), flow marks (associated with in-homogenous flow), jetting (associated with turbulence near gates), air traps (where vents are inadequate), burn marks and flash (e.g. due to inadequate clamping) [5].

In fact, there are numerous research articles studying various aspects of part, mold and process design which continue to emerge at a constant rate [1], examples of the most pertinent to this work

<figure><img src="image_2582.png" /></figure>
# Injection molding control parameter assessment by nested Taguchi design of simulation experiments: a case study

ILIOPOULOU Vasiliki¹,ª and VOSNIAKOS George-Christopher¹,ᵇ*

¹National Technical University of Athens, School of Mechanical Engineering, Manufacturing Technology Laboratory, Heroon Politehniou 9, 15773 Athens, Greece

ªiliopoulouvasiliki@gmail.com, ᵇvosniak@central.ntua.gr

**Keywords:** Polypropylene, Thin-Wall, Injection Molding, Taguchi Methods, Simulation

**Abstract.** Injection molding of a polypropylene (PP) food packaging container is studied using a dedicated simulation program. Three successive Taguchi designs of experiments (DoE) are performed to identify the influence of control factors and to adjust their levels for optimal result. These are nested, each one narrowing down the decision space of its predecessor. In the first design (L27), seven control factors were considered: polymer material in terms of MFI, melt and mold temperature, maximum injection pressure, filling time, maximum packing pressure and packing time. Part weight, clamping force and a fictitious factor related to premature solidification of the material during packing were quality factors. In the next DoE (L27 again), for fixed material, the four most important process factors determined before are examined in more detail, adding cycle time as an extra quality factor. Dependence of results on melt temperature proved to be strong, so a third DoE (L9), examined a narrower temperature range. Calculation of S/N ratios and analysis of variance (ANOVA) led to optimization of control parameters through a weighted objective function.

**Introduction**

Injection molding is the best-established manufacturing processes for producing thermoplastic parts [1]. The process starts with the plasticizing phase where the material is heated above glass transition temperature $T_g$ and is pushed towards the injection nozzles by a ram or transfer screw. The filling phase involves feeding of the material through sprue, runners and gates to fill the cavity. The filling time and injection pressure have to ensure complete filling of the mold and avoidance of premature solidification. As the material starts solidifying it shrinks, thus additional pressure needs to be applied at a switch-off point of the nozzle and the injection pressure signifying the beginning of the packing phase. Packing pressure should cease when its influence becomes negligible in order to reduce residual stresses in the part. The cooling phase ensures that no material flows towards the nozzles and that temperature falls below $T_g$ to obtain rigid parts without distortions as fast as possible. During filling, packing and cooling the mold is kept closed by a clamping force. This is released only to allow ejection of the part from the mold. [2]

Material behavior during molding, mold design, as well as process parameters have been studied extensively through simulation [3]. Simulation software of the injection molding cycle is probably the most advanced compared with other manufacturing process platforms [4]. Yet, due to inherent complexity, optimization of mold design and process planning is quite difficult. A particular requirement is prediction of a variety of defects associated with feeding system design and process control parameters, e.g. short shots, sink marks (especially at thick sections), weld lines (where flow fronts meet), warpage (associated with residual stresses), flow marks (associated with in-homogenous flow), jetting (associated with turbulence near gates), air traps (where vents are inadequate), burn marks and flash (e.g. due to inadequate clamping) [5].

In fact, there are numerous research articles studying various aspects of part, mold and process design which continue to emerge at a constant rate [1], examples of the most pertinent to this work

<figure><img src="image_2575.png" /></figure>
being briefly presented next. The importance of the Melt Flow Index (MFI) for the selection of a suitable polymer material is highlighted in [6], and its correlation with other processing parameters was studied. In [7] the filling, compression and cooling phases were studied by simulation software, in order to establish general guidelines for machine operators. Part weight was used as a quality factor to study a polypropylene product observing that melt temperature had the largest influence followed by filling pressure and speed [8]. Part weight was also used in [9] combined with flash forming to define optimum parameters in micro-injection molding. Three polypropylene materials with different viscosities were tested with respect to part weight to find the appropriate switch-off point and optimum clamping force [10].

Injection molding of a thin-walled part was studied using simulation and the Taguchi method, combined with real-time experiments, concluding that melt temperature and packing pressure had the greatest effect on part distortion [11]. Further Taguchi-based optimization work is reported on, with assessment of alternatives by numerical simulation, e.g. for PC/ABS part molding [12], or in combination with neural networks targeting avoidance of defects of weighted significance [13]. In Taguchi-based optimization, real experiments have also been used for assessment of alternative process parameters targeting improvement of mechanical properties of recycled polypropylene [14], minimization of weld-line width and sink-mark depth [15] etc. A comprehensive review of optimization approaches for injection molding is provided in [16].

This work aims at demonstrating a sequential Design of Experiments (DoE)-based simulation methodology for studying the influence of injection molding control parameters under ideal cooling conditions on part quality. The setup of simulation on Moldex3D platform is briefly presented next, followed by extensive presentation of three DoE experiments and their results as well as the optimization that was based on them, closing with some concluding remarks.

## Process simulation setup

The part that was studied is a food container with 450 ml capacity made of Polypropylene by a mold design and manufacturing company, see Figure 1(a). Section thickness is 0.5 mm. Possible defects include air traps, flow lines associated with inadequate packing time and distortion at the rim. The part is made in a mold with 4 cavities on a machine with 350 ton maximum clamping force. An initial estimate [2] corresponding to 147 mm flow length yields 110 MPa injection pressure, i.e., after multiplication with cavity projected area, a total clamping force of 320 tn.

Simulation of the process was set up on Moldex3D™, an advanced dedicated software platform for injection molding. Many of the simulation setup parameters as well as molding physics parameters have been left to defaults recommended by the software according to experience accumulated over the years, since the main aim of this work was not the detailed design of the mold or the feeding system but the comparative study of process control parameters. Thus, the part geometry was imported, see Figure 1(b), a hot ingate of pin type was set to the center of the container bottom, see Figure 1(c), and the cooling circuit was selected from standard shapes available in the software, its sizing being also recommended by the system, see Figure 1(d). As a final step, the dimensions of the mold block are defined, see Figure 1(e). These settings were accomplished with help by the company's experts.

The mesh employed was of medium accuracy level (3 in a scale of 5). A finer mesh (2 out of 5) resulted in doubling execution time, whilst results remained very close to those attained by the coarser mesh. Polypropylene (supplier Borealis) was selected from the software's database, starting at MFI45. Maximum allowable injection pressure and packing pressure were set at 200 MPa and 60 MPa following Demag™ Systec 350 machine capabilities.
being briefly presented next. The importance of the Melt Flow Index (MFI) for the selection of a suitable polymer material is highlighted in [6], and its correlation with other processing parameters was studied. In [7] the filling, compression and cooling phases were studied by simulation software, in order to establish general guidelines for machine operators. Part weight was used as a quality factor to study a polypropylene product observing that melt temperature had the largest influence followed by filling pressure and speed [8]. Part weight was also used in [9] combined with flash forming to define optimum parameters in micro-injection molding. Three polypropylene materials with different viscosities were tested with respect to part weight to find the appropriate switch-off point and optimum clamping force [10].

Injection molding of a thin-walled part was studied using simulation and the Taguchi method, combined with real-time experiments, concluding that melt temperature and packing pressure had the greatest effect on part distortion [11]. Further Taguchi-based optimization work is reported on, with assessment of alternatives by numerical simulation, e.g. for PC/ABS part molding [12], or in combination with neural networks targeting avoidance of defects of weighted significance [13]. In Taguchi-based optimization, real experiments have also been used for assessment of alternative process parameters targeting improvement of mechanical properties of recycled polypropylene [14], minimization of weld-line width and sink-mark depth [15] etc. A comprehensive review of optimization approaches for injection molding is provided in [16].

This work aims at demonstrating a sequential Design of Experiments (DoE)-based simulation methodology for studying the influence of injection molding control parameters under ideal cooling conditions on part quality. The setup of simulation on Moldex3D platform is briefly presented next, followed by extensive presentation of three DoE experiments and their results as well as the optimization that was based on them, closing with some concluding remarks.

## Process simulation setup

The part that was studied is a food container with 450 ml capacity made of Polypropylene by a mold design and manufacturing company, see Figure 1(a). Section thickness is 0.5 mm. Possible defects include air traps, flow lines associated with inadequate packing time and distortion at the rim. The part is made in a mold with 4 cavities on a machine with 350 ton maximum clamping force. An initial estimate [2] corresponding to 147 mm flow length yields 110 MPa injection pressure, i.e., after multiplication with cavity projected area, a total clamping force of 320 tn.

Simulation of the process was set up on Moldex3D™, an advanced dedicated software platform for injection molding. Many of the simulation setup parameters as well as molding physics parameters have been left to defaults recommended by the software according to experience accumulated over the years, since the main aim of this work was not the detailed design of the mold or the feeding system but the comparative study of process control parameters. Thus, the part geometry was imported, see Figure 1(b), a hot ingate of pin type was set to the center of the container bottom, see Figure 1(c), and the cooling circuit was selected from standard shapes available in the software, its sizing being also recommended by the system, see Figure 1(d). As a final step, the dimensions of the mold block are defined, see Figure 1(e). These settings were accomplished with help by the company's experts.

The mesh employed was of medium accuracy level (3 in a scale of 5). A finer mesh (2 out of 5) resulted in doubling execution time, whilst results remained very close to those attained by the coarser mesh. Polypropylene (supplier Borealis) was selected from the software's database, starting at MFI45. Maximum allowable injection pressure and packing pressure were set at 200 MPa and 60 MPa following Demag™ Systec 350 machine capabilities.
<figure><img src="image_2583.png" /><figcaption>Figure 1 Simulation setup (a) part design (b) part geometry in Moldex3D (c) gate definition (d) simplified cooling system definition (e) Mold with feeding system represented by the gate alone.</figcaption></figure>

Process parameters are set according to the scenario to investigate. Standard pressure profiles over time for the filling and packing phases are followed as defined in the software. Typically, packing pressure starts at 75% of the pressure at the end of the filling phase further dropping in three stages, i.e. from 100% to 62% at 45% of the packing time and further to 50% at 75% of the packing time. Furthermore, the switch-off point from filling to packing is chosen at filling ratio of 98%, as for larger ratios material velocity and pressure in the cavity may exceed machine capacity. Similarly, system defaults have been adopted for the cooling parameters. Cooling is considered ideal i.e. heat is dissipated to the full, thus, distortion calculation is only indicative. Cooling time was set to 1.6 sec, mold open time to 3.48 sec and ejection temperature to 90 °C according to the company's engineers.

The type of analysis was set to 'Filling and Packing', except for familiarization and for optimum control parameter verification where 'Injection Analysis' was set. The 'Enhanced P' solver was opted for as it is recommended for thin-walled parts with a high ratio of flow length to wall thickness. This takes into account viscous heating, non-isothermal and non-newtonian flow.

## Simulation experiments

Taguchi DoE emphasizes robustness to variations and seeks to improve quality while reducing costs by considering both mean and variability simultaneously. It involves a series of well-designed experiments to understand how different control factors influence process or product quality and to determine the optimal settings of those factors with respect to quality criteria. Responses regarding signal to noise ratio (S/N) and analysis of variance (ANOVA) are commonly used to this direction. The former is defined as $S/N=-10\*\log_{10}(\text{MSD})$ where MSD stands for mean squared deviation and depending on the quality definition is given alternatively as: $\text{MSD}=(y_1^2+y_2^2+y_3^2+...+y_n^2)/n$ (smaller is better - SB), $\text{MSD}=(\frac{y_1^4}{m}+\frac{y_2^4}{m^2}+...+\frac{y_n^4}{m^n})/n$ (nominal is best - NB), $\text{MSD}=(1/y_1^2+1/y_2^2+...+1/y_n^2)/n$ (larger is better - LB) where $y_1,y_2,...$ are results of experiments, m is the target value and n is the number of experiment repetitions [17].

*First DoE.* As we focus on the filling and compression phases, factors related to the cooling phase are excluded, ideal heat dissipation being assumed. The quality factors are selected based on customer specifications. Thus, 7 control factors are selected, each examined at three levels, see Table 1, according to experienced company engineers, yielding an L27 Orthogonal Array (OA). Similarly, quality factors in Table 1 are chosen by the engineers for the machine at hand. In addition, since at this stage the sensitivity of the system is examined and material flow cutoff at the packing phase is expected to be intense, the molten core as a fictitious quality factor is defined, which reflects lack of solidification in the part's centre at the end of the packing phase (=1/2, if it
<figure><img src="image_2576.png" /><figcaption>Figure 1 Simulation setup (a) part design (b) part geometry in Moldex3D (c) gate definition (d) simplified cooling system definition (e) Mold with feeding system represented by the gate alone.</figcaption></figure>

Process parameters are set according to the scenario to investigate. Standard pressure profiles over time for the filling and packing phases are followed as defined in the software. Typically, packing pressure starts at 75% of the pressure at the end of the filling phase further dropping in three stages, i.e. from 100% to 62% at 45% of the packing time and further to 50% at 75% of the packing time. Furthermore, the switch-off point from filling to packing is chosen at filling ratio of 98%, as for larger ratios material velocity and pressure in the cavity may exceed machine capacity. Similarly, system defaults have been adopted for the cooling parameters. Cooling is considered ideal i.e. heat is dissipated to the full, thus, distortion calculation is only indicative. Cooling time was set to 1.6 sec, mold open time to 3.48 sec and ejection temperature to 90 °C according to the company's engineers.

The type of analysis was set to 'Filling and Packing', except for familiarization and for optimum control parameter verification where 'Injection Analysis' was set. The 'Enhanced P' solver was opted for as it is recommended for thin-walled parts with a high ratio of flow length to wall thickness. This takes into account viscous heating, non-isothermal and non-newtonian flow.

## Simulation experiments

Taguchi DoE emphasizes robustness to variations and seeks to improve quality while reducing costs by considering both mean and variability simultaneously. It involves a series of well-designed experiments to understand how different control factors influence process or product quality and to determine the optimal settings of those factors with respect to quality criteria. Responses regarding signal to noise ratio (S/N) and analysis of variance (ANOVA) are commonly used to this direction. The former is defined as $S/N=-10\*\log_{10}(\text{MSD})$ where MSD stands for mean squared deviation and depending on the quality definition is given alternatively as: $\text{MSD}=(y_1^2+y_2^2+y_3^2+...+y_n^2)/n$ (smaller is better - SB), $\text{MSD}=(\frac{y_1^4}{m}+\frac{y_2^4}{m^2}+...+\frac{y_n^4}{m^n})/n$ (nominal is best - NB), $\text{MSD}=(1/y_1^2+1/y_2^2+...+1/y_n^2)/n$ (larger is better - LB) where $y_1,y_2,...$ are results of experiments, m is the target value and n is the number of experiment repetitions [17].

*First DoE.* As we focus on the filling and compression phases, factors related to the cooling phase are excluded, ideal heat dissipation being assumed. The quality factors are selected based on customer specifications. Thus, 7 control factors are selected, each examined at three levels, see Table 1, according to experienced company engineers, yielding an L27 Orthogonal Array (OA). Similarly, quality factors in Table 1 are chosen by the engineers for the machine at hand. In addition, since at this stage the sensitivity of the system is examined and material flow cutoff at the packing phase is expected to be intense, the molten core as a fictitious quality factor is defined, which reflects lack of solidification in the part's centre at the end of the packing phase (=1/2, if it
is present / absent). Note that according to the company expert engineers cooling time was set to 1.6 sec, mold open time was set to 3.48 sec and room temperature was set to 25 °C.

<table><caption>Table 1 Control factors (incl. abbreviated names) and Quality factors for the first DoE.</caption><thead><tr><th>Control factor</th><th>Abbr</th><th>Level1</th><th>Level2</th><th>Level3</th><th>Quality factor</th><th>Loss</th><th>Nominal</th></tr></thead><tbody><tr><td>Material [MFI g/10 min]</td><td>MFI</td><td>45</td><td>70</td><td>100</td><td>Clamp force [tn]</td><td>SB</td><td>87.5</td></tr><tr><td>Melt temperature [°C]</td><td>MET</td><td>220</td><td>240</td><td>260</td><td>Part weight [gr]</td><td>NB</td><td>15.5<br/>±3%</td></tr><tr><td>Mold temperature [°C]</td><td>MOT</td><td>10</td><td>20</td><td>30</td><td>Molten core</td><td>SB</td><td>0.2</td></tr><tr><td>Max injection press. [MPa]</td><td>MIP</td><td>160</td><td>180</td><td>220</td><td></td><td></td><td></td></tr><tr><td>Filling time [s]</td><td>FIT</td><td>0.17</td><td>0.22</td><td>0.28</td><td></td><td></td><td></td></tr><tr><td>Max packing press. [MPa]</td><td>MPP</td><td>40</td><td>60</td><td>80</td><td></td><td></td><td></td></tr><tr><td>Packing time [s]</td><td>PAT</td><td>0.45</td><td>0.50</td><td>0.80</td><td></td><td></td><td></td></tr></tbody></table>

The results obtained are summarized in Table 2 concerning mean response of S/N ratio and % ANOVA Contribution of the 7 control factors for each one of the 3 quality factors. Indicative results from run no. 2 are shown in Figure 2. In addition, Figure 3 displays S/N diagrams.

It is noteworthy that 19 of the 27 runs exhibit a molten core, which is desirable. The material seems to play the main role (33% contribution) followed by packing time (25%), see Table 2. The rest of the control factors play a small role. Furthermore, runs where flow cut-off is observed are associated with extreme filling and packing times. When filling takes place at slower rates or when the material does not have enough time to solidify during packing, uneven pressure distribution is reduced, see Figure 3, where even medium time values result in a higher S/N ratio.

<table><caption>Table 2 S/M Delta analysis and ANOVA % Contribution of control factors in the first DoE.</caption><thead><tr><th rowspan="2">Control factor</th><th colspan="2">Clamping force</th><th colspan="2">Part weight</th><th colspan="2">Molten core</th></tr><tr><th>S/N Δ</th><th>% Contr</th><th>S/N Δ</th><th>% Contr</th><th>S/N Δ</th><th>% Contr</th></tr></thead><tbody><tr><td>Material [MFI g/10 min]</td><td>1.42</td><td>53.34</td><td>12.75</td><td>95.20</td><td>9.44</td><td>32.90</td></tr><tr><td>Melt temperature [°C]</td><td>0.99</td><td>26.99</td><td>5.38</td><td>2.10</td><td>3.78</td><td>5.26</td></tr><tr><td>Mold temperature [°C]</td><td>0.12</td><td>0.70</td><td>1.07</td><td>0.02</td><td>1.89</td><td>1.32</td></tr><tr><td>Max injection pressure [MPa]</td><td>0.54</td><td>9.12</td><td>6.69</td><td>1.65</td><td>1.89</td><td>1.32</td></tr><tr><td>Filling time [s]</td><td>0.45</td><td>4.72</td><td>3.22</td><td>0.24</td><td>5.60</td><td>9.21</td></tr><tr><td>Max packing pressure [MPa]</td><td>0.21</td><td>1.77</td><td>2.92</td><td>0.05</td><td>9.44</td><td>9.21</td></tr><tr><td>Packing time [s]</td><td>0.27</td><td>2.19</td><td>2.80</td><td>0.19</td><td>5.66</td><td>25.00</td></tr></tbody></table>

<figure><img src="image_2584.png" /><figcaption>Figure 2 Indicative results for first DoE at the end of packing phase corresponding to MFI 45, MET=220°C, MOT=10°C, MIP=160 MPa, FIT=0.22 sec, MPP=60 MPa, PAT=0.5 sec (a) Temperature at the molten core (b) Pressure in cavity (c) Remaining molten sections.</figcaption></figure>
is present / absent). Note that according to the company expert engineers cooling time was set to 1.6 sec, mold open time was set to 3.48 sec and room temperature was set to 25 °C.

<table><caption>Table 1 Control factors (incl. abbreviated names) and Quality factors for the first DoE.</caption><thead><tr><th>Control factor</th><th>Abbr</th><th>Level1</th><th>Level2</th><th>Level3</th><th>Quality factor</th><th>Loss</th><th>Nominal</th></tr></thead><tbody><tr><td>Material [MFI g/10 min]</td><td>MFI</td><td>45</td><td>70</td><td>100</td><td>Clamp force [tn]</td><td>SB</td><td>87.5</td></tr><tr><td>Melt temperature [°C]</td><td>MET</td><td>220</td><td>240</td><td>260</td><td>Part weight [gr]</td><td>NB</td><td>15.5<br/>±3%</td></tr><tr><td>Mold temperature [°C]</td><td>MOT</td><td>10</td><td>20</td><td>30</td><td>Molten core</td><td>SB</td><td>0.2</td></tr><tr><td>Max injection press. [MPa]</td><td>MIP</td><td>160</td><td>180</td><td>220</td><td></td><td></td><td></td></tr><tr><td>Filling time [s]</td><td>FIT</td><td>0.17</td><td>0.22</td><td>0.28</td><td></td><td></td><td></td></tr><tr><td>Max packing press. [MPa]</td><td>MPP</td><td>40</td><td>60</td><td>80</td><td></td><td></td><td></td></tr><tr><td>Packing time [s]</td><td>PAT</td><td>0.45</td><td>0.50</td><td>0.80</td><td></td><td></td><td></td></tr></tbody></table>

The results obtained are summarized in Table 2 concerning mean response of S/N ratio and % ANOVA Contribution of the 7 control factors for each one of the 3 quality factors. Indicative results from run no. 2 are shown in Figure 2. In addition, Figure 3 displays S/N diagrams.

It is noteworthy that 19 of the 27 runs exhibit a molten core, which is desirable. The material seems to play the main role (33% contribution) followed by packing time (25%), see Table 2. The rest of the control factors play a small role. Furthermore, runs where flow cut-off is observed are associated with extreme filling and packing times. When filling takes place at slower rates or when the material does not have enough time to solidify during packing, uneven pressure distribution is reduced, see Figure 3, where even medium time values result in a higher S/N ratio.

<table><caption>Table 2 S/M Delta analysis and ANOVA % Contribution of control factors in the first DoE.</caption><thead><tr><th rowspan="2">Control factor</th><th colspan="2">Clamping force</th><th colspan="2">Part weight</th><th colspan="2">Molten core</th></tr><tr><th>S/N Δ</th><th>% Contr</th><th>S/N Δ</th><th>% Contr</th><th>S/N Δ</th><th>% Contr</th></tr></thead><tbody><tr><td>Material [MFI g/10 min]</td><td>1.42</td><td>53.34</td><td>12.75</td><td>95.20</td><td>9.44</td><td>32.90</td></tr><tr><td>Melt temperature [°C]</td><td>0.99</td><td>26.99</td><td>5.38</td><td>2.10</td><td>3.78</td><td>5.26</td></tr><tr><td>Mold temperature [°C]</td><td>0.12</td><td>0.70</td><td>1.07</td><td>0.02</td><td>1.89</td><td>1.32</td></tr><tr><td>Max injection pressure [MPa]</td><td>0.54</td><td>9.12</td><td>6.69</td><td>1.65</td><td>1.89</td><td>1.32</td></tr><tr><td>Filling time [s]</td><td>0.45</td><td>4.72</td><td>3.22</td><td>0.24</td><td>5.60</td><td>9.21</td></tr><tr><td>Max packing pressure [MPa]</td><td>0.21</td><td>1.77</td><td>2.92</td><td>0.05</td><td>9.44</td><td>9.21</td></tr><tr><td>Packing time [s]</td><td>0.27</td><td>2.19</td><td>2.80</td><td>0.19</td><td>5.66</td><td>25.00</td></tr></tbody></table>

<figure><img src="image_2577.png" /><figcaption>Figure 2 Indicative results for first DoE at the end of packing phase corresponding to MFI 45, MET=220°C, MOT=10°C, MIP=160 MPa, FIT=0.22 sec, MPP=60 MPa, PAT=0.5 sec (a) Temperature at the molten core (b) Pressure in cavity (c) Remaining molten sections.</figcaption></figure>
<figure><img src="image_2585.png" /><figcaption>Figure 3 S/N diagrams for first DoE (a) Clamping force (b) Part weight (c) Molten core.</figcaption></figure>

At a second level, it becomes clear that choice of material is crucial for all three quality factors examined. Especially with regard to part weight, it holds the exclusivity, see Table 2. A higher MFI facilitates ease of fill as is also confirmed by increase in part weight. Regarding clamping force, the results are somewhat misleading. Materials with a higher MFI, precisely because they achieve easier filling, also show a lower pressure drop at the nozzle and at the inlet gate. Therefore, higher pressures prevail within the cavity, so higher clamping forces are needed. Since all clamping force results are within machine safety margins, at this first stage of investigation no particular attention will be devoted to the respective quality factor. Regarding melt temperature, the value of 240°C tends to give the best results considering all quality factors together. The higher the temperature of the melt, the lower its viscosity becomes, thus better filling of the cavity is achieved. However, high melting temperatures also require longer cooling times, which adversely affects sustainability and productivity.

Second DoE. The aim is to further investigate the most important control factors as a follow up to the first DoE. Given that all three materials investigated there ensure the mechanical strength and thermal properties of the part and knowing that materials with a higher MFI are also more expensive, it is decided to fix the material to ISPLEN™ PB300 A3M with MFI: 80 gr/10min, Density: 905 kg/m³, Flexural modulus: 1250 MPa, Charpy impact strength: 5kJ/m², heat deflection temperature (0.45 MPa): 90 °C and Shore hardness: 62 (D Scale) (see www.b2bpolymers.com). In addition, less influential control factors according to the first DoE are also fixed, in addition to those that were fixed in the first place in the first DoE, as follows: Max injection pressure: 200 MPa, Mold temperature: 20 °C. As far as control factors are concerned, in order to reduce the emergence of material flow cut-off inside the cavity, the range of filling time and packing time is
<figure><img src="image_2578.png" /><figcaption>Figure 3 S/N diagrams for first DoE (a) Clamping force (b) Part weight (c) Molten core.</figcaption></figure>

At a second level, it becomes clear that choice of material is crucial for all three quality factors examined. Especially with regard to part weight, it holds the exclusivity, see Table 2. A higher MFI facilitates ease of fill as is also confirmed by increase in part weight. Regarding clamping force, the results are somewhat misleading. Materials with a higher MFI, precisely because they achieve easier filling, also show a lower pressure drop at the nozzle and at the inlet gate. Therefore, higher pressures prevail within the cavity, so higher clamping forces are needed. Since all clamping force results are within machine safety margins, at this first stage of investigation no particular attention will be devoted to the respective quality factor. Regarding melt temperature, the value of 240°C tends to give the best results considering all quality factors together. The higher the temperature of the melt, the lower its viscosity becomes, thus better filling of the cavity is achieved. However, high melting temperatures also require longer cooling times, which adversely affects sustainability and productivity.

Second DoE. The aim is to further investigate the most important control factors as a follow up to the first DoE. Given that all three materials investigated there ensure the mechanical strength and thermal properties of the part and knowing that materials with a higher MFI are also more expensive, it is decided to fix the material to ISPLEN™ PB300 A3M with MFI: 80 gr/10min, Density: 905 kg/m³, Flexural modulus: 1250 MPa, Charpy impact strength: 5kJ/m², heat deflection temperature (0.45 MPa): 90 °C and Shore hardness: 62 (D Scale) (see www.b2bpolymers.com). In addition, less influential control factors according to the first DoE are also fixed, in addition to those that were fixed in the first place in the first DoE, as follows: Max injection pressure: 200 MPa, Mold temperature: 20 °C. As far as control factors are concerned, in order to reduce the emergence of material flow cut-off inside the cavity, the range of filling time and packing time is
reconsidered, since medium values exhibited better results in the first DoE. Thus, control factors are summarized in Table 3. An L27 OA is again adopted. Regarding quality factors, see Table 3, in addition to those investigated in the first DoE, the cycle time is added, its variable component consisting of filling plus packing time, whereas the sum of its fixed components, namely cooling and mold open time amount to 5.08 sec. Similarly to the first DoE, the results obtained are summarized in Table 4.

<table><caption>Table 3 Control factors for the second DoE.</caption><thead><tr><th>Control factor</th><th>Level 1</th><th>Level 2</th><th>Level 3</th><th>Quality factor</th><th>Loss function</th><th>Nominal</th><th>Weight (%)</th></tr></thead><tbody><tr><td>Melt temperature [°C]</td><td>220</td><td>240</td><td>260</td><td>Clamping force [tn]</td><td>SB</td><td>87.5</td><td>35</td></tr><tr><td>Max pack press [MPa]</td><td>40</td><td>60</td><td>80</td><td>Part weight [gr]</td><td>NB</td><td>15.5 ±3%</td><td>35</td></tr><tr><td>Filling time [s]</td><td>0.19</td><td>0.22</td><td>0.25</td><td>Molten core</td><td>SB</td><td>0.2</td><td>10</td></tr><tr><td>Packing time [s]</td><td>0.45</td><td>0.50</td><td>0.65</td><td>Cycle time [s]</td><td>SB</td><td>4.5</td><td>20</td></tr></tbody></table>

<table><caption>Table 4 S/N Delta analysis and ANOVA % Contribution of control factors in the second DoE.</caption><thead><tr><th rowspan="2">Control factor</th><th colspan="2">Clamping force</th><th colspan="2">Part weight</th><th colspan="2">Molten core</th><th colspan="2">Cycle time</th></tr><tr><th>S/N Δ</th><th>% Contr</th><th>S/N Δ</th><th>% Contr</th><th>S/N Δ</th><th>% Contr</th><th>S/N Δ(X10⁻⁶)</th><th>% Contr</th></tr></thead><tbody><tr><td>Melt temperature [°C]</td><td>1.69</td><td>78.98</td><td>2.49</td><td>74.99</td><td>7.551</td><td>36.00</td><td>13895</td><td>0.20</td></tr><tr><td>Max pack press. [MPa]</td><td>0.343</td><td>3.4</td><td>1.60</td><td>9.86</td><td>3.775</td><td>10.00</td><td>14548</td><td>0.95</td></tr><tr><td>Filling time [s]</td><td>0.632</td><td>11.92</td><td>0.12</td><td>2.97</td><td>3.775</td><td>10.00</td><td>33740</td><td>6.00</td></tr><tr><td>Packing time [s]</td><td>0.377</td><td>4.99</td><td>0.18</td><td>5.94</td><td>7.551</td><td>36.00</td><td>41509</td><td>89.82</td></tr></tbody></table>

Indicative results from run no. 3, which is one of the two runs leading to uneven pressure distribution, are shown in Figure 4. In addition, Figure 5 displays all pertinent S/N diagrams.

The second DoE can be deemed successful, since only 2 out of 27 experiment runs showed a cut-off of material flow inside the cavity, in contrast to 18 out of 27 in the first DoE. The two runs involve lower material temperature and longer packing time, thus an increase in the former and/or a decrease in the latter should be alleviated this problem, as is also confirmed in Table 4 regarding quality factor 'part weight'. In the simulation run example shown in Figure 4(a)-(b), at the end of the packing phase, the pressure and temperature distribution in the part is generally acceptable. At the same time, the area around the ingate remains molten and is cut off from the rest of the already solidified block. This has already started 0.10 sec earlier, see Figure 4(c).

<figure><img src="image_2586.png" /><figcaption>Figure 4 Indicative results for second DoE with MET=220°C, MPP=40 MPa, FIT=0.25 sec, PAT=0.65 sec (a) Pressure at the end of packing phase (0.65 sec) (b) Temperature at the core at the end of packing phase (0.65 sec) (c) Molten core temperature at 0.55 sec into packing phase</figcaption></figure>
reconsidered, since medium values exhibited better results in the first DoE. Thus, control factors are summarized in Table 3. An L27 OA is again adopted. Regarding quality factors, see Table 3, in addition to those investigated in the first DoE, the cycle time is added, its variable component consisting of filling plus packing time, whereas the sum of its fixed components, namely cooling and mold open time amount to 5.08 sec. Similarly to the first DoE, the results obtained are summarized in Table 4.

<table><caption>Table 3 Control factors for the second DoE.</caption><thead><tr><th>Control factor</th><th>Level 1</th><th>Level 2</th><th>Level 3</th><th>Quality factor</th><th>Loss function</th><th>Nominal</th><th>Weight (%)</th></tr></thead><tbody><tr><td>Melt temperature [°C]</td><td>220</td><td>240</td><td>260</td><td>Clamping force [tn]</td><td>SB</td><td>87.5</td><td>35</td></tr><tr><td>Max pack press [MPa]</td><td>40</td><td>60</td><td>80</td><td>Part weight [gr]</td><td>NB</td><td>15.5 ±3%</td><td>35</td></tr><tr><td>Filling time [s]</td><td>0.19</td><td>0.22</td><td>0.25</td><td>Molten core</td><td>SB</td><td>0.2</td><td>10</td></tr><tr><td>Packing time [s]</td><td>0.45</td><td>0.50</td><td>0.65</td><td>Cycle time [s]</td><td>SB</td><td>4.5</td><td>20</td></tr></tbody></table>

<table><caption>Table 4 S/N Delta analysis and ANOVA % Contribution of control factors in the second DoE.</caption><thead><tr><th rowspan="2">Control factor</th><th colspan="2">Clamping force</th><th colspan="2">Part weight</th><th colspan="2">Molten core</th><th colspan="2">Cycle time</th></tr><tr><th>S/N Δ</th><th>% Contr</th><th>S/N Δ</th><th>% Contr</th><th>S/N Δ</th><th>% Contr</th><th>S/N Δ(X10⁻⁶)</th><th>% Contr</th></tr></thead><tbody><tr><td>Melt temperature [°C]</td><td>1.69</td><td>78.98</td><td>2.49</td><td>74.99</td><td>7.551</td><td>36.00</td><td>13895</td><td>0.20</td></tr><tr><td>Max pack press. [MPa]</td><td>0.343</td><td>3.4</td><td>1.60</td><td>9.86</td><td>3.775</td><td>10.00</td><td>14548</td><td>0.95</td></tr><tr><td>Filling time [s]</td><td>0.632</td><td>11.92</td><td>0.12</td><td>2.97</td><td>3.775</td><td>10.00</td><td>33740</td><td>6.00</td></tr><tr><td>Packing time [s]</td><td>0.377</td><td>4.99</td><td>0.18</td><td>5.94</td><td>7.551</td><td>36.00</td><td>41509</td><td>89.82</td></tr></tbody></table>

Indicative results from run no. 3, which is one of the two runs leading to uneven pressure distribution, are shown in Figure 4. In addition, Figure 5 displays all pertinent S/N diagrams.

The second DoE can be deemed successful, since only 2 out of 27 experiment runs showed a cut-off of material flow inside the cavity, in contrast to 18 out of 27 in the first DoE. The two runs involve lower material temperature and longer packing time, thus an increase in the former and/or a decrease in the latter should be alleviated this problem, as is also confirmed in Table 4 regarding quality factor 'part weight'. In the simulation run example shown in Figure 4(a)-(b), at the end of the packing phase, the pressure and temperature distribution in the part is generally acceptable. At the same time, the area around the ingate remains molten and is cut off from the rest of the already solidified block. This has already started 0.10 sec earlier, see Figure 4(c).

<figure><img src="image_2579.png" /><figcaption>Figure 4 Indicative results for second DoE with MET=220°C, MPP=40 MPa, FIT=0.25 sec, PAT=0.65 sec (a) Pressure at the end of packing phase (0.65 sec) (b) Temperature at the core at the end of packing phase (0.65 sec) (c) Molten core temperature at 0.55 sec into packing phase</figcaption></figure>
<figure><img src="image_2587.png" /><figcaption>Figure 5 S/N diagrams for second DoE (a) Clamping pressure (b) Part weight (c) Molten core (d) Cycle time</figcaption></figure>

In general, results are strongly dependent on melt temperature, the latter being the most influential factor for three out of four quality factors examined. As a matter of fact, melt temperature range considered is quite wide, so its further investigation is necessary. Actually, the highest S/N ratios correspond to a temperature of 240°C.

With regard to the quality factor 'cycle time', the most significant control variables are: filling time and packing time, the latter having by far the greatest effect, see Table 4. This seems reasonable, in view of the very short cycle time, meaning that the phase with the longest duration (and also coarser variation step of the order of 0.1 sec) should also have the overwhelming influence. Finally, regarding part weight, the second most important factor is maximum packing pressure, whereas regarding clamping force, it is the filling time.

Third DoE. According to the second DoE melt temperature proved the most significant control factor, thus a further DoE yet restricted in scope was deemed important focusing on a narrower melt temperature range examination around the 240°C value recommended by the manufacturer, i.e. 235, 240 and 245 °C. The rest control factors and their levels remained as in Table 3. Of the quality factors examined in the second DoE, see Table 3, the molten core was exempted because material feed cut-oof is not expected given the much narrower melt temperature range. Weights for the three factors of clamping force, part weight and cycle time have been set to 40, 40 and 20% respectively. The OA employed is L9. Note that degrees of freedom are zero, thus error factor is not relevant. Similarly to the previous DoEs, the results obtained are summarized in Table 5. In addition, Figure 6 displays the pertinent S/N diagrams.

<table><caption>Table 5 S/N Delta analysis and ANOVA % Contribution of control factors in the third DoE.</caption><thead><tr><th rowspan="2">Control factor</th><th colspan="2">Clamping force</th><th colspan="2">Part weight</th><th colspan="2">Cycle time</th></tr><tr><th>S/N Δ</th><th>% Contr</th><th>S/N Δ</th><th>% Contr</th><th>S/N Δ(X10<sup>-6</sup>)</th><th>% Contr</th></tr></thead><tbody><tr><td>Melt temperature [°C]</td><td>0.398</td><td>27.64</td><td>3.630</td><td>32.85</td><td>0.263</td><td>0.9</td></tr><tr><td>Max packing pressure [MPa]</td><td>0.570</td><td>49.92</td><td>1.838</td><td>11.76</td><td>0.672</td><td>8.2</td></tr><tr><td>Filling time [s]</td><td>0.283</td><td>13.68</td><td>3.127</td><td>28.06</td><td>0.400</td><td>1.9</td></tr><tr><td>Packing time [s]</td><td>0.087</td><td>9.00</td><td>3.117</td><td>27.33</td><td>2.168</td><td>90.0</td></tr></tbody></table>
<figure><img src="image_2580.png" /><figcaption>Figure 5 S/N diagrams for second DoE (a) Clamping pressure (b) Part weight (c) Molten core (d) Cycle time</figcaption></figure>

In general, results are strongly dependent on melt temperature, the latter being the most influential factor for three out of four quality factors examined. As a matter of fact, melt temperature range considered is quite wide, so its further investigation is necessary. Actually, the highest S/N ratios correspond to a temperature of 240°C.

With regard to the quality factor 'cycle time', the most significant control variables are: filling time and packing time, the latter having by far the greatest effect, see Table 4. This seems reasonable, in view of the very short cycle time, meaning that the phase with the longest duration (and also coarser variation step of the order of 0.1 sec) should also have the overwhelming influence. Finally, regarding part weight, the second most important factor is maximum packing pressure, whereas regarding clamping force, it is the filling time.

Third DoE. According to the second DoE melt temperature proved the most significant control factor, thus a further DoE yet restricted in scope was deemed important focusing on a narrower melt temperature range examination around the 240°C value recommended by the manufacturer, i.e. 235, 240 and 245 °C. The rest control factors and their levels remained as in Table 3. Of the quality factors examined in the second DoE, see Table 3, the molten core was exempted because material feed cut-oof is not expected given the much narrower melt temperature range. Weights for the three factors of clamping force, part weight and cycle time have been set to 40, 40 and 20% respectively. The OA employed is L9. Note that degrees of freedom are zero, thus error factor is not relevant. Similarly to the previous DoEs, the results obtained are summarized in Table 5. In addition, Figure 6 displays the pertinent S/N diagrams.

<table><caption>Table 5 S/N Delta analysis and ANOVA % Contribution of control factors in the third DoE.</caption><thead><tr><th rowspan="2">Control factor</th><th colspan="2">Clamping force</th><th colspan="2">Part weight</th><th colspan="2">Cycle time</th></tr><tr><th>S/N Δ</th><th>% Contr</th><th>S/N Δ</th><th>% Contr</th><th>S/N Δ(X10<sup>-6</sup>)</th><th>% Contr</th></tr></thead><tbody><tr><td>Melt temperature [°C]</td><td>0.398</td><td>27.64</td><td>3.630</td><td>32.85</td><td>0.263</td><td>0.9</td></tr><tr><td>Max packing pressure [MPa]</td><td>0.570</td><td>49.92</td><td>1.838</td><td>11.76</td><td>0.672</td><td>8.2</td></tr><tr><td>Filling time [s]</td><td>0.283</td><td>13.68</td><td>3.127</td><td>28.06</td><td>0.400</td><td>1.9</td></tr><tr><td>Packing time [s]</td><td>0.087</td><td>9.00</td><td>3.117</td><td>27.33</td><td>2.168</td><td>90.0</td></tr></tbody></table>
<figure><img src="image_2588.png" /><figcaption>Figure 6 S/N diagrams for third DoE (a) Clamping pressure (b) Part weight (c) Cycle time.</figcaption></figure>

It is to be noted that no cut-off of material flow was observed in any simulation run, hence temperature increase is beneficial. In addition, dependence of the quality factors on the temperature of the polymer material is significantly reduced. Melt temperature remains as the first of the two factors with the greatest influence on part weight and clamping force, however it now has a much smaller share. More specifically, the picture concerning cycle time remained approximately the same as in the second DoE. As for part weight, all control parameters contribute to the result. As regards clamping force, the factor with the most significant effect is now filling time, with a percentage contribution of around 50%. It should also be noted that levels 1 and 2 of packing time perform very close to each other. In general, the optimal value for melt temperature is 240 °C, which is also recommended by the material supplier. Regarding filling and packing times, it can be stated that the middle values considered, i.e. 0.22 s and 0.5 s respectively, are preferable. As for the maximum injection pressure its preferable level differs for each quality factor considered. In any case, the optimal values for the control factors will be determined in the next section.

*Optimisation.* In order to determine the optimum values of the control factors a weighted objective function was created, whose minimization was sought, involving the normalized quality factors, where denominators represent the maximum values encountered and multiplicators denote the % weight of each factor:

$$40 \frac{\text{Clamping Force}(Tn)}{75.1} + 40 \frac{\text{Abs. Deviation from Target Weight}(gr)}{0.339} + 20 \frac{\text{Cycle Time}(s)}{0.923} \quad (1)$$

The resulting optimum values were as follows: Material MFI=80, Melt temperature: 240 °C, Mold temperature: 20 °C, Max injection pressure: 200 MPa, Filling time: 0.22 sec, Max packing pressure: 60 MPa, Packing time: 0.50 sec.

A simulation was run for these inputs and characteristic results are depicted in Figure 7. The corresponding quality factor values were as follows: Max clamping force: 70.1 tn, Part weight: 15.365 gr, Cycle time (filling and packing only): 0.73 sec. At the end of packing phase more than 80% of the material has solidified, yet the core remains fully molten, see Figure 7(e). Note that some air bubbles seem to be trapped where flow direction changes, see Figure 7(h). Also note that some volumetric shrinkages has been predicted (1.69, 1.71, 1.58 % in the X,Y,Z direction respectively) but deflection at the rim is negligible, see Figure 7(i).
<figure><img src="image_2581.png" /><figcaption>Figure 6 S/N diagrams for third DoE (a) Clamping pressure (b) Part weight (c) Cycle time.</figcaption></figure>

It is to be noted that no cut-off of material flow was observed in any simulation run, hence temperature increase is beneficial. In addition, dependence of the quality factors on the temperature of the polymer material is significantly reduced. Melt temperature remains as the first of the two factors with the greatest influence on part weight and clamping force, however it now has a much smaller share. More specifically, the picture concerning cycle time remained approximately the same as in the second DoE. As for part weight, all control parameters contribute to the result. As regards clamping force, the factor with the most significant effect is now filling time, with a percentage contribution of around 50%. It should also be noted that levels 1 and 2 of packing time perform very close to each other. In general, the optimal value for melt temperature is 240 °C, which is also recommended by the material supplier. Regarding filling and packing times, it can be stated that the middle values considered, i.e. 0.22 s and 0.5 s respectively, are preferable. As for the maximum injection pressure its preferable level differs for each quality factor considered. In any case, the optimal values for the control factors will be determined in the next section.

*Optimisation.* In order to determine the optimum values of the control factors a weighted objective function was created, whose minimization was sought, involving the normalized quality factors, where denominators represent the maximum values encountered and multiplicators denote the % weight of each factor:

$$40 \frac{\text{Clamping Force}(Tn)}{75.1} + 40 \frac{\text{Abs. Deviation from Target Weight}(gr)}{0.339} + 20 \frac{\text{Cycle Time}(s)}{0.923} \quad (1)$$

The resulting optimum values were as follows: Material MFI=80, Melt temperature: 240 °C, Mold temperature: 20 °C, Max injection pressure: 200 MPa, Filling time: 0.22 sec, Max packing pressure: 60 MPa, Packing time: 0.50 sec.

A simulation was run for these inputs and characteristic results are depicted in Figure 7. The corresponding quality factor values were as follows: Max clamping force: 70.1 tn, Part weight: 15.365 gr, Cycle time (filling and packing only): 0.73 sec. At the end of packing phase more than 80% of the material has solidified, yet the core remains fully molten, see Figure 7(e). Note that some air bubbles seem to be trapped where flow direction changes, see Figure 7(h). Also note that some volumetric shrinkages has been predicted (1.69, 1.71, 1.58 % in the X,Y,Z direction respectively) but deflection at the rim is negligible, see Figure 7(i).
<figure><img src="image_2589.png" /><figcaption>Figure 7: Indicative simulation results for the optimum case (a) Filling melt front time (b) isoparametric pressure curves in filling phase (c) filling flow rate vs time curve (d) clamping force vs time curve (e) molten core at the end of packing (f) pressure distribution after packing (g) pressure vs time in sprue during packing (h) air bubbles accumulation (i) final part distortion</figcaption></figure>

## Concluding remarks

In the present work, injection molding parameters for a polypropylene food packaging container were studied using a dedicated, well-established in industry simulation program. The latter enabled understanding of the process via visualization of the filling pattern of the cavity as well as prediction of any defects.

The Taguchi DoE method proved to be time-efficient for examining different process parameter combination scenarios, given the manageable number of experiments it requires. In this work, it was proven that cavity filling is facilitated by using materials with a higher MFI, as opposed to increasing melt temperature which is economically unacceptable. The influence of filling time and packing time was also confirmed, while the temperature of the mold did not play a special role in the two phases examined. The appropriate order of significance of process control parameters was calculated leading to optimize the overall result in terms of part weight, clamping force and cycle time.

In the future, investigation should be completed by embedding cooling stage in the simulation, since cooling time accounts for the largest part of the injection cycle. In addition, with appropriate channel design, part shrinkage and distortion should be realistically predicted.

Finally, it would be useful to validate simulation results. The software employed enables simulations in ‘Machine Mode’, which requires data from the controller of the injection molding machine. This will ultimately lead to a digital twin.

## Acknowledgments

Part of this work is funded by the European Commission, project 101079398 ‘New Approach to Innovative Technologies in Manufacturing (NEPTUN)’. Company Bazigos S.A. is gratefully acknowledged, for providing part and mold data, and expert injection molding advice.

## References

[1] G. Singh, A. Verma, A Brief Review on injection moulding manufacturing process, Materials Today: Proceedings, 4/2 (2017) 1423-1433. https://doi.org/10.1016/j.matpr.2017.01.164.

[2] B. Catoen, H. Rees, Injection Mold Design Handbook, Hanser Publ., Munich, 2022.
<figure><img src="image_2582.png" /><figcaption>Figure 7: Indicative simulation results for the optimum case (a) Filling melt front time (b) isoparametric pressure curves in filling phase (c) filling flow rate vs time curve (d) clamping force vs time curve (e) molten core at the end of packing (f) pressure distribution after packing (g) pressure vs time in sprue during packing (h) air bubbles accumulation (i) final part distortion</figcaption></figure>

## Concluding remarks

In the present work, injection molding parameters for a polypropylene food packaging container were studied using a dedicated, well-established in industry simulation program. The latter enabled understanding of the process via visualization of the filling pattern of the cavity as well as prediction of any defects.

The Taguchi DoE method proved to be time-efficient for examining different process parameter combination scenarios, given the manageable number of experiments it requires. In this work, it was proven that cavity filling is facilitated by using materials with a higher MFI, as opposed to increasing melt temperature which is economically unacceptable. The influence of filling time and packing time was also confirmed, while the temperature of the mold did not play a special role in the two phases examined. The appropriate order of significance of process control parameters was calculated leading to optimize the overall result in terms of part weight, clamping force and cycle time.

In the future, investigation should be completed by embedding cooling stage in the simulation, since cooling time accounts for the largest part of the injection cycle. In addition, with appropriate channel design, part shrinkage and distortion should be realistically predicted.

Finally, it would be useful to validate simulation results. The software employed enables simulations in ‘Machine Mode’, which requires data from the controller of the injection molding machine. This will ultimately lead to a digital twin.

## Acknowledgments

Part of this work is funded by the European Commission, project 101079398 ‘New Approach to Innovative Technologies in Manufacturing (NEPTUN)’. Company Bazigos S.A. is gratefully acknowledged, for providing part and mold data, and expert injection molding advice.

## References

[1] G. Singh, A. Verma, A Brief Review on injection moulding manufacturing process, Materials Today: Proceedings, 4/2 (2017) 1423-1433. https://doi.org/10.1016/j.matpr.2017.01.164.

[2] B. Catoen, H. Rees, Injection Mold Design Handbook, Hanser Publ., Munich, 2022.
[3] C. Fernandes, A.J. Pontes, J.C. Viana, A. Gaspar-Cunha, Modeling and Optimization of the Injection-Molding Process: A Review. Advances in Polymer Technology, 37/2 (2018) 429-449. https://doi.org/10.1002/adv.21683.

[4] M.-L. Wang, R.-Y. Chang, and C.-H. Hsu, (Eds), Molding Simulation: Theory and Practice, Hanser Publ., Munich, 2022.

[5] A. Mourya, A. Nanda, K. Parashar, Sushant, R. Kumar, An explanatory study on defects in plastic molding parts caused by machine parameters in injection molding process, Materials Today Proceedings 78 (2023) 656-661. https://doi.org/10.1016/j.matpr.2022.12.070.

[6] A. Shenoy, D. R. Saini, Melt Flow Index: More Than Just a Quality Control Rheological Parameter. Part I, Advanced Polymer Technology 6 (1986) 1-58. https://doi.org/10.1002/adv.1986.060060101.

[7] I. Sapounas, G.-C. Vosniakos, G. Papazetis, A simulation-based robust methodology for operator guidance on injection moulding machine settings, International Journal of Interactive Design & Manufacturing 14 (2020) 519-533. https://doi.org/10.1007/s12008-020-00646-z.

[8] M. Kavade, Parameter Optimization of Injection Molding of Polypropylene by using Taguchi Methodology, IOSR Journal of Mechanical and Civil Engineering 4 (2012) 49-58. https://doi.org/10.9790/1684-0444958.

[9] G. Trotta, S. Cacace, Q. Semeraro, Optimizing process parameters in micro injection moulding considering the part weight and probability of flash formation, Journal of Manufacturing Processes 79 (2022) 250-258. https://doi.org/10.1016/j.jmapro.2022.04.048.

[10] C.W. Su, W.J. Su, F.J. Cheng, G.Y. Liou, S.J. Hwang, H.S. Peng, H.Y. Chu, Optimization process parameters and adaptive quality monitoring injection molding process for materials with different viscosity, Polymer Testing, 109 (2022) 107526. https://doi.org/10.1016/j.polymertesting.2022.107526.

[11] C.-P. Chen, M.-T. Chuang, Y.-H. Hsiao, Y.-K. Yang, C.-H. Tsai, Simulation and experimental study in determining injection molding process parameters for thin-shell plastic parts via design of experiments analysis, Expert Systems and Applications 36/7 (2009) 10752-10759. https://doi.org/10.1016/j.eswa.2009.02.017.

[12] F. Hentati, I. Hadriche, N. Masmoudi, C. Bradai, Optimization of the injection molding process for the PC/ABS parts by integrating Taguchi approach and CAE simulation, The International Journal of Advanced Manufacturing Technology 104 (2019) 4353-4363. https://doi.org/10.1007/s00170-019-04283-z.

[13] M. Moayyedian, A. Dinc, A. Mamedov, Optimization of Injection-Molding Process for Thin-Walled Polypropylene Part Using Artificial Neural Network and Taguchi Techniques, Polymers, 13(23) 2021, 4158. https://doi.org/10.3390/polym13234158.

[14] V. Panneerselvam, F.M. Turan, Optimisation of Injection Moulding Process Parameter Using Taguchi and Desirability Function, In: M. Zakaria, A. Abdul Majeed, M. Hassan (eds) Advances in Mechatronics, Manufacturing, and Mechanical Engineering. Lecture Notes in Mechanical Engineering, (2021) 247-260, Springer. doi: 10.1007/978-981-15-7309-5_24

[15] B. Ravikiran, D.K. Pradhan, S. Jeet, D.K. Bagal, A. Barua, S. Nayak, 2022. Parametric optimization of plastic injection moulding for FMCG polymer moulding (PMMA) using hybrid Taguchi-WASPAS-Ant Lion optimization algorithm, Materials Today: Proceedings, 56 (2022) 2411-2420. https://doi.org/10.1016/j.matpr.2021.08.204.

[16] R. Farooque, M. Asjad, S.J.A. Rizvi, A current state of art applied to injection moulding manufacturing process-a review, Materials Today: Proceedings, 43 (2021) 441-446. https://doi.org/10.1016/j.matpr.2020.11.967.

[17] R.K. Roy, A primer on the Taguchi method. Society of Manufacturing Engineers, 2010.
[3] C. Fernandes, A.J. Pontes, J.C. Viana, A. Gaspar-Cunha, Modeling and Optimization of the Injection-Molding Process: A Review. Advances in Polymer Technology, 37/2 (2018) 429-449. https://doi.org/10.1002/adv.21683.

[4] M.-L. Wang, R.-Y. Chang, and C.-H. Hsu, (Eds), Molding Simulation: Theory and Practice, Hanser Publ., Munich, 2022.

[5] A. Mourya, A. Nanda, K. Parashar, Sushant, R. Kumar, An explanatory study on defects in plastic molding parts caused by machine parameters in injection molding process, Materials Today Proceedings 78 (2023) 656-661. https://doi.org/10.1016/j.matpr.2022.12.070.

[6] A. Shenoy, D. R. Saini, Melt Flow Index: More Than Just a Quality Control Rheological Parameter. Part I, Advanced Polymer Technology 6 (1986) 1-58. https://doi.org/10.1002/adv.1986.060060101.

[7] I. Sapounas, G.-C. Vosniakos, G. Papazetis, A simulation-based robust methodology for operator guidance on injection moulding machine settings, International Journal of Interactive Design & Manufacturing 14 (2020) 519-533. https://doi.org/10.1007/s12008-020-00646-z.

[8] M. Kavade, Parameter Optimization of Injection Molding of Polypropylene by using Taguchi Methodology, IOSR Journal of Mechanical and Civil Engineering 4 (2012) 49-58. https://doi.org/10.9790/1684-0444958.

[9] G. Trotta, S. Cacace, Q. Semeraro, Optimizing process parameters in micro injection moulding considering the part weight and probability of flash formation, Journal of Manufacturing Processes 79 (2022) 250-258. https://doi.org/10.1016/j.jmapro.2022.04.048.

[10] C.W. Su, W.J. Su, F.J. Cheng, G.Y. Liou, S.J. Hwang, H.S. Peng, H.Y. Chu, Optimization process parameters and adaptive quality monitoring injection molding process for materials with different viscosity, Polymer Testing, 109 (2022) 107526. https://doi.org/10.1016/j.polymertesting.2022.107526.

[11] C.-P. Chen, M.-T. Chuang, Y.-H. Hsiao, Y.-K. Yang, C.-H. Tsai, Simulation and experimental study in determining injection molding process parameters for thin-shell plastic parts via design of experiments analysis, Expert Systems and Applications 36/7 (2009) 10752-10759. https://doi.org/10.1016/j.eswa.2009.02.017.

[12] F. Hentati, I. Hadriche, N. Masmoudi, C. Bradai, Optimization of the injection molding process for the PC/ABS parts by integrating Taguchi approach and CAE simulation, The International Journal of Advanced Manufacturing Technology 104 (2019) 4353-4363. https://doi.org/10.1007/s00170-019-04283-z.

[13] M. Moayyedian, A. Dinc, A. Mamedov, Optimization of Injection-Molding Process for Thin-Walled Polypropylene Part Using Artificial Neural Network and Taguchi Techniques, Polymers, 13(23) 2021, 4158. https://doi.org/10.3390/polym13234158.

[14] V. Panneerselvam, F.M. Turan, Optimisation of Injection Moulding Process Parameter Using Taguchi and Desirability Function, In: M. Zakaria, A. Abdul Majeed, M. Hassan (eds) Advances in Mechatronics, Manufacturing, and Mechanical Engineering. Lecture Notes in Mechanical Engineering, (2021) 247-260, Springer. doi: 10.1007/978-981-15-7309-5_24

[15] B. Ravikiran, D.K. Pradhan, S. Jeet, D.K. Bagal, A. Barua, S. Nayak, 2022. Parametric optimization of plastic injection moulding for FMCG polymer moulding (PMMA) using hybrid Taguchi-WASPAS-Ant Lion optimization algorithm, Materials Today: Proceedings, 56 (2022) 2411-2420. https://doi.org/10.1016/j.matpr.2021.08.204.

[16] R. Farooque, M. Asjad, S.J.A. Rizvi, A current state of art applied to injection moulding manufacturing process-a review, Materials Today: Proceedings, 43 (2021) 441-446. https://doi.org/10.1016/j.matpr.2020.11.967.

[17] R.K. Roy, A primer on the Taguchi method. Society of Manufacturing Engineers, 2010.
Sustainability on material forming

<figure><img src="image_2590.png" /></figure>
Sustainability on material forming

<figure><img src="image_2583.png" /></figure>
# A combined finite element – Life cycle assessment approach for assessing the sustainability of the thermoforming process of a thermoplastic composite component

STAMOPOULOS Antonios G.<fnref n="1" />,ᵃ*, LA ROSA Angela Daniela<fnref n="2" />,ᵇ and CREONTI Gianluigi<fnref n="3" />,ᶜ

**Keywords:** Thermoforming, Thermoplastic Composites, Finite Element Simulation, Life Cycle Assessment

**Abstract.** Nowadays, thermoplastic-based composite materials are catching up in many industrial sectors due to their unique features, especially their recyclability, their lower requirements regarding their storage and the fact that may be heated-up and formed into a definitive product. To this end, the thermoforming process appears to be an appealing fabrication process. However, throughout this process, a number of defects may be introduced that can affect the morphology and structural performance of the product. Until recently, the definition of the optimal process characteristics was based on trial-and-error experimental tests that increased the resources required for its development. In addition, a significant part of the thermoplastic composite plate has to be removed/wasted after the thermoforming process. In this work, a well-established numerical methodology is applied to an industrial thermoplastic composite component. After considering an initial thermoplastic composite plate, the effect of the reduction of the overall dimensions, without compromising the product quality, on the environmental footprint is considered. To perform this task, a finite element (FE) simulation is utilized for assessing the possibility to reduce the plate dimensions without introducing defects while, in parallel, a life cycle assessment (LCA) is put into practice. Through the synergy between the 2 disciplines, useful insights are provided regarding the effectiveness of the fabrication process by defining the composite plate dimensions that contributes to the amelioration of the environmental footprint with the minimum expense on the component quality.

**Introduction**

Thermoplastic composite materials are considered as an appealing solution for producing lightweight components due to their unique features they exhibit such as their recyclability and the existence of a variety of well-established manufacturing production processes [1]. Among them, there can be distinguished the injection molding [2-3] for short-fiber reinforced composites, the compression molding [2] and thermoforming [4]. Especially the last one, copes with the deformation of a semi-finished, previously impregnated, composite lamina under a combined pressure-temperature cycle inside the cavity of the molds (punch and mold tools). After the extraction of the permanently deformed shape, the out-of-figure part is removed, and the definite product may be obtained [4-5]. Even though this method is currently applied to industrial products, the quality of the final component is a critical issue since intense shearing, wrinkles, residual stresses, and thickness variations may be introduced [5]. Therefore, the majority of the scientific

<figure><img src="image_2591.png" /></figure>

<footnote n="1">¹Department of Industrial and Information Engineering and Economy (DIIIE), University of L'Aquila, Via Giovanni Gronchi, 18, Nucleo Industriale Pile L'Aquila, 67100, Italy</footnote>

<footnote n="2">²Department of Manufacturing and Civil Engineering, Norwegian University of Science and Technology, Gjøvik 2815, Norway</footnote>

<footnote n="3">³Crossfire srl, Via Corona, 1, 48027, Solarolo, Ravenna, Italy</footnote>

<footnote>ᵃantonios.stamopoulos@univaq.it, ᵇangela.d.l.rosa@ntnu.no, ᶜg.creonti@crossfire-srl.com</footnote>
# A combined finite element – Life cycle assessment approach for assessing the sustainability of the thermoforming process of a thermoplastic composite component

STAMOPOULOS Antonios G.<fnref n="1" />,ᵃ*, LA ROSA Angela Daniela<fnref n="2" />,ᵇ and CREONTI Gianluigi<fnref n="3" />,ᶜ

**Keywords:** Thermoforming, Thermoplastic Composites, Finite Element Simulation, Life Cycle Assessment

**Abstract.** Nowadays, thermoplastic-based composite materials are catching up in many industrial sectors due to their unique features, especially their recyclability, their lower requirements regarding their storage and the fact that may be heated-up and formed into a definitive product. To this end, the thermoforming process appears to be an appealing fabrication process. However, throughout this process, a number of defects may be introduced that can affect the morphology and structural performance of the product. Until recently, the definition of the optimal process characteristics was based on trial-and-error experimental tests that increased the resources required for its development. In addition, a significant part of the thermoplastic composite plate has to be removed/wasted after the thermoforming process. In this work, a well-established numerical methodology is applied to an industrial thermoplastic composite component. After considering an initial thermoplastic composite plate, the effect of the reduction of the overall dimensions, without compromising the product quality, on the environmental footprint is considered. To perform this task, a finite element (FE) simulation is utilized for assessing the possibility to reduce the plate dimensions without introducing defects while, in parallel, a life cycle assessment (LCA) is put into practice. Through the synergy between the 2 disciplines, useful insights are provided regarding the effectiveness of the fabrication process by defining the composite plate dimensions that contributes to the amelioration of the environmental footprint with the minimum expense on the component quality.

**Introduction**

Thermoplastic composite materials are considered as an appealing solution for producing lightweight components due to their unique features they exhibit such as their recyclability and the existence of a variety of well-established manufacturing production processes [1]. Among them, there can be distinguished the injection molding [2-3] for short-fiber reinforced composites, the compression molding [2] and thermoforming [4]. Especially the last one, copes with the deformation of a semi-finished, previously impregnated, composite lamina under a combined pressure-temperature cycle inside the cavity of the molds (punch and mold tools). After the extraction of the permanently deformed shape, the out-of-figure part is removed, and the definite product may be obtained [4-5]. Even though this method is currently applied to industrial products, the quality of the final component is a critical issue since intense shearing, wrinkles, residual stresses, and thickness variations may be introduced [5]. Therefore, the majority of the scientific

<figure><img src="image_2584.png" /></figure>

<footnote n="1">¹Department of Industrial and Information Engineering and Economy (DIIIE), University of L'Aquila, Via Giovanni Gronchi, 18, Nucleo Industriale Pile L'Aquila, 67100, Italy</footnote>

<footnote n="2">²Department of Manufacturing and Civil Engineering, Norwegian University of Science and Technology, Gjøvik 2815, Norway</footnote>

<footnote n="3">³Crossfire srl, Via Corona, 1, 48027, Solarolo, Ravenna, Italy</footnote>

<footnote>ᵃantonios.stamopoulos@univaq.it, ᵇangela.d.l.rosa@ntnu.no, ᶜg.creonti@crossfire-srl.com</footnote>
research utilized benchmark geometries for further study and for ameliorating the predictability of the existing numerical tools. Following up this strategy, in the past few years, FE tools and numerical methods have been introduced that enabled the accurate prediction of these defects and, consequently, the definition of the optimal process parameters for minimizing them [4-5]. These parameters are mostly related to the temperature cycles, the applied pressure, the geometry orientation of the cavity of the molds, the stamping speed, and the auxiliary equipment [6].

On the other hand, the energy and resource efficiency of the process chain needs to be guaranteed as they contribute to the so-called climate neutral economy [7-8]. To this end, the life cycle assessment is a versatile method for addressing the impact of the semi-finished or definite products, considering factors such as materials, consumables, and energy requirements for fabricating these products [8]. Considering the characteristics of the abovementioned processes chain, the life cycle assessment may be a useful tool for quantifying the environmental impact of the variation of some process parameters.

In the present work, the LCA technique is employed in an industrial case study for quantifying the environmental impact while producing a thermoplastic composite component for the automotive sector. In parallel, an FE simulation campaign is conducted for defining the optimal process parameters and for excluding the experimental trial-and-error tests. In addition, the effect of the reduction of the composite plate dimensions, prior to the thermoforming process, reducing the material waste as well, and for increasing the product units per lamina by 1 is considered. By the synergy of these methods/disciplines, it was found a significant decrease of the environmental impact while reducing the composite plate dimensions prior to the thermoforming process, even though the material waste may be considered recyclable.

## Methodology, Manufacturing and Assumptions

The basic concept of the methodology of the present work is presented in Figure 1 and may be further divided into 3 main parts, namely the manufacturing of the component that includes all the fabrication chain processes, the finite element simulation for defining the thermoforming process parameters and the life cycle assessment. There should be noted that the present methodology was applied to an already-existing industrial equipment and, therefore, the restrictions regarding the machinery, relative production capacity (dimensions of the impregnated lamina, applied pressures, heating systems etc.) as well as the energy consumption are defined by the industrial partner. Consequently, the present work deals with an actual industrial case starting from raw materials and ending to the production of the finished product.
research utilized benchmark geometries for further study and for ameliorating the predictability of the existing numerical tools. Following up this strategy, in the past few years, FE tools and numerical methods have been introduced that enabled the accurate prediction of these defects and, consequently, the definition of the optimal process parameters for minimizing them [4-5]. These parameters are mostly related to the temperature cycles, the applied pressure, the geometry orientation of the cavity of the molds, the stamping speed, and the auxiliary equipment [6].

On the other hand, the energy and resource efficiency of the process chain needs to be guaranteed as they contribute to the so-called climate neutral economy [7-8]. To this end, the life cycle assessment is a versatile method for addressing the impact of the semi-finished or definite products, considering factors such as materials, consumables, and energy requirements for fabricating these products [8]. Considering the characteristics of the abovementioned processes chain, the life cycle assessment may be a useful tool for quantifying the environmental impact of the variation of some process parameters.

In the present work, the LCA technique is employed in an industrial case study for quantifying the environmental impact while producing a thermoplastic composite component for the automotive sector. In parallel, an FE simulation campaign is conducted for defining the optimal process parameters and for excluding the experimental trial-and-error tests. In addition, the effect of the reduction of the composite plate dimensions, prior to the thermoforming process, reducing the material waste as well, and for increasing the product units per lamina by 1 is considered. By the synergy of these methods/disciplines, it was found a significant decrease of the environmental impact while reducing the composite plate dimensions prior to the thermoforming process, even though the material waste may be considered recyclable.

## Methodology, Manufacturing and Assumptions

The basic concept of the methodology of the present work is presented in Figure 1 and may be further divided into 3 main parts, namely the manufacturing of the component that includes all the fabrication chain processes, the finite element simulation for defining the thermoforming process parameters and the life cycle assessment. There should be noted that the present methodology was applied to an already-existing industrial equipment and, therefore, the restrictions regarding the machinery, relative production capacity (dimensions of the impregnated lamina, applied pressures, heating systems etc.) as well as the energy consumption are defined by the industrial partner. Consequently, the present work deals with an actual industrial case starting from raw materials and ending to the production of the finished product.
<figure><img src="image_2592.png" /><figcaption>Figure 1: The outline of the methodology of the study performed in the present work.</figcaption></figure>

## *Manufacturing of the Thermoplastic Composite Component*

Before the thermoforming process, the initial Woven Textile E-glass/PP composite plate should be produced using the hot-pressing impregnation process where 2 films of polypropylene are combined with the E-glass fabric. After applying 20 kg/cm² of pressure for 40 seconds at 200 °C, a 4 m long and 0.7 wide mono-layer semi-finished composite plate (commercial name: CrossLam GppBx600 0/90) is obtained. Consecutively, a plotter is used for producing smaller rectangular plates that are thermoformed afterwards. Besides the production of smaller plates, a quantity of waste is obtained as residual from the process chain up to this point.

Regarding the main fabrication part (thermoforming), the process parameters such as the stamping speed and material temperature are defined by the finite element simulation. Using the finite element methodology presented in past works [6, 9] the dimensions of the small plates prior to the thermal forming are considered. This part is crucial since the dimensions of the composite plate define the waste that is produced at this point and the number of products that are extracted from the initial 4 x 0.7 [m] composite lamina. The equipment for conducting the thermoforming process considered by industrial part of the present study is composed of:

* A 4 axis press machine where the aluminium molds are placed.

* The molds that are heated up to 110 °C using heated oil through a pump system. The hot oil flows close to the external surfaces of the moulds. Once the target temperature is achieved, the system may be considered as isothermal since the losses are negligible and no continuous heating is required.

* A system of 4 couples of 2 kW infrared lamps for heating the composite material up to the temperature defined by the outcome of the FE simulation. In the present work, the material temperature is considered at 190 °C.

* No additional auxiliary equipment, such as tensioners, is considered.

Once the process is completed, the final product is extracted from the thermoformed composite plate using an anthropomorph cutter. Therefore, not only the final component is obtained but also the out-of-figure waste as well.
<figure><img src="image_2585.png" /><figcaption>Figure 1: The outline of the methodology of the study performed in the present work.</figcaption></figure>

## *Manufacturing of the Thermoplastic Composite Component*

Before the thermoforming process, the initial Woven Textile E-glass/PP composite plate should be produced using the hot-pressing impregnation process where 2 films of polypropylene are combined with the E-glass fabric. After applying 20 kg/cm² of pressure for 40 seconds at 200 °C, a 4 m long and 0.7 wide mono-layer semi-finished composite plate (commercial name: CrossLam GppBx600 0/90) is obtained. Consecutively, a plotter is used for producing smaller rectangular plates that are thermoformed afterwards. Besides the production of smaller plates, a quantity of waste is obtained as residual from the process chain up to this point.

Regarding the main fabrication part (thermoforming), the process parameters such as the stamping speed and material temperature are defined by the finite element simulation. Using the finite element methodology presented in past works [6, 9] the dimensions of the small plates prior to the thermal forming are considered. This part is crucial since the dimensions of the composite plate define the waste that is produced at this point and the number of products that are extracted from the initial 4 x 0.7 [m] composite lamina. The equipment for conducting the thermoforming process considered by industrial part of the present study is composed of:

* A 4 axis press machine where the aluminium molds are placed.

* The molds that are heated up to 110 °C using heated oil through a pump system. The hot oil flows close to the external surfaces of the moulds. Once the target temperature is achieved, the system may be considered as isothermal since the losses are negligible and no continuous heating is required.

* A system of 4 couples of 2 kW infrared lamps for heating the composite material up to the temperature defined by the outcome of the FE simulation. In the present work, the material temperature is considered at 190 °C.

* No additional auxiliary equipment, such as tensioners, is considered.

Once the process is completed, the final product is extracted from the thermoformed composite plate using an anthropomorph cutter. Therefore, not only the final component is obtained but also the out-of-figure waste as well.
Definition of the Process Parameters Through Finite Element Analysis

Considering the abovementioned characteristics of the process chain, an essential part is the definition of the thermoforming process parameters such as the material temperature under an already-defined stamping speed. In addition, for reducing the overall waste and for exploring the possibility of producing more components, the characteristics of the thermoformed product, in terms of residual stresses, probability of wrinkles formation and thickness variation, are addressed while reducing the dimensions of the smaller composite plates. Therefore, the role of the process simulation is about providing information regarding the product quality while reducing the dimensions of the thermoplastic composite plate. This task is performed using a well-established and accurate FE methodology [6,9] using the AniformTM FE code [10]. The geometry of the stamping and mold tools are presented in Figure 2. The material model is the one of a PP/GF balanced twill weave material with the same content as the one of interest in the present work, seen in [6]. The thermoforming simulation is conducted using membrane elements LTR3D membrane elements and the DKT (Direct Kirchhoff Triangle) shell elements where the in-plane shear and bending behaviour of the heated-up composite material are enclosed. Contact elements are placed at the top and bottom of the plate that enclose the dynamic friction behaviour of the composite material (tool-composite).

<figure><img src="image_2593.png" /><figcaption>Figure 2: The FE model set-up for simulating the thermoforming process and the dimensions of the 2 plates considered.</figcaption></figure>

The process is considered as isothermal while no cooling-down of the material is considered. Moreover, as seen in Fig. 2, the plate is oriented towards the y axis (warp). In addition, a complete and perfect closing of the molds is assumed, and no vacuum is present in order to comply with the production requirements of the industrial partner. After a parametric study of the mesh and the various material temperatures (160 °C, 190 °C) the characteristics of the thermoformed plate in terms of probability of wrinkles formation, stresses, shearing, and thickness variation are evaluated. Consequently, the thermoforming temperature is defined at 190 °C which is consistent with past works using similar materials for achieving satisfactory results: no wrinkles are present in the area of the final product and not higher textile shearing as well. The validity and robustness of the above-mentioned simulation methodology was previously validated in other applications as seen in past works [11-12] where the same material was studied.
Definition of the Process Parameters Through Finite Element Analysis

Considering the abovementioned characteristics of the process chain, an essential part is the definition of the thermoforming process parameters such as the material temperature under an already-defined stamping speed. In addition, for reducing the overall waste and for exploring the possibility of producing more components, the characteristics of the thermoformed product, in terms of residual stresses, probability of wrinkles formation and thickness variation, are addressed while reducing the dimensions of the smaller composite plates. Therefore, the role of the process simulation is about providing information regarding the product quality while reducing the dimensions of the thermoplastic composite plate. This task is performed using a well-established and accurate FE methodology [6,9] using the AniformTM FE code [10]. The geometry of the stamping and mold tools are presented in Figure 2. The material model is the one of a PP/GF balanced twill weave material with the same content as the one of interest in the present work, seen in [6]. The thermoforming simulation is conducted using membrane elements LTR3D membrane elements and the DKT (Direct Kirchhoff Triangle) shell elements where the in-plane shear and bending behaviour of the heated-up composite material are enclosed. Contact elements are placed at the top and bottom of the plate that enclose the dynamic friction behaviour of the composite material (tool-composite).

<figure><img src="image_2586.png" /><figcaption>Figure 2: The FE model set-up for simulating the thermoforming process and the dimensions of the 2 plates considered.</figcaption></figure>

The process is considered as isothermal while no cooling-down of the material is considered. Moreover, as seen in Fig. 2, the plate is oriented towards the y axis (warp). In addition, a complete and perfect closing of the molds is assumed, and no vacuum is present in order to comply with the production requirements of the industrial partner. After a parametric study of the mesh and the various material temperatures (160 °C, 190 °C) the characteristics of the thermoformed plate in terms of probability of wrinkles formation, stresses, shearing, and thickness variation are evaluated. Consequently, the thermoforming temperature is defined at 190 °C which is consistent with past works using similar materials for achieving satisfactory results: no wrinkles are present in the area of the final product and not higher textile shearing as well. The validity and robustness of the above-mentioned simulation methodology was previously validated in other applications as seen in past works [11-12] where the same material was studied.
## Objectives and Assumptions for Life Cycle Assessment

Life Cycle Assessment (LCA) is a tool that was developed to evaluate the environmental aspects and potential impacts (e.g., use of resources and the environmental consequences of releases) throughout a product's life cycle from raw material acquisition through production, use, end-of-life treatment, recycling, and final disposal (i.e., cradle-to-grave). The LCA methodology follows the ISO 14040-44 [13] and is structured in four main phases in an LCA study presented in Fig. 3.

<figure><img src="image_2594.png" /><figcaption>Figure 3: The interaction between the main phases of the life cycle assessment (LCA).</figcaption></figure>

The Goal and Scope definition phase includes:

* intended application of the study, reasons for carrying out the study, intended audience,

* product system,

* the functional unit; the system boundary; allocation procedures,

* the impact categories selected: methodology of impact assessment,

* data requirements; assumptions; limitations.

The Inventory analysis phase includes:

* data collection,

* calculation.

Impact assessment phase includes:

* selection of impact categories, category indicators and characterization model,

* assignment of Life Cycle Inventory (LCI) results to the selected impact categories, (classification)

* calculation of category indicator result (characterization).

Interpretation phase

* identification of the significant issues based on the results of the LCI and the Life Cycle Impact Assessment (LCIA) phases of the LCA,

* an evaluation that considers completeness, sensitivity, and consistency checks,

* conclusions, limitations, and recommendations.

LCA is an iterative approach where the individual phases use results from the other phases, as seen in Fig. 3. This approach leads to comprehensiveness and consistency of LCA studies and the reported results. However, LCA address potential environmental impacts but does not predict absolute or precise environmental impacts. This is, among other things, due to the relative expression of potential environmental impacts to a reference unit, the inherent uncertainty in modelling of environmental impacts and the fact that some possible environmental impacts are future impacts.
## Objectives and Assumptions for Life Cycle Assessment

Life Cycle Assessment (LCA) is a tool that was developed to evaluate the environmental aspects and potential impacts (e.g., use of resources and the environmental consequences of releases) throughout a product's life cycle from raw material acquisition through production, use, end-of-life treatment, recycling, and final disposal (i.e., cradle-to-grave). The LCA methodology follows the ISO 14040-44 [13] and is structured in four main phases in an LCA study presented in Fig. 3.

<figure><img src="image_2587.png" /><figcaption>Figure 3: The interaction between the main phases of the life cycle assessment (LCA).</figcaption></figure>

The Goal and Scope definition phase includes:

* intended application of the study, reasons for carrying out the study, intended audience,

* product system,

* the functional unit; the system boundary; allocation procedures,

* the impact categories selected: methodology of impact assessment,

* data requirements; assumptions; limitations.

The Inventory analysis phase includes:

* data collection,

* calculation.

Impact assessment phase includes:

* selection of impact categories, category indicators and characterization model,

* assignment of Life Cycle Inventory (LCI) results to the selected impact categories, (classification)

* calculation of category indicator result (characterization).

Interpretation phase

* identification of the significant issues based on the results of the LCI and the Life Cycle Impact Assessment (LCIA) phases of the LCA,

* an evaluation that considers completeness, sensitivity, and consistency checks,

* conclusions, limitations, and recommendations.

LCA is an iterative approach where the individual phases use results from the other phases, as seen in Fig. 3. This approach leads to comprehensiveness and consistency of LCA studies and the reported results. However, LCA address potential environmental impacts but does not predict absolute or precise environmental impacts. This is, among other things, due to the relative expression of potential environmental impacts to a reference unit, the inherent uncertainty in modelling of environmental impacts and the fact that some possible environmental impacts are future impacts.
## Comparative Life Cycle Assessment Inventory

The goal of the study is to conduct a comparative LCA of two plates (plate A and plate B) produced with the same materials and for the same function and final weight, but with different size. The difference between them may be observed in Fig.2 of the present work. Size optimization was performed by means of FEM analysis as seen in the previous section. The functional unit is defined as 1 plate with dimensions 0.8 x 0.52 x 0.5 [m] for Plate A and 0.6 x 0.43 x 0.5 [m] for Plate B respectively while the final product weight is 71,6 gr. Considering the dimensions of the plate A and B, 5 plates may be extracted from the initial lamina in the case of Plate A while 6 plates may be extracted in the case of Plate B. Inventory data of materials and energy consumption are reported in Table 1. The plates were extracted with a plotter from a GFRP laminate made through impregnation and hot-pressing of E-glass fiber textile (600 gr/m² x 2.8 m²= 1680 gr.) with polypropylene (2 films 345 gr in total). All the data regarding the energy consumption are related to the equipment/machine utilized are provided by the industrial partner (Crossfire srl, Solarolo, Italy).

<table><caption>Table 1: Inventory data of materials and energy consumption for the scenarios A and B.</caption><thead><tr><th>Scenario/Plate</th><th>Process</th><th>Machine</th><th>Energy Consumption [kWh]</th><th>Output</th><th>Material Quantity [gr]</th><th>Waste [gr]</th></tr></thead><tbody><tr><td rowspan="4">A</td><td>Impregnation</td><td>Continuous Press + Heating</td><td>25.9</td><td>1 monolayer plate</td><td>2025</td><td>-</td></tr><tr><td>Cutting in Plotter</td><td>Plotter</td><td>0.08</td><td>5 plates A</td><td>1505</td><td>520</td></tr><tr><td>Thermoforming of A at 190 °C</td><td>Hot Press + IR Lamps</td><td>7.89</td><td>1 thermoformed plate A</td><td>301</td><td>-</td></tr><tr><td>Cutter</td><td>Anthropomorph</td><td>0.012</td><td>Final Product</td><td>71.6</td><td>229.4</td></tr><tr><td rowspan="4">B</td><td>Impregnation</td><td>Continuous Press + Heating</td><td>25.9</td><td>1 monolayer plate</td><td>2025</td><td>-</td></tr><tr><td>Cutting in Plotter</td><td>Plotter</td><td>0.08</td><td>6 plates B</td><td>1122</td><td>903</td></tr><tr><td>Thermoforming of B at 190 °C</td><td>Hot Press + IR Lamps</td><td>6,54</td><td>1 thermoformed plate B</td><td>187</td><td>-</td></tr><tr><td>Cutter</td><td>Anthropomorph</td><td>0.0116</td><td>Final Product</td><td>71.6</td><td>115.4</td></tr></tbody></table>

## Results

The results are presented in a comparable way to understand the difference between the production of the same component using the initially considered plate dimensions (Plate A) and the minimized ones (Plate B). Starting with the global warming potential the network for the 2 cases is presented in Fig.4. In both cases, the impregnation and thermoforming processes contribute the most while the avoided impacts (green part of the network) are related to the recycle of the waste. Moreover, since the 2 roots for producing the product start from the same impregnation method, their differences mostly rely on the plate dimensions and the related energy for heating them up, prior to the thermoforming process.
## Comparative Life Cycle Assessment Inventory

The goal of the study is to conduct a comparative LCA of two plates (plate A and plate B) produced with the same materials and for the same function and final weight, but with different size. The difference between them may be observed in Fig.2 of the present work. Size optimization was performed by means of FEM analysis as seen in the previous section. The functional unit is defined as 1 plate with dimensions 0.8 x 0.52 x 0.5 [m] for Plate A and 0.6 x 0.43 x 0.5 [m] for Plate B respectively while the final product weight is 71,6 gr. Considering the dimensions of the plate A and B, 5 plates may be extracted from the initial lamina in the case of Plate A while 6 plates may be extracted in the case of Plate B. Inventory data of materials and energy consumption are reported in Table 1. The plates were extracted with a plotter from a GFRP laminate made through impregnation and hot-pressing of E-glass fiber textile (600 gr/m² x 2.8 m²= 1680 gr.) with polypropylene (2 films 345 gr in total). All the data regarding the energy consumption are related to the equipment/machine utilized are provided by the industrial partner (Crossfire srl, Solarolo, Italy).

<table><caption>Table 1: Inventory data of materials and energy consumption for the scenarios A and B.</caption><thead><tr><th>Scenario/ Plate</th><th>Process</th><th>Machine</th><th>Energy Consumption [kWh]</th><th>Output</th><th>Material Quantity [gr]</th><th>Waste [gr]</th></tr></thead><tbody><tr><td rowspan="4">A</td><td>Impregnation</td><td>Continuous Press + Heating</td><td>25.9</td><td>1 monolayer plate</td><td>2025</td><td>-</td></tr><tr><td>Cutting in Plotter</td><td>Plotter</td><td>0.08</td><td>5 plates A</td><td>1505</td><td>520</td></tr><tr><td>Thermoforming of A at 190 °C</td><td>Hot Press + IR Lamps</td><td>7.89</td><td>1 thermoformed plate A</td><td>301</td><td>-</td></tr><tr><td>Cutter</td><td>Anthropomorph</td><td>0.012</td><td>Final Product</td><td>71.6</td><td>229.4</td></tr><tr><td rowspan="4">B</td><td>Impregnation</td><td>Continuous Press + Heating</td><td>25.9</td><td>1 monolayer plate</td><td>2025</td><td>-</td></tr><tr><td>Cutting in Plotter</td><td>Plotter</td><td>0.08</td><td>6 plates B</td><td>1122</td><td>903</td></tr><tr><td>Thermoforming of B at 190 °C</td><td>Hot Press + IR Lamps</td><td>6,54</td><td>1 thermoformed plate B</td><td>187</td><td>-</td></tr><tr><td>Cutter</td><td>Anthropomorph</td><td>0.0116</td><td>Final Product</td><td>71.6</td><td>115.4</td></tr></tbody></table>

## Results

The results are presented in a comparable way to understand the difference between the production of the same component using the initially considered plate dimensions (Plate A) and the minimized ones (Plate B). Starting with the global warming potential the network for the 2 cases is presented in Fig.4. In both cases, the impregnation and thermoforming processes contribute the most while the avoided impacts (green part of the network) are related to the recycle of the waste. Moreover, since the 2 roots for producing the product start from the same impregnation method, their differences mostly rely on the plate dimensions and the related energy for heating them up, prior to the thermoforming process.
<figure><img src="image_2595.png" /><figcaption>Figure 4: Network of the global warming potential (GWP) related to the manufacturing of Plate A (a) and B(b) using the IPCC GWP100a method.</figcaption></figure>

In addition, the main environmental impacts for the 2 cases are presented in Fig.5. Since 6 products may be extracted from the initial composite lamina instead of 5, the reduction of the dimensions of the plates prior to the thermoforming process appears to significantly reduce the impacts related to the fossil fuel consumption (Fig.5a), the equivalent CO2 emitted (Fig.5b) as well as both the human and aquatic toxicities (Fig.5c and 5d).

<figure><img src="image_2596.png" /></figure>
<figure><img src="image_2588.png" /><figcaption>Figure 4: Network of the global warming potential (GWP) related to the manufacturing of Plate A (a) and B(b) using the IPCC GWP100a method.</figcaption></figure>

In addition, the main environmental impacts for the 2 cases are presented in Fig.5. Since 6 products may be extracted from the initial composite lamina instead of 5, the reduction of the dimensions of the plates prior to the thermoforming process appears to significantly reduce the impacts related to the fossil fuel consumption (Fig.5a), the equivalent CO2 emitted (Fig.5b) as well as both the human and aquatic toxicities (Fig.5c and 5d).

<figure><img src="image_2589.png" /></figure>
<figure><img src="image_2597.png" /><figcaption>Figure 5: Quantitative results of the most relevant impacts related to the abiotic depletion (fossil fuels) (a) and the equivalent CO2 emissions (b), human toxicity (c) and marine aquatic ecotoxicity (d) by the production of the thermoplastic composite component with the 2 plate scenarios.</figcaption></figure>

Having taken into consideration the characteristics of the processes chain, the additional 1 plate derived from the impregnated lamina is the main factor responsible for the reduction of the environmental impacts. Therefore, considering the fact that a significant part of the thermal-mechanical processes work under quasi-isothermal conditions, the reduction of the dimensions and the waste of a single plate before the thermoforming process does not affect significantly the energy required. As shown in figure 6 all the impact categories result lower for plate B compared to plate A and this was expected as, starting from the initial lamina, the impacts are allocated to 6 plates type B and to 5 plates type A.

<figure><img src="image_2598.png" /><figcaption>Figure 6. Comparative results with CML-IA baseline method.</figcaption></figure>

## Summary and Conclusions

In the present work, a methodology containing a synergy between finite element modelling, manufacturing and life cycle assessment was presented and was applied to a complex mono-layer PP/Woven Textile automotive component. The optimal material temperature was defined by the
<figure><img src="image_2590.png" /><figcaption>Figure 5: Quantitative results of the most relevant impacts related to the abiotic depletion (fossil fuels) (a) and the equivalent CO2 emissions (b), human toxicity (c) and marine aquatic ecotoxicity (d) by the production of the thermoplastic composite component with the 2 plate scenarios.</figcaption></figure>

Having taken into consideration the characteristics of the processes chain, the additional 1 plate derived from the impregnated lamina is the main factor responsible for the reduction of the environmental impacts. Therefore, considering the fact that a significant part of the thermal-mechanical processes work under quasi-isothermal conditions, the reduction of the dimensions and the waste of a single plate before the thermoforming process does not affect significantly the energy required. As shown in figure 6 all the impact categories result lower for plate B compared to plate A and this was expected as, starting from the initial lamina, the impacts are allocated to 6 plates type B and to 5 plates type A.

<figure><img src="image_2591.png" /><figcaption>Figure 6. Comparative results with CML-IA baseline method.</figcaption></figure>

## Summary and Conclusions

In the present work, a methodology containing a synergy between finite element modelling, manufacturing and life cycle assessment was presented and was applied to a complex mono-layer PP/Woven Textile automotive component. The optimal material temperature was defined by the
FE simulation. In addition, the reduction of the semifinished composite plate dimensions, cut from a previously impregnated lamina were considered. Using a virtual trial-and-error analysis, a reduction to the composite plate dimensions (from 0.8 x 0.52 to 0.6 x 0.43 [m]) was achieved having similar the length-to-width (L/w) ratios (1.54 for Plate A, 1.40 for Plate B).

From the LCA the following conclusions may be extracted:

* The thermomechanical processes occupy a major part of the environmental impact due to the significant energy required for the impregnation and the thermoforming of the composite material.

* Even though the recyclability of the waste produced by the process chain was considered, the positive impact appears to be significantly lower since the material is recycled and not re-used as is.

* The reduction of the dimensions of the composite plate increased the products per lamina by 1. This fact is the main reason behind the decrease of the environmental impact related to the production using the Plate B compared to Plate A.

## Funding

The present work was partially funded by the national Norwegian project "1-2-TRE-STEG: Steps towards circularity in wood-based".

## References

[1] M. Patel, B. Pardhi, S. Chopara, M. Pal, Lightweight Composite Materials for Automotive - A Review: International Research Journal of Engineering and Technology 5 (11) (2018) 41-47. e-ISSN: 2395-0056

[2] W. Zhang, J. Hu, Advanced lightweight materials for automotive applications: A Review: Materials and Design 221 (2022) 110994. https://doi.org/10.1016/j.matdes.2022.110994

[3] A.G. Stamopoulos, F. Gazza, G. Neirotti, Assessment of the compressive mechanical behavior of injection molded E-glass/polypropylene by mechanical testing and X-ray computed tomography: The International Journal of Advanced Manufacturing Technology 126 (2023) 209-223. https://doi.org/10.1007/s00170-023-11094-w

[4] S.P. Haanappel, U. Sachs, R.H.W. ten Thije, B. Rietman, R. Akkerman: Forming of Thermoplastic Composites. Key Engineering Materials 504 (2012), 237-242. 10.4028/www.scientific.net/KEM.504-506.237

[5] G. D'Emilia, A. Gaspari, E. Natale, A.G. Stamopoulos, A. Di Ilio: Experimental and numerical analysis of the defects induced by the thermoforming process on woven textile thermoplastic composites. Engineering Failure Analysis 135 (2022) 106093. https://doi.org/10.1016/j.engfailanal.2022.106093

[6] A.G. Stamopoulos, L.G. Di Genova, A. Di Ilio: Simulation of the thermoforming process of glass-fiber reinforced polymeric components: investigation of the combined effect of the crosshead speed and the material temperature. The International Journal of Advanced Manufacturing Technology 117 (2021) 2987-3009. https://doi.org/10.1007/s00170-021-07845-2

[7] G. Ingarao, M. Amato, A. Latif, A.D. La Rosa, R. Di Lorenzo, L. Fratini. Life cycle assessment of aluminium alloy chips recycling through single and multi-step friction stir consolidation processes, Journal of Manufacturing Systems 68 (2023) 651-659. https://doi.org/10.1016/j.jmsy.2023.05.021

[8] F. Gagliardi, A.D. La Rosa, L. Filice, G. Ambrogio: Environmental impact of material selection in a car body component-the side door intrusion beam, Journal of Cleaner Production 318 (2021) 128528. https://doi.org/10.1016/j.jclepro.2021.128528
FE simulation. In addition, the reduction of the semifinished composite plate dimensions, cut from a previously impregnated lamina were considered. Using a virtual trial-and-error analysis, a reduction to the composite plate dimensions (from 0.8 x 0.52 to 0.6 x 0.43 [m]) was achieved having similar the length-to-width (L/w) ratios (1.54 for Plate A, 1.40 for Plate B).

From the LCA the following conclusions may be extracted:

* The thermomechanical processes occupy a major part of the environmental impact due to the significant energy required for the impregnation and the thermoforming of the composite material.

* Even though the recyclability of the waste produced by the process chain was considered, the positive impact appears to be significantly lower since the material is recycled and not re-used as is.

* The reduction of the dimensions of the composite plate increased the products per lamina by 1. This fact is the main reason behind the decrease of the environmental impact related to the production using the Plate B compared to Plate A.

## Funding

The present work was partially funded by the national Norwegian project "1-2-TRE-STEG: Steps towards circularity in wood-based".

## References

[1] M. Patel, B. Pardhi, S. Chopara, M. Pal, Lightweight Composite Materials for Automotive - A Review: International Research Journal of Engineering and Technology 5 (11) (2018) 41-47. e-ISSN: 2395-0056

[2] W. Zhang, J. Hu, Advanced lightweight materials for automotive applications: A Review: Materials and Design 221 (2022) 110994. https://doi.org/10.1016/j.matdes.2022.110994

[3] A.G. Stamopoulos, F. Gazza, G. Neirotti, Assessment of the compressive mechanical behavior of injection molded E-glass/polypropylene by mechanical testing and X-ray computed tomography: The International Journal of Advanced Manufacturing Technology 126 (2023) 209-223. https://doi.org/10.1007/s00170-023-11094-w

[4] S.P. Haanappel, U. Sachs, R.H.W. ten Thije, B. Rietman, R. Akkerman: Forming of Thermoplastic Composites. Key Engineering Materials 504 (2012), 237-242. 10.4028/www.scientific.net/KEM.504-506.237

[5] G. D'Emilia, A. Gaspari, E. Natale, A.G. Stamopoulos, A. Di Ilio: Experimental and numerical analysis of the defects induced by the thermoforming process on woven textile thermoplastic composites. Engineering Failure Analysis 135 (2022) 106093. https://doi.org/10.1016/j.engfailanal.2022.106093

[6] A.G. Stamopoulos, L.G. Di Genova, A. Di Ilio: Simulation of the thermoforming process of glass-fiber reinforced polymeric components: investigation of the combined effect of the crosshead speed and the material temperature. The International Journal of Advanced Manufacturing Technology 117 (2021) 2987-3009. https://doi.org/10.1007/s00170-021-07845-2

[7] G. Ingarao, M. Amato, A. Latif, A.D. La Rosa, R. Di Lorenzo, L. Fratini. Life cycle assessment of aluminium alloy chips recycling through single and multi-step friction stir consolidation processes, Journal of Manufacturing Systems 68 (2023) 651-659. https://doi.org/10.1016/j.jmsy.2023.05.021

[8] F. Gagliardi, A.D. La Rosa, L. Filice, G. Ambrogio: Environmental impact of material selection in a car body component-the side door intrusion beam, Journal of Cleaner Production 318 (2021) 128528. https://doi.org/10.1016/j.jclepro.2021.128528
[9] S.N. Haanappel, R.H.W. Ten Thije, U. Sachs, B. Rietman, R. Akkerman: Formability analyses of uni-directional and textile reinforced thermoplastics. Composites: Part A 56 (2014) 80-92.
https://doi.org/10.1016/j.compositesa.2013.09.009

[10] AniForm Engineering BV. https://aniform.com/features

[11] A.G. Stamopoulos, A. Di Ilio, L.G. Di Genova. Simulation of the Thermoforming Process of Glass Fiber Reinforced Polymeric Components: Investigation of the Combined Effect of the Crosshead Speed and Material Temperature. The International Journal of Advanced Manufacturing Technology 117 (2021) 2987-3009. https://doi.org/10.1007/s00170-021-07845-2

[12] G. D'Emilia G., A. Gaspari, E. Natale, A.G. Stamopoulos, A. Di Ilio. Experimental and numerical analysis of the defects induced by the thermoforming process on woven textile thermoplastic composites. Engineering Failure Analysis 135 (2022) 106093.
https://doi.org/10.1016/j.engfailanal.2022.106093

[13] ISO 14040:2006. Environmental Management-Life cycle assessment-Principles and framework. International Organization for Standardization.
[9] S.N. Haanappel, R.H.W. Ten Thije, U. Sachs, B. Rietman, R. Akkerman: Formability analyses of uni-directional and textile reinforced thermoplastics. Composites: Part A 56 (2014) 80-92. https://doi.org/10.1016/j.compositesa.2013.09.009

[10] AniForm Engineering BV. https://aniform.com/features

[11] A.G. Stamopoulos, A. Di Ilio, L.G. Di Genova. Simulation of the Thermoforming Process of Glass Fiber Reinforced Polymeric Components: Investigation of the Combined Effect of the Crosshead Speed and Material Temperature. The International Journal of Advanced Manufacturing Technology 117 (2021) 2987-3009. https://doi.org/10.1007/s00170-021-07845-2

[12] G. D'Emilia G., A. Gaspari, E. Natale, A.G. Stamopoulos, A. Di Ilio. Experimental and numerical analysis of the defects induced by the thermoforming process on woven textile thermoplastic composites. Engineering Failure Analysis 135 (2022) 106093. https://doi.org/10.1016/j.engfailanal.2022.106093

[13] ISO 14040:2006. Environmental Management-Life cycle assessment-Principles and framework. International Organization for Standardization.
# Wear monitoring of metal sheet stamping process driven by acoustic emission data

TIANJIAO Bi¹,²,³,a, LEI Li¹,²,³,b and HAIHONG Huang¹,²,³,c*

¹School of Mechanical Engineering, Hefei University of Technology, Anhui, Hefei 230009, China

²Key Laboratory of Green Design and Manufacturing of Machinery Industry, Hefei University of Technology, Anhui, Hefei 230009, China

³Anhui Province Key Laboratory of Low Carbon Recycling Technology and Equipment for Mechanical and Electrical Products, Anhui, Hefei 230009, China

a2020010009@mail.hfut.edu.cn, blei_li@hfut.edu.cn, c huanghaihong@hfut.edu.cn

**Keywords:** Stamping Molds, Wear Monitoring, Acoustic Emission

**Abstract.** The study addresses limitations related to mold wear during the stamping process, which impairs part quality due to the low predictability of wear, leading to increased production scrap and unreliable production schedules. To tackle these issues, a tool condition monitoring strategy driven by Acoustic Emission (AE) data is proposed for assessing the wear state of stamping molds. An integrated model for decomposing AE signals, employing the rolling decomposition method, is developed. This model eliminates the reliance on future data to avoid information leakage and is capable of monitoring critical wear indicators in stamping molds. The experimental phase, conducted in an industrial-grade stamping process setting, involved collecting degradation signals from healthy to fatigued states to verify the effectiveness of the proposed method. The outcomes indicate that the data-driven approach, utilizing AE signals, is efficient in monitoring stamping mold wear.

**This article has been removed due to copyright issues**

<figure><img src="image_2599.png" /></figure>
# Wear monitoring of metal sheet stamping process driven by acoustic emission data

TIANJIAO Bi¹,²,³,a, LEI Li¹,²,³,b and HAIHONG Huang¹,²,³,c*

¹School of Mechanical Engineering, Hefei University of Technology, Anhui, Hefei 230009, China

²Key Laboratory of Green Design and Manufacturing of Machinery Industry, Hefei University of Technology, Anhui, Hefei 230009, China

³Anhui Province Key Laboratory of Low Carbon Recycling Technology and Equipment for Mechanical and Electrical Products, Anhui, Hefei 230009, China

a2020010009@mail.hfut.edu.cn, blei_li@hfut.edu.cn, c huanghaihong@hfut.edu.cn

**Keywords:** Stamping Molds, Wear Monitoring, Acoustic Emission

**Abstract.** The study addresses limitations related to mold wear during the stamping process, which impairs part quality due to the low predictability of wear, leading to increased production scrap and unreliable production schedules. To tackle these issues, a tool condition monitoring strategy driven by Acoustic Emission (AE) data is proposed for assessing the wear state of stamping molds. An integrated model for decomposing AE signals, employing the rolling decomposition method, is developed. This model eliminates the reliance on future data to avoid information leakage and is capable of monitoring critical wear indicators in stamping molds. The experimental phase, conducted in an industrial-grade stamping process setting, involved collecting degradation signals from healthy to fatigued states to verify the effectiveness of the proposed method. The outcomes indicate that the data-driven approach, utilizing AE signals, is efficient in monitoring stamping mold wear.

**This article has been removed due to copyright issues**

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This article has been removed due to copyright issues
This article has been removed due to copyright issues
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This article has been removed due to copyright issues
This article has been removed due to copyright issues
This article has been removed due to copyright issues
This article has been removed due to copyright issues
This article has been removed due to copyright issues
This article has been removed due to copyright issues
This article has been removed due to copyright issues
This article has been removed due to copyright issues
This article has been removed due to copyright issues
This article has been removed due to copyright issues
This article has been removed due to copyright issues
This article has been removed due to copyright issues
This article has been removed due to copyright issues
# Property grading by friction induced and continuous solid-state recycling of aluminium scrap

BORGERT Thomas¹,ᵃ*, NORDIEKER Ansgar¹,ᵇ and HOMBERG Werner¹,ᶜ

¹Forming and Machining Technology, Paderborn University, Warburger Straße 100, 33098 Paderborn, Germany

ᵃtb@luf.upb.de, ᵇan@luf.upb.de, ᶜwh@luf.upb.de

**Keywords:** Recycling, Property-Grading, Aluminium, Energy

**Abstract.** Saving energy and reducing emissions in all sectors is essential if the ambitious climate targets of various European countries are to be met. One way of achieving this is to recycle metallic materials, which require a lot of energy to produce, in a way that minimizes the use of energy and resources. The friction-induced solid-state recycling process enables the energy-efficient recycling of what is in theory an endless aluminium semi-finished product through the continuous rotation of the wheel used. The past investigations proved the energy-efficient recycling of new aluminium scrap (powder, foil, chips) to a full semi-finished product with good properties. The continuous character of the process along with the likewise continuous feeding of the material to be recycled enables both mixing and successive processing of different aluminium alloys. For this purpose, the processed four different aluminium alloys are selectively mixed and processed simultaneously, as well as different alloys are processed one after the other to achieve a gradation of properties along the length of the semi-finished product. The recycled semi-finished product is examined regarding die filling, hardness, tensile strength as well as microstructure. The central result of the investigations is the fact that the friction-induced recycling process has different possibilities for grading the properties and microstructure in a wide range.

**Introduction**

Reducing greenhouse emissions is essential to limit global warming. In many parts of the world, political guidelines are being developed and implemented. In order for Germany to meet its climate targets by 2030, measures must be implemented in many areas of industry, transport, trade and housing [1].

If the emissions of complex systems and assemblies are analysed in the context of engineering, both the emissions generated during production and during use must be considered. For example, the production emissions for a car with a petrol or diesel engine account for 15-20% of total emissions. For a fully electric car, assuming a green electricity mix, up to 95% of the total emissions are those from production [2].

In order to reduce emissions during the production of complex systems, it makes sense to change the production of semi-finished products. In theory, metals such as aluminium can be recycled an infinite number of times [3]. The advantage of recycled materials is that the energy required for recycling is significantly lower than for primary production. Only 10% of the energy required for the primary production of aluminium is needed to recycle aluminium. Recycled aluminium therefore has a significantly lower energy balance of 17.7 MJ/kg [4]. This means that the production of aluminium using recycling processes has great potential for savings compared to primary production [5]. Research in recent years has shown that in addition to the conventional recycling of aluminium, in which the aluminium has to be melted down and toxic slag is produced, solid-state processes are very suitable for recycling [6].

Some studies have shown that it is possible to produce semi-finished products from aluminium waste using a conventional extrusion line. To enable processing on a conventional system, the

<figure><img src="image_2600.png" /></figure>
# Property grading by friction induced and continuous solid-state recycling of aluminium scrap

BORGERT Thomas¹,ᵃ*, NORDIEKER Ansgar¹,ᵇ and HOMBERG Werner¹,ᶜ

¹Forming and Machining Technology, Paderborn University, Warburger Straße 100, 33098 Paderborn, Germany

ᵃtb@luf.upb.de, ᵇan@luf.upb.de, ᶜwh@luf.upb.de

**Keywords:** Recycling, Property-Grading, Aluminium, Energy

**Abstract.** Saving energy and reducing emissions in all sectors is essential if the ambitious climate targets of various European countries are to be met. One way of achieving this is to recycle metallic materials, which require a lot of energy to produce, in a way that minimizes the use of energy and resources. The friction-induced solid-state recycling process enables the energy-efficient recycling of what is in theory an endless aluminium semi-finished product through the continuous rotation of the wheel used. The past investigations proved the energy-efficient recycling of new aluminium scrap (powder, foil, chips) to a full semi-finished product with good properties. The continuous character of the process along with the likewise continuous feeding of the material to be recycled enables both mixing and successive processing of different aluminium alloys. For this purpose, the processed four different aluminium alloys are selectively mixed and processed simultaneously, as well as different alloys are processed one after the other to achieve a gradation of properties along the length of the semi-finished product. The recycled semi-finished product is examined regarding die filling, hardness, tensile strength as well as microstructure. The central result of the investigations is the fact that the friction-induced recycling process has different possibilities for grading the properties and microstructure in a wide range.

**Introduction**

Reducing greenhouse emissions is essential to limit global warming. In many parts of the world, political guidelines are being developed and implemented. In order for Germany to meet its climate targets by 2030, measures must be implemented in many areas of industry, transport, trade and housing [1].

If the emissions of complex systems and assemblies are analysed in the context of engineering, both the emissions generated during production and during use must be considered. For example, the production emissions for a car with a petrol or diesel engine account for 15-20% of total emissions. For a fully electric car, assuming a green electricity mix, up to 95% of the total emissions are those from production [2].

In order to reduce emissions during the production of complex systems, it makes sense to change the production of semi-finished products. In theory, metals such as aluminium can be recycled an infinite number of times [3]. The advantage of recycled materials is that the energy required for recycling is significantly lower than for primary production. Only 10% of the energy required for the primary production of aluminium is needed to recycle aluminium. Recycled aluminium therefore has a significantly lower energy balance of 17.7 MJ/kg [4]. This means that the production of aluminium using recycling processes has great potential for savings compared to primary production [5]. Research in recent years has shown that in addition to the conventional recycling of aluminium, in which the aluminium has to be melted down and toxic slag is produced, solid-state processes are very suitable for recycling [6].

Some studies have shown that it is possible to produce semi-finished products from aluminium waste using a conventional extrusion line. To enable processing on a conventional system, the

<figure><img src="image_2593.png" /></figure>
aluminium waste is first compacted in a hydraulic press. They are then extruded into a semi-finished product using large plastic deformations in an extrusion line. Profiles can be produced that have similar or better properties than conventionally recycled aluminium. The reason for this is that the aluminium oxides are broken up by the plastic deformation, dispersed in the semi-finished product and serve as reinforcement [7]. It has also been shown that an ECAP mould improves the mechanical properties and electrical conductivity of the extruded semi-finished products because it ensures higher pressures and higher strains [8, 9].

To reduce the heat input from the outside, the extrusion process can be carried out with a rotating die. This process is known as friction extrusion. Frictional heat is generated by constant axial pressure and the rotation of the die, which heats the chips in the billet chamber. This plasticises them and they flow through the hole in the die. The process has good energy efficiency because no external heat needs to be supplied to plasticise the aluminium [10].

Both processes presented have a discontinuous process flow. An approach to the continuous recycling of chips is being researched using the Conform process. In those research titanium chips are processed in a conventional conform plant. The results show good ductility and strength of the extruded wire. The surfaces, on the other hand, show poor quality. Additionally, the mould could not be filled, and the resulting cross-section is therefore not ideally round [11].

In addition to the explained current research to reduce emissions during production, it is also possible to cut emissions during the utilisation phase of a product. For example, in order to reduce vehicle emissions during the use phase, steel materials are increasingly being replaced by aluminium or magnesium. These have a low density and some, such as EN AW-7075, have strengths comparable to those of steel [12]. By substituting materials, up to 10 % of CO2 emissions per kilometre can be saved during use [13]. In addition to the substitution of materials, it is important that the components of the vehicle are designed to be load-adapted. This avoids unnecessary material accumulation and further reduces weight. There are two ways of doing this. On the one hand, a component can be locally reinforced with a higher-strength material such as carbon fibre [14] or the strength of the material can be increased. This can be achieved, for example, by partially changing the properties of the material [15]. As a result, localised load peaks can be absorbed by the high-strength area. On the other hand, a higher material thickness can be set locally. This results in a localised reduction in stresses due to the larger cross-sectional area. As a result, a weight reduction of up to 20 % is possible [16].

The investigations carried out here show how an energy-efficient, continuous recycling process for aluminium chips enables the production of load-adapted semi-finished products. Therefore, it offers the possibility to reduce emissions during the production and the use phase of the later product. The process has been developed at the Chair of Forming and Machining Technology, Paderborn University. The experimental setup works according to the principles of the Conform process. The advantage of the innovative process is the continuous processing of raw materials like chips. In theory, unlimited lengths of semi-finished products can be produced. Furthermore, the process does not require an external heat source, but works with frictional heat and thus is very energy-efficient. As a result, a self-regulating stationary state of the process is achieved after a short time, in which the temperature, torque and mass flow of the supplied chips are kept constant. The central point of this research is how the mixing of different aluminium alloys affects the process and the semi-finished product produced. The reason for this is that chips in an industrial environment are rarely ideally separated, but often consist of a mixture of different alloys. Furthermore, two materials can also be deliberately mixed in order to adjust the chemical, physical or mechanical properties of the semi-finished product. This allows local areas with higher strengths to be produced and components with load-optimised properties to be manufactured.
aluminium waste is first compacted in a hydraulic press. They are then extruded into a semi-finished product using large plastic deformations in an extrusion line. Profiles can be produced that have similar or better properties than conventionally recycled aluminium. The reason for this is that the aluminium oxides are broken up by the plastic deformation, dispersed in the semi-finished product and serve as reinforcement [7]. It has also been shown that an ECAP mould improves the mechanical properties and electrical conductivity of the extruded semi-finished products because it ensures higher pressures and higher strains [8, 9].

To reduce the heat input from the outside, the extrusion process can be carried out with a rotating die. This process is known as friction extrusion. Frictional heat is generated by constant axial pressure and the rotation of the die, which heats the chips in the billet chamber. This plasticises them and they flow through the hole in the die. The process has good energy efficiency because no external heat needs to be supplied to plasticise the aluminium [10].

Both processes presented have a discontinuous process flow. An approach to the continuous recycling of chips is being researched using the Conform process. In those research titanium chips are processed in a conventional conform plant. The results show good ductility and strength of the extruded wire. The surfaces, on the other hand, show poor quality. Additionally, the mould could not be filled, and the resulting cross-section is therefore not ideally round [11].

In addition to the explained current research to reduce emissions during production, it is also possible to cut emissions during the utilisation phase of a product. For example, in order to reduce vehicle emissions during the use phase, steel materials are increasingly being replaced by aluminium or magnesium. These have a low density and some, such as EN AW-7075, have strengths comparable to those of steel [12]. By substituting materials, up to 10 % of CO2 emissions per kilometre can be saved during use [13]. In addition to the substitution of materials, it is important that the components of the vehicle are designed to be load-adapted. This avoids unnecessary material accumulation and further reduces weight. There are two ways of doing this. On the one hand, a component can be locally reinforced with a higher-strength material such as carbon fibre [14] or the strength of the material can be increased. This can be achieved, for example, by partially changing the properties of the material [15]. As a result, localised load peaks can be absorbed by the high-strength area. On the other hand, a higher material thickness can be set locally. This results in a localised reduction in stresses due to the larger cross-sectional area. As a result, a weight reduction of up to 20 % is possible [16].

The investigations carried out here show how an energy-efficient, continuous recycling process for aluminium chips enables the production of load-adapted semi-finished products. Therefore, it offers the possibility to reduce emissions during the production and the use phase of the later product. The process has been developed at the Chair of Forming and Machining Technology, Paderborn University. The experimental setup works according to the principles of the Conform process. The advantage of the innovative process is the continuous processing of raw materials like chips. In theory, unlimited lengths of semi-finished products can be produced. Furthermore, the process does not require an external heat source, but works with frictional heat and thus is very energy-efficient. As a result, a self-regulating stationary state of the process is achieved after a short time, in which the temperature, torque and mass flow of the supplied chips are kept constant. The central point of this research is how the mixing of different aluminium alloys affects the process and the semi-finished product produced. The reason for this is that chips in an industrial environment are rarely ideally separated, but often consist of a mixture of different alloys. Furthermore, two materials can also be deliberately mixed in order to adjust the chemical, physical or mechanical properties of the semi-finished product. This allows local areas with higher strengths to be produced and components with load-optimised properties to be manufactured.
## Methods and Materials

The set-up used for the tests is shown in Figure 1. At its centre, the process consists of a wheel, a tool insert and a tool holder. The wheel is driven by a motor during the process. In addition, a gearbox is installed between the two components to enable the high torques. For all investigations carried out for this paper, the speed was set at $n = 11$ rpm. During the entire process, the aluminium chips are filled into the designated groove of the wheel via a funnel. The groove has a width $w = 7$ mm and a depth $t = 6$ mm. The cross-sectional area available for the chips is continuously reduced by the tool insert to compress the chips. As a result, the air between the chips is pressed out of the groove. As a result, the density, which initially varies between $\rho = 0.24$ g/cm$^3$ - 0.35 g/cm$^3$ depending on the chip shape, increases to 2.7 g/cm$^3$. In addition to the compression of the chips, the relative movement between the wheel and the aluminium residues leads to friction. Due to the friction, the process itself generates the necessary heat to plasticise the chips. High pressures are generated between the wheel and the tool insert during the process, which cause the oxide layers of the aluminium to break open in the last third of the forming zone and thus weld the chips together. At the end of the tool insert there is a counterholder that deflects the material and ensures additional forming of the aluminium. This breaks up the remaining oxide layers and a homogeneous semi-finished product is extruded. The extruded wire in the tests presented here has a diameter of $d = 5$ mm.

<figure><img src="image_2601.png" /><figcaption>Figure 1: Set-up of the friction induced recycling process with a) all components required for extrusion and b) a close-up of the extrusion zone and the temperature measuring points [17]</figcaption></figure>

Chips of two wrought alloys and one cast alloy are used for the experimental investigations. Defined ratios of the alloys EN AW-6060, EN AW-7075 and EN AC-43000 are mixed and then fed into the process. In each case, two of the alloys are mixed together in different mass ratios. The mixture alloys analysed are listed in Table 1.

<table><caption>Table 1: List of the mixtures produced from the various aluminium alloys</caption><thead><tr><th>Alloys and mixing ratio</th></tr></thead><tbody><tr><td>50% EN AW-6060</td></tr><tr><td>50% EN AW-7075</td></tr><tr><td>50% EN AW-6060</td></tr><tr><td>50% EN AC-43000</td></tr><tr><td>75% EN AW-6060</td></tr><tr><td>25% EN AW-7075</td></tr><tr><td>75% EN AW-6060</td></tr><tr><td>25% EN AC-43000</td></tr></tbody></table>
## Methods and Materials

The set-up used for the tests is shown in Figure 1. At its centre, the process consists of a wheel, a tool insert and a tool holder. The wheel is driven by a motor during the process. In addition, a gearbox is installed between the two components to enable the high torques. For all investigations carried out for this paper, the speed was set at $n = 11$ rpm. During the entire process, the aluminium chips are filled into the designated groove of the wheel via a funnel. The groove has a width $w = 7$ mm and a depth $t = 6$ mm. The cross-sectional area available for the chips is continuously reduced by the tool insert to compress the chips. As a result, the air between the chips is pressed out of the groove. As a result, the density, which initially varies between $\rho = 0.24$ g/cm$^3$ - 0.35 g/cm$^3$ depending on the chip shape, increases to 2.7 g/cm$^3$. In addition to the compression of the chips, the relative movement between the wheel and the aluminium residues leads to friction. Due to the friction, the process itself generates the necessary heat to plasticise the chips. High pressures are generated between the wheel and the tool insert during the process, which cause the oxide layers of the aluminium to break open in the last third of the forming zone and thus weld the chips together. At the end of the tool insert there is a counterholder that deflects the material and ensures additional forming of the aluminium. This breaks up the remaining oxide layers and a homogeneous semi-finished product is extruded. The extruded wire in the tests presented here has a diameter of $d = 5$ mm.

<figure><img src="image_2594.png" /><figcaption>Figure 1: Set-up of the friction induced recycling process with a) all components required for extrusion and b) a close-up of the extrusion zone and the temperature measuring points [17]</figcaption></figure>

Chips of two wrought alloys and one cast alloy are used for the experimental investigations. Defined ratios of the alloys EN AW-6060, EN AW-7075 and EN AC-43000 are mixed and then fed into the process. In each case, two of the alloys are mixed together in different mass ratios. The mixture alloys analysed are listed in Table 1.

<table><caption>Table 1: List of the mixtures produced from the various aluminium alloys</caption><thead><tr><th>Alloys and mixing ratio</th></tr></thead><tbody><tr><td>50% EN AW-6060</td></tr><tr><td>50% EN AW-7075</td></tr><tr><td>50% EN AW-6060</td></tr><tr><td>50% EN AC-43000</td></tr><tr><td>75% EN AW-6060</td></tr><tr><td>25% EN AW-7075</td></tr><tr><td>75% EN AW-6060</td></tr><tr><td>25% EN AC-43000</td></tr></tbody></table>
As aluminium chips are used in the investigations, the characteristic features of the individual chip shapes of the different alloys are shown in Table 2. The ratio of volume to surface area is relevant for processing, as this ratio allows conclusions to be drawn about the volume proportion of aluminium oxide in the entire chip. An increasing oxide content reduces the ductility of the semi-finished product produced.

<table><caption>Table 2: Characteristic properties of the aluminium chips used</caption><thead><tr><th>Alloy</th><th>EN AW-6060</th><th>EN AW-7075</th><th>EN AC-43000</th></tr></thead><tbody><tr><th>Characteristic</th><td></td><td></td><td></td></tr><tr><th>Chip length [mm]</th><td>2.1 ± 0.26</td><td>1.7 ± 0.1</td><td>1.8 ± 0.06</td></tr><tr><th>Chip width [mm]</th><td>1.8 ± 0.12</td><td>1.1 ± 0.1</td><td>1.5 ± 0.13</td></tr><tr><th>Chip thickness [mm]</th><td>0.1 ± 0.01</td><td>0.1 ± 0.01</td><td>0.1 ± 0.01</td></tr><tr><th>Bulk density [g/cm³]</th><td>0,29 ± 0.01</td><td>0.24 ± 0.01</td><td>0.34 ± 0.01</td></tr></tbody></table>

To analyse the mixtures, the stationary process temperature is recorded during the experiment and differences between the alloys and their mixtures are discussed. In addition, the hardness, tensile strength and uniform elongation of the semi-finished products produced are analysed. The hardness measurement is carried out using the Vickers method with an indentation force of N = 2.942 N (HV0.3). A hardness measurement is carried out at a total of thirteen points in the wire cross-section. The distribution of the points can be seen in Figure 2.

<figure><img src="image_2602.png" /><figcaption>Figure 2: Position of the thirteen hardness measuring points on the cross-section of the extruded semi-finished product</figcaption></figure>

## Results and discussion

As the alloys used have different mechanical properties, an effect on the temperature is to be expected. Table 3 lists the steady-state process temperatures of the friction-induced recycling process for the alloys and their mixtures analysed. As the alloys EN AW-7075 and EN AC-43000 have higher strengths compared to EN AW-6060, the steady-state process temperature for these two alloys is higher. The reason for this is that the flow stress required for the process is only achieved at higher temperatures.

If the EN AW-6060 (75 %) alloy is mixed with the EN AW-7075 (25 %) alloy, a steady-state final temperature of $T_s \approx 350$ °C is achieved with a low mass fraction of EN AW-7075 (25 %). This lies between the steady-state end temperatures of the two pure alloys
As aluminium chips are used in the investigations, the characteristic features of the individual chip shapes of the different alloys are shown in Table 2. The ratio of volume to surface area is relevant for processing, as this ratio allows conclusions to be drawn about the volume proportion of aluminium oxide in the entire chip. An increasing oxide content reduces the ductility of the semi-finished product produced.

<table><caption>Table 2: Characteristic properties of the aluminium chips used</caption><thead><tr><th>Alloy</th><th>EN AW-6060</th><th>EN AW-7075</th><th>EN AC-43000</th></tr></thead><tbody><tr><th>Characteristic</th><td></td><td></td><td></td></tr><tr><th>Chip length [mm]</th><td>2.1 ± 0.26</td><td>1.7 ± 0.1</td><td>1.8 ± 0.06</td></tr><tr><th>Chip width [mm]</th><td>1.8 ± 0.12</td><td>1.1 ± 0.1</td><td>1.5 ± 0.13</td></tr><tr><th>Chip thickness [mm]</th><td>0.1 ± 0.01</td><td>0.1 ± 0.01</td><td>0.1 ± 0.01</td></tr><tr><th>Bulk density [g/cm³]</th><td>0,29 ± 0.01</td><td>0.24 ± 0.01</td><td>0.34 ± 0.01</td></tr></tbody></table>

To analyse the mixtures, the stationary process temperature is recorded during the experiment and differences between the alloys and their mixtures are discussed. In addition, the hardness, tensile strength and uniform elongation of the semi-finished products produced are analysed. The hardness measurement is carried out using the Vickers method with an indentation force of N = 2.942 N (HV0.3). A hardness measurement is carried out at a total of thirteen points in the wire cross-section. The distribution of the points can be seen in Figure 2.

<figure><img src="image_2595.png" /><figcaption>Figure 2: Position of the thirteen hardness measuring points on the cross-section of the extruded semi-finished product</figcaption></figure>

## Results and discussion

As the alloys used have different mechanical properties, an effect on the temperature is to be expected. Table 3 lists the steady-state process temperatures of the friction-induced recycling process for the alloys and their mixtures analysed. As the alloys EN AW-7075 and EN AC-43000 have higher strengths compared to EN AW-6060, the steady-state process temperature for these two alloys is higher. The reason for this is that the flow stress required for the process is only achieved at higher temperatures.

If the EN AW-6060 (75 %) alloy is mixed with the EN AW-7075 (25 %) alloy, a steady-state final temperature of $T_s \approx 350$ °C is achieved with a low mass fraction of EN AW-7075 (25 %). This lies between the steady-state end temperatures of the two pure alloys
$(T_{S,EN AW-6060} \approx 310 \,^\circ\text{C}, T_{S,EN AW-7075} \approx 410 \,^\circ\text{C})$. If the proportion of EN AW-7075 is increased to $n_L = 50 \%$, the steady-state final temperature rises to $T_S \approx 410 \,^\circ\text{C}$. This corresponds to the steady-state process temperature of the pure alloy EN AW-7075. A similar phenomenon can be observed if the alloy EN AW-6060 (50 %) is mixed with EN AC-43000 (50 %). Then the steady-state final temperature of $T_S \approx 390 \,^\circ\text{C}$ also corresponds to that of the cast alloy EN AC-43000.

Thus, the steady-state processing temperature during the process does not scale linearly with the mass fraction of the alloys. If a certain limiting quantity of the higher-strength alloy is mixed (approximately 50 %), the process temperature rises to that of the higher-strength alloy. From this it can be concluded that this is the dominant alloy in terms of the process temperature at a higher mass fraction.

<table><caption>Table 3: Stationary process temperatures of the analysed alloys and their mixtures</caption><thead><tr><td>Alloy / alloy mixture</td><td>Stationary process temperature</td></tr></thead><tbody><tr><td>50% EN AW-6060 / 50% EN AW-7075</td><td>~ 410 °C</td></tr><tr><td>50% EN AW-6060 / 50% EN AC-43000</td><td>~ 390 °C</td></tr><tr><td>75% EN AW-6060 / 25% EN AW-7075</td><td>~ 350 °C</td></tr><tr><td>75% EN AW-6060 / 25% EN AC-43000</td><td>~ 340 °C</td></tr><tr><td>EN AW-6060</td><td>~ 310 °C</td></tr><tr><td>EN AW-7075</td><td>~ 410 °C</td></tr><tr><td>EN AC-43000</td><td>~ 390 °C</td></tr></tbody></table>

Figure 3 compares the mechanical hardness of the profiles produced from the chips of different alloys with the hardness of the profiles produced from pure alloys from the friction-induced recycling process. The figure shows the mean values from three measurements. The hardness points are measured on the cross-section of the profile according to the procedure described. In addition to the measured values from the experiment, a calculation of the theoretically expected hardness values is also shown. The calculations are carried out to check whether the mechanical properties of two or more alloys can be predicted using a simple method. For the calculation, the respective proportion of the different alloys is multiplied by the mechanical characteristic value (in this case the hardness) of the pure alloy. The corresponding values are then added together. To determine the mechanical properties, the alloys used were processed in their pure state in the friction-induced recycling process and the parameters were determined. The hardness values also show that the characteristic values of EN AW-7075 dominate in a mixture of EN AW-7075 (50 %) and EN AW-6060 (50 %). At $H = 98.9$ HV0.3, the profile produced shows only a slight difference to the hardness of the base alloy of $H = 103$ HV0.3. If the calculated value is considered for this test point, it is noticeable that it is significantly lower at $H = 73.5$ HV0.3. For all other mixtures, however, the calculated values deviate only slightly from the hardness values determined in the experimental test.
$(T_{S,EN AW-6060} \approx 310 \,^\circ\text{C}, T_{S,EN AW-7075} \approx 410 \,^\circ\text{C})$. If the proportion of EN AW-7075 is increased to $n_L = 50 \%$, the steady-state final temperature rises to $T_S \approx 410 \,^\circ\text{C}$. This corresponds to the steady-state process temperature of the pure alloy EN AW-7075. A similar phenomenon can be observed if the alloy EN AW-6060 (50 %) is mixed with EN AC-43000 (50 %). Then the steady-state final temperature of $T_S \approx 390 \,^\circ\text{C}$ also corresponds to that of the cast alloy EN AC-43000.

Thus, the steady-state processing temperature during the process does not scale linearly with the mass fraction of the alloys. If a certain limiting quantity of the higher-strength alloy is mixed (approximately 50 %), the process temperature rises to that of the higher-strength alloy. From this it can be concluded that this is the dominant alloy in terms of the process temperature at a higher mass fraction.

<table><caption>Table 3: Stationary process temperatures of the analysed alloys and their mixtures</caption><thead><tr><td>Alloy / alloy mixture</td><td>Stationary process temperature</td></tr></thead><tbody><tr><td>50% EN AW-6060 / 50% EN AW-7075</td><td>~ 410 °C</td></tr><tr><td>50% EN AW-6060 / 50% EN AC-43000</td><td>~ 390 °C</td></tr><tr><td>75% EN AW-6060 / 25% EN AW-7075</td><td>~ 350 °C</td></tr><tr><td>75% EN AW-6060 / 25% EN AC-43000</td><td>~ 340 °C</td></tr><tr><td>EN AW-6060</td><td>~ 310 °C</td></tr><tr><td>EN AW-7075</td><td>~ 410 °C</td></tr><tr><td>EN AC-43000</td><td>~ 390 °C</td></tr></tbody></table>

Figure 3 compares the mechanical hardness of the profiles produced from the chips of different alloys with the hardness of the profiles produced from pure alloys from the friction-induced recycling process. The figure shows the mean values from three measurements. The hardness points are measured on the cross-section of the profile according to the procedure described. In addition to the measured values from the experiment, a calculation of the theoretically expected hardness values is also shown. The calculations are carried out to check whether the mechanical properties of two or more alloys can be predicted using a simple method. For the calculation, the respective proportion of the different alloys is multiplied by the mechanical characteristic value (in this case the hardness) of the pure alloy. The corresponding values are then added together. To determine the mechanical properties, the alloys used were processed in their pure state in the friction-induced recycling process and the parameters were determined. The hardness values also show that the characteristic values of EN AW-7075 dominate in a mixture of EN AW-7075 (50 %) and EN AW-6060 (50 %). At $H = 98.9$ HV0.3, the profile produced shows only a slight difference to the hardness of the base alloy of $H = 103$ HV0.3. If the calculated value is considered for this test point, it is noticeable that it is significantly lower at $H = 73.5$ HV0.3. For all other mixtures, however, the calculated values deviate only slightly from the hardness values determined in the experimental test.
<figure><img src="image_2603.png" /><figcaption>Figure 3: Hardness of the different alloys and mixtures investigated</figcaption></figure>

In addition to the mechanical hardness, the recycled wires were subjected to tensile testing in form F in accordance with DIN 50125. The results, shown in Figure 4, are the respective mean values from at least three samples for each test point. The tensile strengths achieved for the alloy mixtures are almost identical to the calculated values. Nevertheless, the mean values of the experimental tests are slightly higher than the calculated values for all mixtures. If, for example, the tensile strength to be expected for a mixture of EN AW-6060 (75 %) and EN AW-7075 (25 %) is calculated based on the previously determined tensile strengths of the pure alloys, a strength of $R_m = 185$ MPa is anticipated. The investigation resulted in an average strength value of $R_m = 207.4$ MPa. This represents a deviation of 12 % from the calculated value. Deviations of 10 – 15 % are also found for the other mixtures.

<figure><img src="image_2604.png" /><figcaption>Figure 4: Achieved tensile strengths of the different alloys and mixtures</figcaption></figure>

The results for the uniform elongation are shown in Figure 5. It is evident that the calculated results deviate from the measured results. The deviation for a mixture of EN AW-6060 (75 %) and EN AC-43000 (25 %) is a particularly noticeable difference. A uniform elongation of $A_g = 22.2$ % was measured in the experimental test. The elongation is therefore around 93 % greater than the calculated result ($A_g = 11.5$ %). For the other test points, the measured uniform elongation is below the calculated values.
<figure><img src="image_2596.png" /><figcaption>Figure 3: Hardness of the different alloys and mixtures investigated</figcaption></figure>

In addition to the mechanical hardness, the recycled wires were subjected to tensile testing in form F in accordance with DIN 50125. The results, shown in Figure 4, are the respective mean values from at least three samples for each test point. The tensile strengths achieved for the alloy mixtures are almost identical to the calculated values. Nevertheless, the mean values of the experimental tests are slightly higher than the calculated values for all mixtures. If, for example, the tensile strength to be expected for a mixture of EN AW-6060 (75 %) and EN AW-7075 (25 %) is calculated based on the previously determined tensile strengths of the pure alloys, a strength of $R_m = 185$ MPa is anticipated. The investigation resulted in an average strength value of $R_m = 207.4$ MPa. This represents a deviation of 12 % from the calculated value. Deviations of 10 – 15 % are also found for the other mixtures.

<figure><img src="image_2597.png" /><figcaption>Figure 4: Achieved tensile strengths of the different alloys and mixtures</figcaption></figure>

The results for the uniform elongation are shown in Figure 5. It is evident that the calculated results deviate from the measured results. The deviation for a mixture of EN AW-6060 (75 %) and EN AC-43000 (25 %) is a particularly noticeable difference. A uniform elongation of $A_g = 22.2$ % was measured in the experimental test. The elongation is therefore around 93 % greater than the calculated result ($A_g = 11.5$ %). For the other test points, the measured uniform elongation is below the calculated values.
<figure><img src="image_2605.png" /><figcaption>Figure 5: Achieved uniform elongations of the pure alloys and mixtures</figcaption></figure>

In addition to an influence on the mechanical properties, the microstructure is also influenced by mixing different aluminium alloys. Figure 6 shows the microstructure of a mixture of EN AW-6060 (75 %) and the proportions of the cast alloy EN AC-43000 (25 %) shown with a green frame. As the mass fraction of the wrought alloy is significantly higher, the microstructure of the casting alloy can only be recognised as embedded stripes in the mixture. Due to the compaction, extrusion and plasticisation of the material, it is heavily deformed and the width of the stripes deviates significantly from the chip thickness of the EN AC-43000 chips used ($d_s$ = 0.1 mm). The fact that the grain structures of the different alloys can be easily distinguished from each other makes it possible to draw conclusions about the process. In future investigations, the alloy content can be specifically adjusted to investigate the material flow during the process in more detail. In addition, the deformation acting on the aluminium chips can be approximated.

<figure><img src="image_2606.png" /><figcaption>Figure 6: Microstructure of an alloy mixture of 75 % EN AW-6060 and 25 % EN AC-43000</figcaption></figure>

## Summary

This paper briefly introduces the friction-induced recycling of aluminium chips and explains the process. This is a solid-state process and does not require an external heat source. The process is also characterised by the fact that it runs continuously, and no interruptions are necessary. In the friction-induced recycling process presented, defined mixture ratios of three aluminium alloys were processed. The experimental investigations proved that it is possible to adjust the properties
<figure><img src="image_2598.png" /><figcaption>Figure 5: Achieved uniform elongations of the pure alloys and mixtures</figcaption></figure>

In addition to an influence on the mechanical properties, the microstructure is also influenced by mixing different aluminium alloys. Figure 6 shows the microstructure of a mixture of EN AW-6060 (75 %) and the proportions of the cast alloy EN AC-43000 (25 %) shown with a green frame. As the mass fraction of the wrought alloy is significantly higher, the microstructure of the casting alloy can only be recognised as embedded stripes in the mixture. Due to the compaction, extrusion and plasticisation of the material, it is heavily deformed and the width of the stripes deviates significantly from the chip thickness of the EN AC-43000 chips used ($d_s$ = 0.1 mm). The fact that the grain structures of the different alloys can be easily distinguished from each other makes it possible to draw conclusions about the process. In future investigations, the alloy content can be specifically adjusted to investigate the material flow during the process in more detail. In addition, the deformation acting on the aluminium chips can be approximated.

<figure><img src="image_2599.png" /><figcaption>Figure 6: Microstructure of an alloy mixture of 75 % EN AW-6060 and 25 % EN AC-43000</figcaption></figure>

## Summary

This paper briefly introduces the friction-induced recycling of aluminium chips and explains the process. This is a solid-state process and does not require an external heat source. The process is also characterised by the fact that it runs continuously, and no interruptions are necessary. In the friction-induced recycling process presented, defined mixture ratios of three aluminium alloys were processed. The experimental investigations proved that it is possible to adjust the properties
of the extruded semi-finished product. For example, the addition of EN AW-7075 to an EN AW-6060 leads to a significant increase in strength. In addition, the achievable tensile strength and hardness can be predicted using a simple mathematical model. By mixing two alloys, the properties can be changed locally using the process presented. It is also possible to process the alloys one after the other without interrupting the process. This means, for example, that components can be produced from EN AW-6060 and locally reinforced with EN AW-7075. As a result, the semi-finished product can be manufactured to suit the load during its use phase and the components made from it are lighter and lead to lower emissions during use.

## Acknowledgments

The authors would like to thank the German Research Foundation (DFG) for funding the research project with the project number 500288680: "Influence of the surface chemistry of aluminium chips on a friction-induced recycling process for the production of aluminium wires".

## References

[1] BMU, Climate Action Report 2017 on the German Government's Climate Action Programme 2020, 2018

[2] J. Buberger, A. Kersten, M. Kuder, R. Eckerle, T. Weyh, T. Thiringer, Total CO2-equivalent life-cycle emissions from commercially available passenger cars, Renew. Sust. Energ. Rev. 159 (2022). https://doi.org/10.1016/j.rser.2022.112158

[3] Q. Dai, J.C. Kelly, A. Burnham, A. Elgowainy, Updated Life-Cycle Analysis of Aluminum Production and Semi-Fabrication for the GREET Model, Energy Systems Division, 2015. https://doi.org/10.2172/1224957

[4] H.-G. Schwarz, Aluminum Production and Energy, Encyclopedia of Energy 1 (2004) 81–95. https://doi.org/10.1016/B0-12-176480-X/00372-7

[5] E. Balomenos, D. Panias, I. Paspaliaris, Energy and Exergy Analysis of the Primary Aluminum Production Processes: A Review on Current and Future Sustainability, Miner. Process. Extr. Metall. 32 (2011) 69–89. https://doi.org/10.1080/08827508.2010.530721

[6] S. Shamsudin, M.A. Lajis, Z.W. Zhong, Solid-state recycling of light metals: A review, Adv. Mech. Eng. 8 (2016) 1–23. https://doi.org/10.1177/1687814016661921

[7] J.B. Fogagnolo, E.M. Ruiz-Navas, M.A. Simón, M.A. Martinez, Recycling of aluminium alloy and aluminium matrix composite chips by pressing and hot extrusion, J. Mater. Process. Technol. 143 (2003) 792–795. https://doi.org/10.1016/S0924-0136(03)00380-7

[8] M. Haase, N. Ben Khalifa, A.E. Tekkaya, W.Z. Misiolek, Improving mechanical properties of chip-based aluminum extrudates by integrated extrusion and equal channel angular pressing (iECAP), Mater Sci Eng A 539 (2012) 194–204. https://doi.org/10.1016/j.msea.2012.01.081

[9] A. Wagiman, M. S. Mustapa, R. Asmawi, S. Shamzudin, M. A. Lajis, Y. Mutoh, A review on direct hot extrusion technique in recycling of aluminium chips, Int. J. Adv. Manuf. Technol. 106 (2019) 641–653. https://doi.org/10.1007/s00170-019-04629-7

[10] U.F.H. Suhuddin, L. Rath, R.M. Halak, B. Klusemann, Microstructure evolution and texture development during production of homogeneous fine-grained aluminum wire by friction extrusion, Mater. Charact. 205 (2023). https://doi.org/10.1016/j.matchar.2023.113252

[11] S.A. Smythe, B.M. Thomas, D. Suarez-Fernandez, X. Fang, B. Marguet, M. Jackson, Recycling of titanium alloy swarf directly into wire using the conform TM continuous extrusion process, MATEC Web Conf 321 (2020). https://doi.org/10.1051/matecconf/202032103028
of the extruded semi-finished product. For example, the addition of EN AW-7075 to an EN AW-6060 leads to a significant increase in strength. In addition, the achievable tensile strength and hardness can be predicted using a simple mathematical model. By mixing two alloys, the properties can be changed locally using the process presented. It is also possible to process the alloys one after the other without interrupting the process. This means, for example, that components can be produced from EN AW-6060 and locally reinforced with EN AW-7075. As a result, the semi-finished product can be manufactured to suit the load during its use phase and the components made from it are lighter and lead to lower emissions during use.

## Acknowledgments

The authors would like to thank the German Research Foundation (DFG) for funding the research project with the project number 500288680: "Influence of the surface chemistry of aluminium chips on a friction-induced recycling process for the production of aluminium wires".

## References

[1] BMU, Climate Action Report 2017 on the German Government's Climate Action Programme 2020, 2018

[2] J. Buberger, A. Kersten, M. Kuder, R. Eckerle, T. Weyh, T. Thiringer, Total CO2-equivalent life-cycle emissions from commercially available passenger cars, Renew. Sust. Energ. Rev. 159 (2022). https://doi.org/10.1016/j.rser.2022.112158

[3] Q. Dai, J.C. Kelly, A. Burnham, A. Elgowainy, Updated Life-Cycle Analysis of Aluminum Production and Semi-Fabrication for the GREET Model, Energy Systems Division, 2015. https://doi.org/10.2172/1224957

[4] H.-G. Schwarz, Aluminum Production and Energy, Encyclopedia of Energy 1 (2004) 81–95. https://doi.org/10.1016/B0-12-176480-X/00372-7

[5] E. Balomenos, D. Panias, I. Paspaliaris, Energy and Exergy Analysis of the Primary Aluminum Production Processes: A Review on Current and Future Sustainability, Miner. Process. Extr. Metall. 32 (2011) 69–89. https://doi.org/10.1080/08827508.2010.530721

[6] S. Shamsudin, M.A. Lajis, Z.W. Zhong, Solid-state recycling of light metals: A review, Adv. Mech. Eng. 8 (2016) 1–23. https://doi.org/10.1177/1687814016661921

[7] J.B. Fogagnolo, E.M. Ruiz-Navas, M.A. Simón, M.A. Martinez, Recycling of aluminium alloy and aluminium matrix composite chips by pressing and hot extrusion, J. Mater. Process. Technol. 143 (2003) 792–795. https://doi.org/10.1016/S0924-0136(03)00380-7

[8] M. Haase, N. Ben Khalifa, A.E. Tekkaya, W.Z. Misiolek, Improving mechanical properties of chip-based aluminum extrudates by integrated extrusion and equal channel angular pressing (iECAP), Mater Sci Eng A 539 (2012) 194–204. https://doi.org/10.1016/j.msea.2012.01.081

[9] A. Wagiman, M. S. Mustapa, R. Asmawi, S. Shamzudin, M. A. Lajis, Y. Mutoh, A review on direct hot extrusion technique in recycling of aluminium chips, Int. J. Adv. Manuf. Technol. 106 (2019) 641–653. https://doi.org/10.1007/s00170-019-04629-7

[10] U.F.H. Suhuddin, L. Rath, R.M. Halak, B. Klusemann, Microstructure evolution and texture development during production of homogeneous fine-grained aluminum wire by friction extrusion, Mater. Charact. 205 (2023). https://doi.org/10.1016/j.matchar.2023.113252

[11] S.A. Smythe, B.M. Thomas, D. Suarez-Fernandez, X. Fang, B. Marguet, M. Jackson, Recycling of titanium alloy swarf directly into wire using the conform TM continuous extrusion process, MATEC Web Conf 321 (2020). https://doi.org/10.1051/matecconf/202032103028
[12] R. Schneider, B. Heine, R.J. Grant, Z. Zouaoui, Mechanical behaviour of aircraft relevant aluminium wrought alloys at low temperatures, Proc. Inst. Mech. Eng. Pt. L. J. Mater. Des. Appl. 229 (2015) 126-136. https://doi.org/10.1177/14644207135017342015

[13] Y. Türe, C. Türe, An assessment of using Aluminum and Magnesium on CO2 emission in European passenger cars, J. Clean. Prod. 247 (2020). https://doi.org/10.1016/j.jclepro.2019.119120

[14] D.-J. Kim, J. Lim, B. Nam, H.-J. Kim, H.-S. Kim, Design and Manufacture of Automotive Hybrid Steel/Carbon Fiber Composite B-Pillar Component with High Crashworthiness, Int. J. Pr. Eng. Man.-G.T. 8 (2021) 547-559. https://doi.org/10.1007/s40684-020-00188-5

[15] R. George, M. Worswick, D. Detwiler, J. Kang, Impact Testing of a Hot-Formed B-Pillar with Tailored Properties - Experiments and Simulation, SAE Int. J. Mater. Manf. 6 (2013) 157-162. https://doi.org/10.4271/2013-01-0608

[16] A. Schrek, P. Švec, V. Gajdosová, Deformation properties of tailor-welded blank made of dual phase steels, Acta Mech. et Autom. 10 (2016) 38-42. https://doi.org/10.1515/ama-2016-0007

[17] T. Borgert, W. Homberg, Analysis of temperature effect on strength and microstructure in friction induced recycling process (FIRP), Mater. Res. Proc. (2023). https://doi.org/10.21741/9781644902479-211
[12] R. Schneider, B. Heine, R.J. Grant, Z. Zouaoui, Mechanical behaviour of aircraft relevant aluminium wrought alloys at low temperatures, Proc. Inst. Mech. Eng. Pt. L. J. Mater. Des. Appl. 229 (2015) 126-136. https://doi.org/10.1177/14644207135017342015

[13] Y. Türe, C. Türe, An assessment of using Aluminum and Magnesium on CO2 emission in European passenger cars, J. Clean. Prod. 247 (2020). https://doi.org/10.1016/j.jclepro.2019.119120

[14] D.-J. Kim, J. Lim, B. Nam, H.-J. Kim, H.-S. Kim, Design and Manufacture of Automotive Hybrid Steel/Carbon Fiber Composite B-Pillar Component with High Crashworthiness, Int. J. Pr. Eng. Man.-G.T. 8 (2021) 547-559. https://doi.org/10.1007/s40684-020-00188-5

[15] R. George, M. Worswick, D. Detwiler, J. Kang, Impact Testing of a Hot-Formed B-Pillar with Tailored Properties - Experiments and Simulation, SAE Int. J. Mater. Manf. 6 (2013) 157-162. https://doi.org/10.4271/2013-01-0608

[16] A. Schrek, P. Švec, V. Gajdosová, Deformation properties of tailor-welded blank made of dual phase steels, Acta Mech. et Autom. 10 (2016) 38-42. https://doi.org/10.1515/ama-2016-0007

[17] T. Borgert, W. Homberg, Analysis of temperature effect on strength and microstructure in friction induced recycling process (FIRP), Mater. Res. Proc. (2023). https://doi.org/10.21741/9781644902479-211
# Influence of surface treatments on the fatigue strength of cross bores in shafts from EN-GJS700-2

UHLMANN Lars<fnref n="1" />,ª*, DADGAR Mohammad<fnref n="1" />,ᵇ, MÜLLER Martina<fnref n="1" />,ᶜ, HERRIG Tim<fnref n="1" />,ᵈ and BERGS Thomas<fnref n="1" />,<fnref n="2" />,ᵉ

**Keywords:** Deep Rolling, Residual Stresses, Shot Peening, Induction Hardening

**Abstract.** The European Union's Federal Climate Protection Act mandates a 55 % reduction in greenhouse gas emissions from 1990 levels by 2030, aiming for climate neutrality by 2050. Achieving these goals requires emission cuts across sectors, including energy, industry, and transportation. Lightweight design in transportation, achieved through alternative materials, geometry optimization, or enhanced fatigue strength, is vital. Highly stressed components like shafts are prone to failure due to notch locations which reduces the fatigue strength significantly. Inducing compressive residual stresses through methods like deep rolling and shot peening may improve the fatigue strength by reducing critical tensile load stresses. This study compares the fatigue strength of shaft cross bores treated with induction hardening, deep rolling, shot peening, and the combination of induction hardening and deep rolling. The research aims to establish a cause-effect relationship between surface layer properties and fatigue strength, considering the redistribution of residual stresses during testing.

## Introduction

The Federal Climate Protection Act was enacted in the European Union to mitigate anthropogenic climate change. According to the act, greenhouse gas emissions must be reduced by 55 % from 1990 levels by 2030 and become climate neutral by 2050 [1]. To achieve these ambitious climate targets, it is necessary to reduce greenhouse gas emissions in all relevant areas, including the energy, industry, and transport sector. In the transport sector, lightweight design is often pursued to reduce component weight, leading to lower material usage and energy requirements during operation. This can be achieved through the use of alternative materials [2], geometry optimization [3], or increased fatigue strength of existing components [4]. The latter term is used specifically for components that are subjected to high levels of stress. One example of highly stressed components in the transport sector are shafts. The fatigue strength of shafts is limited by notch locations, such as shaft heels or cross bores, as these usually represent the starting point for structural failure, such as crack initiation and crack growth [5]. To increase the fatigue strength of shafts, the surface layer is inductively hardened [6] or compressive residual stresses are induced into the surface layer [7]. The depth effect of inductive hardening is limited, which limits the influence on cross bores in particular. The compressive residual stresses counteract the critical tensile load stresses, which increases the fatigue strength [8]. Typical processes for inducing compressive residual stresses are deep rolling and shot peening. Both processes are mechanical surface treatments in which local plastic deformation induces compressive residual stresses. The compressive residual stresses are typically direction-dependent after deep rolling [9] and direction-independent after shot peening

<figure><img src="image_2607.png" /></figure>

<footnote n="1">¹ Manufacturing Technology Institute MTI of RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany</footnote>

<footnote n="2">² Fraunhofer Institute for Production Technology IPT, Steinbachstraße 17, 52074 Aachen, Germany</footnote>

<footnote>ªl.uhlmann@mti.rwth-aachen.de, ᵇm.dadgar@mti.rwth-aachen.de,</footnote>

<footnote>ᶜm.mueller@mti.rwth-aachen.de, ᵈt.herrig@mti.rwth-aachen.de, ᶜt.bergs@mti.rwth-aachen.de</footnote>
# Influence of surface treatments on the fatigue strength of cross bores in shafts from EN-GJS700-2

UHLMANN Lars<fnref n="1" />,ª*, DADGAR Mohammad<fnref n="1" />,ᵇ, MÜLLER Martina<fnref n="1" />,ᶜ, HERRIG Tim<fnref n="1" />,ᵈ and BERGS Thomas<fnref n="1" />,<fnref n="2" />,ᵉ

**Keywords:** Deep Rolling, Residual Stresses, Shot Peening, Induction Hardening

**Abstract.** The European Union's Federal Climate Protection Act mandates a 55 % reduction in greenhouse gas emissions from 1990 levels by 2030, aiming for climate neutrality by 2050. Achieving these goals requires emission cuts across sectors, including energy, industry, and transportation. Lightweight design in transportation, achieved through alternative materials, geometry optimization, or enhanced fatigue strength, is vital. Highly stressed components like shafts are prone to failure due to notch locations which reduces the fatigue strength significantly. Inducing compressive residual stresses through methods like deep rolling and shot peening may improve the fatigue strength by reducing critical tensile load stresses. This study compares the fatigue strength of shaft cross bores treated with induction hardening, deep rolling, shot peening, and the combination of induction hardening and deep rolling. The research aims to establish a cause-effect relationship between surface layer properties and fatigue strength, considering the redistribution of residual stresses during testing.

## Introduction

The Federal Climate Protection Act was enacted in the European Union to mitigate anthropogenic climate change. According to the act, greenhouse gas emissions must be reduced by 55 % from 1990 levels by 2030 and become climate neutral by 2050 [1]. To achieve these ambitious climate targets, it is necessary to reduce greenhouse gas emissions in all relevant areas, including the energy, industry, and transport sector. In the transport sector, lightweight design is often pursued to reduce component weight, leading to lower material usage and energy requirements during operation. This can be achieved through the use of alternative materials [2], geometry optimization [3], or increased fatigue strength of existing components [4]. The latter term is used specifically for components that are subjected to high levels of stress. One example of highly stressed components in the transport sector are shafts. The fatigue strength of shafts is limited by notch locations, such as shaft heels or cross bores, as these usually represent the starting point for structural failure, such as crack initiation and crack growth [5]. To increase the fatigue strength of shafts, the surface layer is inductively hardened [6] or compressive residual stresses are induced into the surface layer [7]. The depth effect of inductive hardening is limited, which limits the influence on cross bores in particular. The compressive residual stresses counteract the critical tensile load stresses, which increases the fatigue strength [8]. Typical processes for inducing compressive residual stresses are deep rolling and shot peening. Both processes are mechanical surface treatments in which local plastic deformation induces compressive residual stresses. The compressive residual stresses are typically direction-dependent after deep rolling [9] and direction-independent after shot peening

<figure><img src="image_2600.png" /></figure>

<footnote n="1">¹ Manufacturing Technology Institute MTI of RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany</footnote>

<footnote n="2">² Fraunhofer Institute for Production Technology IPT, Steinbachstraße 17, 52074 Aachen, Germany</footnote>

<footnote>ªl.uhlmann@mti.rwth-aachen.de, ᵇm.dadgar@mti.rwth-aachen.de,</footnote>

<footnote>ᶜm.mueller@mti.rwth-aachen.de, ᵈt.herrig@mti.rwth-aachen.de, ᶜt.bergs@mti.rwth-aachen.de</footnote>
[10]. During deep rolling, the compressive residual stresses are typically lower in the feed direction than perpendicular to it. In both processes, the hardness and roughness are modified in addition to the residual stresses. The roughness after deep rolling is typically lower than after shot peening. This is due to the stochastic characteristics of shot peening compared to the deterministic deep rolling. The influence of shot peening of cross bores in shafts from EN-GJS-700-2 and 34CrNiMo6 +QT on the fatigue strength was investigated by Reissner et al [11]. In the work it was shown, that shot peening influences the slope of the S-N curve due to a redistribution of residual stresses. Transferring the results to deep rolled cross bores is not necessarily possible because the residual stress distribution and roughness characteristics are different from those after shot peening. Accordingly, the influence of the surface layer properties of especially deep rolled but also shot peened cross bores on the fatigue strength is insufficiently investigated. The aim of the work is therefore to determine the cause-effect relationships between the surface layer properties set by shot peening and deep rolling on the fatigue strength of shafts with cross bores. For this purpose, experimental fatigue strength tests were carried out on analogous specimens made of EN-GJS-700-2 with untreated, induction hardened, deep rolled, shot peened and induction hardened and subsequently deep rolled cross bores. In addition to the fatigue strength tests, the surface layer properties were characterized on the basis of residual stresses, roughness, fracture pattern, microstructure and hardness. In addition, the redistribution of the residual stresses was considered for the interpretation of the fatigue strength results (compare [11] for shot peening and [12] for deep rolling).

## Materials and Methods

The fatigue strength of shafts with cross bores was investigated using analogy specimen to reduce the complexity of the setup. The geometry of the specimen is shown in Figure 1 (a). The specimen from EN-GJS-700-2 feature a cross bore with a diameter of $d_B$ = 5 mm in the center. The initial cross bore was manufactured by drilling and then reaming. A high-speed steel drill with a diameter of $d_{Drill}$ = 4.8 mm was used for drilling at a feed rate of $F_{Drill}$ = 100 mm·min⁻¹ and a rotational speed of $n_{Drill}$ = 1600 min⁻¹. A reamer made of high-speed steel with a diameter of 5H7 was used for reaming and operated at a feed rate of $F_{Ream}$ = 100 mm·min⁻¹ and a rotational speed of $n_{Ream}$ = 630 min⁻¹.

The cross bore was subsequently post-processed. A total of seven different variants were considered as part of the investigations. Two reference variants based on industrial applications were used. Furthermore, two shot-peened and two deep rolled variants were considered, as well as one variant that was first induction hardened and then deep rolled.

**Referent variants.** The reference variants considered were an untreated (REF) and an induction hardened variant (IND). The REF variant was not further post-processed. For the IND variant the shaft surface of the specimen was induction hardened to a hardness of $H_{IND}$ = 52-53 HRC with a hardness depth of $t_{IND}$ = 3 mm. The induction hardened area is shown in Figure 1 (a).

**Shot peened variants.** Two variants with different process parameter setups SP1 and SP2 were considered. The shot peening parameters as well as the fatigue results are described in the work of Reissner et al. and were used as comparison within this work especially by means of the different residual stress distribution after shot peening and deep rolling [11].

**Deep rolled variants.** Deep rolling was conducted by Ecoroll AG using a hydrostatic deep rolling tool with a tool diameter of $d_w$ = 2.38 mm. Two different deep rolling setups (DR1 and DR2) were considered. For DR1 a deep rolling pressure of $p_w$ = 400 bar and a feed of $f_w$ = 0.062 mm and for DR2 a deep rolling pressure of $p_w$ = 250 bar and a feed of $f_w$ = 0.054 mm were applied.

**Induction hardened and deep rolled variant.** The induction hardened and then deep rolled variant (IND + DR1) was first induction-hardened according to the IND variant and then deep-rolled according to DR1.
[10]. During deep rolling, the compressive residual stresses are typically lower in the feed direction than perpendicular to it. In both processes, the hardness and roughness are modified in addition to the residual stresses. The roughness after deep rolling is typically lower than after shot peening. This is due to the stochastic characteristics of shot peening compared to the deterministic deep rolling. The influence of shot peening of cross bores in shafts from EN-GJS-700-2 and 34CrNiMo6 +QT on the fatigue strength was investigated by Reissner et al [11]. In the work it was shown, that shot peening influences the slope of the S-N curve due to a redistribution of residual stresses. Transferring the results to deep rolled cross bores is not necessarily possible because the residual stress distribution and roughness characteristics are different from those after shot peening. Accordingly, the influence of the surface layer properties of especially deep rolled but also shot peened cross bores on the fatigue strength is insufficiently investigated. The aim of the work is therefore to determine the cause-effect relationships between the surface layer properties set by shot peening and deep rolling on the fatigue strength of shafts with cross bores. For this purpose, experimental fatigue strength tests were carried out on analogous specimens made of EN-GJS-700-2 with untreated, induction hardened, deep rolled, shot peened and induction hardened and subsequently deep rolled cross bores. In addition to the fatigue strength tests, the surface layer properties were characterized on the basis of residual stresses, roughness, fracture pattern, microstructure and hardness. In addition, the redistribution of the residual stresses was considered for the interpretation of the fatigue strength results (compare [11] for shot peening and [12] for deep rolling).

## Materials and Methods

The fatigue strength of shafts with cross bores was investigated using analogy specimen to reduce the complexity of the setup. The geometry of the specimen is shown in Figure 1 (a). The specimen from EN-GJS-700-2 feature a cross bore with a diameter of $d_B$ = 5 mm in the center. The initial cross bore was manufactured by drilling and then reaming. A high-speed steel drill with a diameter of $d_{Drill}$ = 4.8 mm was used for drilling at a feed rate of $F_{Drill}$ = 100 mm·min⁻¹ and a rotational speed of $n_{Drill}$ = 1600 min⁻¹. A reamer made of high-speed steel with a diameter of 5H7 was used for reaming and operated at a feed rate of $F_{Ream}$ = 100 mm·min⁻¹ and a rotational speed of $n_{Ream}$ = 630 min⁻¹.

The cross bore was subsequently post-processed. A total of seven different variants were considered as part of the investigations. Two reference variants based on industrial applications were used. Furthermore, two shot-peened and two deep rolled variants were considered, as well as one variant that was first induction hardened and then deep rolled.

**Referent variants.** The reference variants considered were an untreated (REF) and an induction hardened variant (IND). The REF variant was not further post-processed. For the IND variant the shaft surface of the specimen was induction hardened to a hardness of $H_{IND}$ = 52-53 HRC with a hardness depth of $t_{IND}$ = 3 mm. The induction hardened area is shown in Figure 1 (a).

**Shot peened variants.** Two variants with different process parameter setups SP1 and SP2 were considered. The shot peening parameters as well as the fatigue results are described in the work of Reissner et al. and were used as comparison within this work especially by means of the different residual stress distribution after shot peening and deep rolling [11].

**Deep rolled variants.** Deep rolling was conducted by Ecoroll AG using a hydrostatic deep rolling tool with a tool diameter of $d_w$ = 2.38 mm. Two different deep rolling setups (DR1 and DR2) were considered. For DR1 a deep rolling pressure of $p_w$ = 400 bar and a feed of $f_w$ = 0.062 mm and for DR2 a deep rolling pressure of $p_w$ = 250 bar and a feed of $f_w$ = 0.054 mm were applied.

**Induction hardened and deep rolled variant.** The induction hardened and then deep rolled variant (IND + DR1) was first induction-hardened according to the IND variant and then deep-rolled according to DR1.
<figure><img src="image_2608.png" /><figcaption>Figure 1: Geometry of specimen used (a) and experimental setup for torsion fatigue tests</figcaption></figure>

Torsional fatigue strength tests. The torsional fatigue strength of the specimen was tested on a FLATO of Schenk at the Institute for Materials Applications in Mechanical Engineering IWM of RWTH Aachen University (see Figure 1 (b)). The tests were carried out in the finite life fatigue area with a torque ratio of $R_T = -1$. The test frequency was $f = 25$ Hz and the maximum number of load cycles was $N_m = 5 \cdot 10^6$.

Analysis of surface layer properties. To understand the cause effect relationship between the surface treatments and the resulting fatigue strength the roughness, the microhardness, the fracture pattern and the residual stresses within the cross bore were considered. The roughness of the cross bore surface was determined using a Hommel-Etamic nanoscan 855 tactile contour and roughness measuring device from JENOPTIK Industrial Metrology Germany GmbH. Measurements were taken in three areas of the cross bore, namely in the area of the chamfer on both sides and in the center of the cross bore. Three measurements were carried out in each area for statistical assurance. The roughness measurements were evaluated in accordance with DIN EN ISO 4288. The evaluation was based on the mean roughness depth $Rz$ and the arithmetic mean roughness $Ra$. The microhardness test was carried out in accordance with DIN EN ISO 14577-1 on a Fischerscope HM2000 from Helmut Fischer and converted into Vickers hardness. To determine the microhardness in the cross bore, metallographic sections of the analogy specimens in the area of the cross bore were used. The test force was $F_{HM} = 490$ mN, which was applied over a period of $t = 20$ s. The holding time during the hardness test was $T_{hold} = 10$ s. The subsequent reduction in force also took place over a period of $t = 20$ s. The fracture patterns were analyzed using reflected light microscope images and scanning electron microscope (SEM) images. The residual stresses were recorded using X-ray diffraction (XRD) measurements with an Xstress G2R with a Mythen detector from Stresstech GmbH. The combination of XRD measurement, electrochemical etching with a Polimat from Buehler and depth measurements using a dial gauge with an alignment using a MarSurf GD 120 tactile feed device from Mahr was used to determine residual stress depth curves.

In addition to the residual stress measurements of the specimen as manufactured, the redistribution of the residual stresses due to cyclic loading was taken into account both experimentally and numerically (see [12]). In addition to the residual stresses, the load stress distribution due to the torsional load was taken into account using finite element (FE) simulations. The simulation model presented in Uhlmann et al. was used, although no residual stresses were considered, and the calculation was simplified using a purely elastic material model [12].