# Defect monitoring in wire arc additive manufacturing using frequency domain analysis

MATTERA Giulio<fnref n="1" />,ᵃ*, POLDEN Joseph<fnref n="2" />,ᵇ, CAGGIANO Alessandra<fnref n="1" />,ᶜ, VAN DUIN Stephen<fnref n="2" />,ᵈ NELE Luigi<fnref n="1" />,ᵉ and PAN Zengxi<fnref n="2" />,*f

**Keywords:** Additive Manufacturing, WAAM, Machine Learning, Defect Detection, Frequency Domain

**Abstract.** Efforts to integrate Wire Arc Additive Manufacturing (WAAM) into industrial settings drive a focus on refining in-process defect detection. WAAM commonly employs waveform-controlled welding techniques, like pulsed or controlled dip transfer processes, to enhance material properties and reduce heat input. The cyclic nature of voltage and current waveforms in these processes suggests that valuable information exists in their frequency content for assessing the process state and potential defects. This study introduces the employment of frequency domain analyses, utilizing Fast Fourier transform (FFT) and discrete wavelet transform (DWT) methodologies, to identify anomalies in welding signal data. Statistical assessments reveal the efficacy of online frequency domain analysis in extracting valuable insights across various WAAM processes. The research showcases the utility of this information in developing unsupervised learning techniques for online anomaly detection systems tailored to WAAM, proficient in identifying issues like arc instability, porosity, and geometrical defects caused by arc blow and humping.

## 1. Introduction

Wire Arc Additive Manufacturing (WAAM) (Fig. 1) utilizes an electric arc to melt wire feedstock, depositing layers to form near net-shape components (Norrish et al., 2021). Although promising for large and intricate metal parts, WAAM faces several defects like porosity, humping, arc blow, spatter, and lack of fusion (Wu et al., 2018). Anomaly detection in WAAM, crucial for defect identification (Reisch et al., 2020), aids in early detection, minimizing defective part production and material waste. Current WAAM anomaly detection tends to rely heavily on supervised learning (Cheepu, 2023; Li et al., 2022; Nele et al., 2022), demanding a well-labelled dataset. Unsupervised learning, as an alternative, eliminates this complex data labelling (Mattera, Polden, et al., 2023) and can detect anomalies by identifying patterns deviating from the expected behaviour, using solely normal data for training (Chandola et al., 2009; Omar et al., 2013)), thus simplifying the task in an operational industrial setting. While time domain signals offer a degree of information, frequency domain analysis emerges as a valuable tool for extracting a diverse range of information across different frequency bands, particularly crucial due to the close connection between welding waveform frequency content and droplet frequency. The latter, representing the rate of molten metal droplet transfer, is essential for monitoring and controlling welding processes to prevent defects in weld bead geometry and microstructure.

<figure><img src="image_55.png" /></figure>

<footnote n="1">¹University of Naples Federico II, Naples, Italy</footnote>

<footnote n="2">²University of Wollongong, Wollongong, NSW, Australia</footnote>

<footnote>ᵃgiulio.mattera@unina.it, ᵇjpolden@uow.edu.au, ᶜalessandra.caggiano@unina.it</footnote>

<footnote>ᵈsvanduin@uow.edu.au, ᶜnele@unina.it, ᶠzengxi@uow.edu.au</footnote>
# Defect monitoring in wire arc additive manufacturing using frequency domain analysis

MATTERA Giulio<fnref n="1" />,ᵃ*, POLDEN Joseph<fnref n="2" />,ᵇ, CAGGIANO Alessandra<fnref n="1" />,ᶜ, VAN DUIN Stephen<fnref n="2" />,ᵈ NELE Luigi<fnref n="1" />,ᵉ and PAN Zengxi<fnref n="2" />,*f

**Keywords:** Additive Manufacturing, WAAM, Machine Learning, Defect Detection, Frequency Domain

**Abstract.** Efforts to integrate Wire Arc Additive Manufacturing (WAAM) into industrial settings drive a focus on refining in-process defect detection. WAAM commonly employs waveform-controlled welding techniques, like pulsed or controlled dip transfer processes, to enhance material properties and reduce heat input. The cyclic nature of voltage and current waveforms in these processes suggests that valuable information exists in their frequency content for assessing the process state and potential defects. This study introduces the employment of frequency domain analyses, utilizing Fast Fourier transform (FFT) and discrete wavelet transform (DWT) methodologies, to identify anomalies in welding signal data. Statistical assessments reveal the efficacy of online frequency domain analysis in extracting valuable insights across various WAAM processes. The research showcases the utility of this information in developing unsupervised learning techniques for online anomaly detection systems tailored to WAAM, proficient in identifying issues like arc instability, porosity, and geometrical defects caused by arc blow and humping.

## 1. Introduction

Wire Arc Additive Manufacturing (WAAM) (Fig. 1) utilizes an electric arc to melt wire feedstock, depositing layers to form near net-shape components (Norrish et al., 2021). Although promising for large and intricate metal parts, WAAM faces several defects like porosity, humping, arc blow, spatter, and lack of fusion (Wu et al., 2018). Anomaly detection in WAAM, crucial for defect identification (Reisch et al., 2020), aids in early detection, minimizing defective part production and material waste. Current WAAM anomaly detection tends to rely heavily on supervised learning (Cheepu, 2023; Li et al., 2022; Nele et al., 2022), demanding a well-labelled dataset. Unsupervised learning, as an alternative, eliminates this complex data labelling (Mattera, Polden, et al., 2023) and can detect anomalies by identifying patterns deviating from the expected behaviour, using solely normal data for training (Chandola et al., 2009; Omar et al., 2013)), thus simplifying the task in an operational industrial setting. While time domain signals offer a degree of information, frequency domain analysis emerges as a valuable tool for extracting a diverse range of information across different frequency bands, particularly crucial due to the close connection between welding waveform frequency content and droplet frequency. The latter, representing the rate of molten metal droplet transfer, is essential for monitoring and controlling welding processes to prevent defects in weld bead geometry and microstructure.

<figure><img src="image_56.png" /></figure>

<footnote n="1">¹University of Naples Federico II, Naples, Italy</footnote>

<footnote n="2">²University of Wollongong, Wollongong, NSW, Australia</footnote>

<footnote>ᵃgiulio.mattera@unina.it, ᵇjpolden@uow.edu.au, ᶜalessandra.caggiano@unina.it</footnote>

<footnote>ᵈsvanduin@uow.edu.au, ᶜnele@unina.it, ᶠzengxi@uow.edu.au</footnote>
<figure><img src="image_56.png" /><figcaption>Figure 1: Overview of the WAAM system used in this work. Yaskawa MA2010 motion platform with Lincoln PowerWave S500CE welding power source.</figcaption></figure>

Despite its significance, literature on this subject remains limited and time domain features extraction represent the state of art in this field. Current research (Mattera et al., 2023) predominantly focuses on supervised learning and acoustic emission sensor applications, leaving a notable gap in exploring unsupervised learning within a multi-sensor framework that incorporates current and voltage signals (Ramalho et al., 2022; Zhang et al., 2023). This study introduces a novel method for online anomaly detection in WAAM using unsupervised learning and frequency domain analysis. In this work the frequency characteristics associated with diverse deposition processes, including Pulsed Gas Metal Arc Welding (P-GMAW) and Surface Tension Transfer (STT), across materials such as aluminium 4043 alloy, Inconel 718 alloy, mild steel ER70S6, and stainless steel 316L have been investigated. In particular, frequency analysis techniques like Fast Fourier Transform and Discrete Wavelet Decomposition have been explored to extract features from welding voltage and welding current signals. This study demonstrates the practicality of frequency domain analysis in evaluating WAAM process quality via unsupervised learning techniques for different processes and the applicability for different materials.

## 2. Frequency domain analysis of welding signals

Frequency domain analysis plays a crucial role in data processing and machine learning, serving purposes such as signal denoising and feature extraction from time series signals. Two widely utilized techniques for this analysis are the fast Fourier transform (FFT) and wavelet analysis. The FFT transforms a waveform from the time domain to the frequency domain, representing the signal in terms of its constituent sine wave frequencies. In contrast, wavelet analysis offers greater flexibility than the FFT. It can analyse signals with complex frequency content using various wavelet functions like Gaussian or Morlet wavelets, enabling analysis at different scales.

### 2.1 Fast Fourier Transform analysis.

The FFT stands as a powerful tool for signal analysis, facilitating the extraction of a signal's magnitude spectrum. For a welding current signal (Fig. 2), this spectrum unveils the amplitudes of sinusoidal components at different frequencies within the signal. When an intelligent agent learns the magnitudes linked to successful deposition and encounters variations in new data, it may indicate anomalies suggesting potential defects in the process. Statistical analysis of the FFT spectrum provides insights into the energy, frequency peak, and amplitude of the frequency response, enabling differentiation between normal and anomalous behaviour. Traditionally, this method is applied offline, involving the collection of all welding signal samples to construct a comprehensive frequency response at the end of the deposition.
<figure><img src="image_57.png" /><figcaption>Figure 1: Overview of the WAAM system used in this work. Yaskawa MA2010 motion platform with Lincoln PowerWave S500CE welding power source.</figcaption></figure>

Despite its significance, literature on this subject remains limited and time domain features extraction represent the state of art in this field. Current research (Mattera et al., 2023) predominantly focuses on supervised learning and acoustic emission sensor applications, leaving a notable gap in exploring unsupervised learning within a multi-sensor framework that incorporates current and voltage signals (Ramalho et al., 2022; Zhang et al., 2023). This study introduces a novel method for online anomaly detection in WAAM using unsupervised learning and frequency domain analysis. In this work the frequency characteristics associated with diverse deposition processes, including Pulsed Gas Metal Arc Welding (P-GMAW) and Surface Tension Transfer (STT), across materials such as aluminium 4043 alloy, Inconel 718 alloy, mild steel ER70S6, and stainless steel 316L have been investigated. In particular, frequency analysis techniques like Fast Fourier Transform and Discrete Wavelet Decomposition have been explored to extract features from welding voltage and welding current signals. This study demonstrates the practicality of frequency domain analysis in evaluating WAAM process quality via unsupervised learning techniques for different processes and the applicability for different materials.

## 2. Frequency domain analysis of welding signals

Frequency domain analysis plays a crucial role in data processing and machine learning, serving purposes such as signal denoising and feature extraction from time series signals. Two widely utilized techniques for this analysis are the fast Fourier transform (FFT) and wavelet analysis. The FFT transforms a waveform from the time domain to the frequency domain, representing the signal in terms of its constituent sine wave frequencies. In contrast, wavelet analysis offers greater flexibility than the FFT. It can analyse signals with complex frequency content using various wavelet functions like Gaussian or Morlet wavelets, enabling analysis at different scales.

### 2.1 Fast Fourier Transform analysis.

The FFT stands as a powerful tool for signal analysis, facilitating the extraction of a signal's magnitude spectrum. For a welding current signal (Fig. 2), this spectrum unveils the amplitudes of sinusoidal components at different frequencies within the signal. When an intelligent agent learns the magnitudes linked to successful deposition and encounters variations in new data, it may indicate anomalies suggesting potential defects in the process. Statistical analysis of the FFT spectrum provides insights into the energy, frequency peak, and amplitude of the frequency response, enabling differentiation between normal and anomalous behaviour. Traditionally, this method is applied offline, involving the collection of all welding signal samples to construct a comprehensive frequency response at the end of the deposition.
<figure><img src="image_57.png" /><figcaption>Figure 2: The output of a FFT on a welding current signal for a normal deposition (a) and a deposition with anomalies (b) of a pulsed-GMAW process.</figcaption></figure>

However, due to the lack of temporal information, this approach may struggle to localize defects, and rapid frequency changes associated with defect formation may not be immediately evident in the overall deposition. In such cases, more intricate techniques, like the wavelet transform, can be employed with the same concept but yielding superior results by adapting better and extracting more information from complex signals that vary in frequency response within time.

## 2.2 Discrete Wavelet Transform analysis.

Although the FFT is applicable for analysing the frequency content of signals, it might not effectively detect minor variations in frequency content, e.g. as it happens when signals display defects. Hence, in cases of varying frequency content in signals, the Discrete Wavelet Transform (DWT) is a more appropriate choice. As shown in Fig. 3, by applying a DWT with a pre-defined wavelet shape, details coefficients of each decomposition may be obtained and features can be derived from them, such as the standard deviation of different decomposition levels, and these features can be used for further analysis.

<figure><img src="image_58.png" /><figcaption>Figure 3: DWT of the input signal allows to decompose it in several detail and approximation coefficients which, as for the results of a FFT, may be used to identify anomalous behaviour.</figcaption></figure>
<figure><img src="image_58.png" /><figcaption>Figure 2: The output of a FFT on a welding current signal for a normal deposition (a) and a deposition with anomalies (b) of a pulsed-GMAW process.</figcaption></figure>

However, due to the lack of temporal information, this approach may struggle to localize defects, and rapid frequency changes associated with defect formation may not be immediately evident in the overall deposition. In such cases, more intricate techniques, like the wavelet transform, can be employed with the same concept but yielding superior results by adapting better and extracting more information from complex signals that vary in frequency response within time.

## 2.2 Discrete Wavelet Transform analysis.

Although the FFT is applicable for analysing the frequency content of signals, it might not effectively detect minor variations in frequency content, e.g. as it happens when signals display defects. Hence, in cases of varying frequency content in signals, the Discrete Wavelet Transform (DWT) is a more appropriate choice. As shown in Fig. 3, by applying a DWT with a pre-defined wavelet shape, details coefficients of each decomposition may be obtained and features can be derived from them, such as the standard deviation of different decomposition levels, and these features can be used for further analysis.

<figure><img src="image_59.png" /><figcaption>Figure 3: DWT of the input signal allows to decompose it in several detail and approximation coefficients which, as for the results of a FFT, may be used to identify anomalous behaviour.</figcaption></figure>
# 3. Experiments and Selected Results

## 3.1. Setup and Data collection

An experimental study was conducted to demonstrate the utility of the presented frequency-based analysis methods to extract features that allow to differentiate normal and anormal behaviour during the deposition process. In these experiments, single-bead walls (100 mm in length), were deposited in a layer-by-layer fashion via the conventional robotic welding system. A number of different consumables were used, each coupled with suitable welding processes and associated parameters, which are outlined in Table 1. Despite multiple layers being deposited during the experimental campaign, this work primarily centres on presenting results by comparing layers deposited under standard operating conditions with layers that displayed defects. These defects encompassed issues such as humping, variations in geometry, porosity and arc instability during the deposition process, as shown in Fig. 4. Two different processes have been analysed in this work. Pulsed GMAW is used for the deposition of Aluminium 4043, Inconel 718 and Stainless steel 316L, while STT is employed for mild-steel ER70S-6. In the P-GMAW process, the power source alternates between high and low currents during each welding cycle. This pulsing action provides better control over the weld pool and reduces heat input, minimizing the risk of overheating and distortion. On other hand, STT is a modified short-circuit transfer process developed by Lincoln that utilizes the surface tension of the molten metal to control the transfer of droplets. Both techniques are employed in WAAM thanks to the lower heat input, which reduce final component's distortion and residual stress. (Pan et al., 2018)

<table><caption>Table 1: Experimental campaign conducted in this work.</caption><thead><tr><th>Material</th><th>Welding process</th><th>WS [mm/min]</th><th>WFS [m/min]</th><th>CTWD [mm]</th><th>Quality Observations</th></tr></thead><tbody><tr><td rowspan="2">Aluminium 4043</td><td rowspan="2">P-GMAW</td><td>380</td><td>7</td><td>12</td><td>Normal</td></tr><tr><td>400</td><td>7</td><td>12</td><td>Geometry defect</td></tr><tr><td rowspan="2">Inconel 718</td><td rowspan="2">P-GMAW</td><td>600</td><td>8.5</td><td>15</td><td>Normal</td></tr><tr><td>600</td><td>8.5</td><td>10</td><td>Instability</td></tr><tr><td rowspan="2">316L</td><td rowspan="2">P-GMAW</td><td>750</td><td>2</td><td>20</td><td>Normal</td></tr><tr><td>750</td><td>2</td><td>20</td><td>Humping</td></tr><tr><td rowspan="2">ER70S-6</td><td rowspan="2">STT</td><td>450</td><td>4.5</td><td>25</td><td>Normal</td></tr><tr><td>450</td><td>4.5</td><td>30</td><td>Porosity and instabilities</td></tr></tbody></table>

<figure><img src="image_59.png" /><figcaption>Figure 4: Detected defects. A: 4043 Aluminium i) normal deposition, ii) undesired geometry defect; B: stainless steel 316L i) normal deposition, ii) humping defect; C: ER70S6 steel i) normal deposition, ii) porosity defect.</figcaption></figure>
# 3. Experiments and Selected Results

## 3.1. Setup and Data collection

An experimental study was conducted to demonstrate the utility of the presented frequency-based analysis methods to extract features that allow to differentiate normal and anormal behaviour during the deposition process. In these experiments, single-bead walls (100 mm in length), were deposited in a layer-by-layer fashion via the conventional robotic welding system. A number of different consumables were used, each coupled with suitable welding processes and associated parameters, which are outlined in Table 1. Despite multiple layers being deposited during the experimental campaign, this work primarily centres on presenting results by comparing layers deposited under standard operating conditions with layers that displayed defects. These defects encompassed issues such as humping, variations in geometry, porosity and arc instability during the deposition process, as shown in Fig. 4. Two different processes have been analysed in this work. Pulsed GMAW is used for the deposition of Aluminium 4043, Inconel 718 and Stainless steel 316L, while STT is employed for mild-steel ER70S-6. In the P-GMAW process, the power source alternates between high and low currents during each welding cycle. This pulsing action provides better control over the weld pool and reduces heat input, minimizing the risk of overheating and distortion. On other hand, STT is a modified short-circuit transfer process developed by Lincoln that utilizes the surface tension of the molten metal to control the transfer of droplets. Both techniques are employed in WAAM thanks to the lower heat input, which reduce final component's distortion and residual stress. (Pan et al., 2018)

<table><caption>Table 1: Experimental campaign conducted in this work.</caption><thead><tr><th>Material</th><th>Welding process</th><th>WS [mm/min]</th><th>WFS [m/min]</th><th>CTWD [mm]</th><th>Quality Observations</th></tr></thead><tbody><tr><td rowspan="2">Aluminium 4043</td><td rowspan="2">P-GMAW</td><td>380</td><td>7</td><td>12</td><td>Normal</td></tr><tr><td>400</td><td>7</td><td>12</td><td>Geometry defect</td></tr><tr><td rowspan="2">Inconel 718</td><td rowspan="2">P-GMAW</td><td>600</td><td>8.5</td><td>15</td><td>Normal</td></tr><tr><td>600</td><td>8.5</td><td>10</td><td>Instability</td></tr><tr><td rowspan="2">316L</td><td rowspan="2">P-GMAW</td><td>750</td><td>2</td><td>20</td><td>Normal</td></tr><tr><td>750</td><td>2</td><td>20</td><td>Humping</td></tr><tr><td rowspan="2">ER70S-6</td><td rowspan="2">STT</td><td>450</td><td>4.5</td><td>25</td><td>Normal</td></tr><tr><td>450</td><td>4.5</td><td>30</td><td>Porosity and instabilities</td></tr></tbody></table>

<figure><img src="image_60.png" /><figcaption>Figure 4: Detected defects. A: 4043 Aluminium i) normal deposition, ii) undesired geometry defect; B: stainless steel 316L i) normal deposition, ii) humping defect; C: ER70S6 steel i) normal deposition, ii) porosity defect.</figcaption></figure>
## 3.2. Data processing

The gathered data, which includes welding current and welding voltage, was simultaneously recorded, and both spectra were analysed. To develop an online anomaly detection, the data was divided into 1-second intervals, with each interval consisting of 5000 samples due to a sampling frequency of 5kHz. A normalization process was applied to the data. Notably, the maximum and minimum values obtained during the normalization of the normal signal were used to scale the signals of anomalous conditions. From this dataset, 20 features were extracted per second using both FFT and a 3-level DWT with a third-order Gaussian wavelet. Specifically, peak amplitude (PA) and corresponding frequency (PF) were extracted from both voltage and current signals using FFT. Additionally, two features were extracted from each decomposition level in the DWT: Standard Deviation per Level, offering insights into the distribution of wavelet coefficients across various decompositions, and Average Energy of each level, providing a measure of the overall importance of that component in the signal. In total, 20 features were extracted, see Fig. 5.

<figure><img src="image_60.png" /><figcaption>Figure 5: Proposed statistical methodology to extract frequency domain features from welding current and welding voltage signals.</figcaption></figure>

## 3.3. Results

For visualization purposes, Principal Component Analysis (PCA) was utilized to reduce the dimensionality of normal samples to four principal components. The use of a 3D-scatter plot enables the assessment of the effective discrimination between normal and anomalous data based on the extracted features. Upon observing Figs. 6, 7, 8, and 9, it becomes apparent that the extracted features, represented through their principal components, allow for the division of the feature space. This division facilitates the distinction between portions of the deposition associated with the normal layer (depicted as green spots) and certain portions of the other layer exhibiting defects in some areas. It is evident that by employing a machine learning algorithm, such as the Isolation Forest, the feature space of normal deposition can be learned. Furthermore, using the extracted features from each second of deposition, it becomes possible to detect various types of anomalies in different materials.
## 3.2. Data processing

The gathered data, which includes welding current and welding voltage, was simultaneously recorded, and both spectra were analysed. To develop an online anomaly detection, the data was divided into 1-second intervals, with each interval consisting of 5000 samples due to a sampling frequency of 5kHz. A normalization process was applied to the data. Notably, the maximum and minimum values obtained during the normalization of the normal signal were used to scale the signals of anomalous conditions. From this dataset, 20 features were extracted per second using both FFT and a 3-level DWT with a third-order Gaussian wavelet. Specifically, peak amplitude (PA) and corresponding frequency (PF) were extracted from both voltage and current signals using FFT. Additionally, two features were extracted from each decomposition level in the DWT: Standard Deviation per Level, offering insights into the distribution of wavelet coefficients across various decompositions, and Average Energy of each level, providing a measure of the overall importance of that component in the signal. In total, 20 features were extracted, see Fig. 5.

<figure><img src="image_61.png" /><figcaption>Figure 5: Proposed statistical methodology to extract frequency domain features from welding current and welding voltage signals.</figcaption></figure>

## 3.3. Results

For visualization purposes, Principal Component Analysis (PCA) was utilized to reduce the dimensionality of normal samples to four principal components. The use of a 3D-scatter plot enables the assessment of the effective discrimination between normal and anomalous data based on the extracted features. Upon observing Figs. 6, 7, 8, and 9, it becomes apparent that the extracted features, represented through their principal components, allow for the division of the feature space. This division facilitates the distinction between portions of the deposition associated with the normal layer (depicted as green spots) and certain portions of the other layer exhibiting defects in some areas. It is evident that by employing a machine learning algorithm, such as the Isolation Forest, the feature space of normal deposition can be learned. Furthermore, using the extracted features from each second of deposition, it becomes possible to detect various types of anomalies in different materials.
<figure><img src="image_61.png" /><figcaption>Figure 6: Scatter plots of the 4 principal components which demonstrated that the extracted frequency domain features may be used to distinguish normal deposition from humping in pulsed GMAW of 316L.</figcaption></figure>

<figure><img src="image_62.png" /><figcaption>Figure 7: Scatter plots of the 4 principal components of extracted frequency features during a deposition via pulsed GMAW process of aluminium 4043 of a normal layer and a layer subject to a geometry defect due to arc blow.</figcaption></figure>
<figure><img src="image_62.png" /><figcaption>Figure 6: Scatter plots of the 4 principal components which demonstrated that the extracted frequency domain features may be used to distinguish normal deposition from humping in pulsed GMAW of 316L.</figcaption></figure>

<figure><img src="image_63.png" /><figcaption>Figure 7: Scatter plots of the 4 principal components of extracted frequency features during a deposition via pulsed GMAW process of aluminium 4043 of a normal layer and a layer subject to a geometry defect due to arc blow.</figcaption></figure>
<figure><img src="image_63.png" /><figcaption>Figure 8: Scatter plots of the 4 principal components of extracted frequency domain features demonstrating that unsupervised learning may be applied to distinguish normal data from anomalous data like spatter in Inconel 825.</figcaption></figure>

<figure><img src="image_64.png" /><figcaption>Figure 9: Scatter plots of the 4 principal components of extracted frequency domain features for a normal deposition via Surface Tension Transfer process of ER70S-6 and of a layer presenting defects such as porosity and instabilities.</figcaption></figure>
<figure><img src="image_64.png" /><figcaption>Figure 8: Scatter plots of the 4 principal components of extracted frequency domain features demonstrating that unsupervised learning may be applied to distinguish normal data from anomalous data like spatter in Inconel 825.</figcaption></figure>

<figure><img src="image_65.png" /><figcaption>Figure 9: Scatter plots of the 4 principal components of extracted frequency domain features for a normal deposition via Surface Tension Transfer process of ER70S-6 and of a layer presenting defects such as porosity and instabilities.</figcaption></figure>
## 4. Limitations and future opportunities

The methodology presented in this study utilizes time series and their frequency analysis to identify anomalies. While the scatter plot demonstrates the feasibility of employing this technique for developing an application capable of detecting defects, the manual extraction of features poses a challenge in determining which features are pertinent for distinguishing between a successful deposition and an anomalous one. To address this challenge, a more intuitive and informative approach can be adopted by incorporating additional information through time-frequency analysis. The Continuous Wavelet Transform, for instance, allows the generation of spectrograms that provide insights into the frequency content of different wavelets over time. The wealth of information derived from these analyses can be converted into images and further analyzed using image processing techniques. Numerous features can be extracted using various filters, or automatic features can be obtained through deep learning techniques such as autoencoders. This application of image processing opens up new frontiers for signal analysis, proving to be particularly powerful due to the evident differences between spectrograms associated with normal and anomalous conditions, as illustrated in Figs. 10 and 11 for ER70-6 obtained by STT process and Inconel 718 via P-GMAW. These techniques can be employed not only for online defect detection but also for localizing anomalies in time at the end of the deposition, as time information is present in the final output. Furthermore, an in-depth analysis of the hidden pattern in anomaly clusters can be used to develop semi-supervised classifier.

<figure><img src="image_65.png" /><figcaption>Figure 10: Scalogram obtained from current signal using a Morlet CWT of (a) normal deposition and (b) anomalous deposition with porosity of ER70S6 via STT process.</figcaption></figure>

<figure><img src="image_66.png" /><figcaption>Figure 11: Scalogram obtained from current signal using a Morlet CWT of (a) normal deposition and (b) anomalous deposition with spatter and arc instability of Inconel 825 via pulsed-GMAW process.</figcaption></figure>

These techniques can be employed not only for online defect detection but also for localizing anomalies in time at the end of the deposition, as time information is present in the final output.
## 4. Limitations and future opportunities

The methodology presented in this study utilizes time series and their frequency analysis to identify anomalies. While the scatter plot demonstrates the feasibility of employing this technique for developing an application capable of detecting defects, the manual extraction of features poses a challenge in determining which features are pertinent for distinguishing between a successful deposition and an anomalous one. To address this challenge, a more intuitive and informative approach can be adopted by incorporating additional information through time-frequency analysis. The Continuous Wavelet Transform, for instance, allows the generation of spectrograms that provide insights into the frequency content of different wavelets over time. The wealth of information derived from these analyses can be converted into images and further analyzed using image processing techniques. Numerous features can be extracted using various filters, or automatic features can be obtained through deep learning techniques such as autoencoders. This application of image processing opens up new frontiers for signal analysis, proving to be particularly powerful due to the evident differences between spectrograms associated with normal and anomalous conditions, as illustrated in Figs. 10 and 11 for ER70-6 obtained by STT process and Inconel 718 via P-GMAW. These techniques can be employed not only for online defect detection but also for localizing anomalies in time at the end of the deposition, as time information is present in the final output. Furthermore, an in-depth analysis of the hidden pattern in anomaly clusters can be used to develop semi-supervised classifier.

<figure><img src="image_66.png" /><figcaption>Figure 10: Scalogram obtained from current signal using a Morlet CWT of (a) normal deposition and (b) anomalous deposition with porosity of ER70S6 via STT process.</figcaption></figure>

<figure><img src="image_67.png" /><figcaption>Figure 11: Scalogram obtained from current signal using a Morlet CWT of (a) normal deposition and (b) anomalous deposition with spatter and arc instability of Inconel 825 via pulsed-GMAW process.</figcaption></figure>

These techniques can be employed not only for online defect detection but also for localizing anomalies in time at the end of the deposition, as time information is present in the final output.
Furthermore, an in-depth analysis of the hidden pattern in anomaly clusters can be used to develop semi-supervised classifier.

## Conclusion

Identifying process anomalies in WAAM applications is crucial for enabling timely intervention in potential repairs. Anomaly detection approaches in this technology area typically rely on supervised machine learning techniques. While supervised techniques exhibit high performance, unsupervised methods offer a reduction of costs related to defect generation and post-processing activities for label generation. However, unsupervised techniques introduce higher uncertainty in output generation, leading to lower performance. This study introduces a method for feature extraction from frequency domain analysis of welding voltage and welding current signals. Utilizing a multi-sensor monitoring system that is less susceptible to external factors, both FFT and DWT are applied for online feature extraction in the frequency domain in different frequency bandwidths. These features, extracted at one-second intervals, proved to effectively distinguish between defect-free and anomalous deposition. The technique successfully identifies defects such as humping, spatter, geometric irregularities due to arc blow, and porosity in materials such as Inconel 825, Aluminium 4043, mild steel ER70S, and stainless steel 316L, showing that this approach can be used for different welding waveform which depends by both process and materials employed. However, limitations associated with manual feature extraction may impact the ultimate performance of the unsupervised algorithm, which serves as the initial layer of a defect detection module in an intelligent production system. To address this, future developments will explore the integration of image processing techniques and a comparison with more advanced time-frequency domain analyses to enhance the anomaly detection capabilities.

## Acknowledgment

The INVITALIA Project on *New Engineering & Manufacturing Enhanced System Innovation (NEMESI)* and POR Campania FESR 2014/2020 Project on *Tecnologie One Piece (TOP)* are gratefully acknowledged for their support to this research work.

## References

[1] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection. *ACM Computing Surveys*, 41(3), 1–58. https://doi.org/10.1145/1541880.1541882

[2] Cheepu, M. (2023). Machine Learning Approach for the Prediction of Defect Characteristics in Wire Arc Additive Manufacturing. *Transactions of the Indian Institute of Metals*, 76(2), 447–455. https://doi.org/10.1007/s12666-022-02715-1

[3] Li, Y., Polden, J., Pan, Z., Cui, J., Xia, C., He, F., Mu, H., Li, H., & Wang, L. (2022). A defect detection system for wire arc additive manufacturing using incremental learning. *Journal of Industrial Information Integration*, 27, 100291. https://doi.org/10.1016/j.jii.2021.100291

[4] Mattera, G., Nele, L., & Paolella, D. (2023). Monitoring and control the Wire Arc Additive Manufacturing process using artificial intelligence techniques: a review. *Journal of Intelligent Manufacturing*. https://doi.org/10.1007/s10845-023-02085-5

[5] Mattera, G., Polden, J., Caggiano, A., Commins, P., Nele, L., & Pan, Z. (2023). Anomaly Detection of Wire Arc Additively Manufactured Parts via Surface Tension Transfer through Unsupervised Machine Learning Techniques. *17th CIRP Conference on Intelligent Computation in Manufacturing Engineering*.

[6] Nele, L., Mattera, G., & Vozza, M. (2022). Deep Neural Networks for Defects Detection in Gas Metal Arc Welding. *Applied Sciences* (Switzerland), 12(7). https://doi.org/10.3390/app12073615
Furthermore, an in-depth analysis of the hidden pattern in anomaly clusters can be used to develop semi-supervised classifier.

## Conclusion

Identifying process anomalies in WAAM applications is crucial for enabling timely intervention in potential repairs. Anomaly detection approaches in this technology area typically rely on supervised machine learning techniques. While supervised techniques exhibit high performance, unsupervised methods offer a reduction of costs related to defect generation and post-processing activities for label generation. However, unsupervised techniques introduce higher uncertainty in output generation, leading to lower performance. This study introduces a method for feature extraction from frequency domain analysis of welding voltage and welding current signals. Utilizing a multi-sensor monitoring system that is less susceptible to external factors, both FFT and DWT are applied for online feature extraction in the frequency domain in different frequency bandwidths. These features, extracted at one-second intervals, proved to effectively distinguish between defect-free and anomalous deposition. The technique successfully identifies defects such as humping, spatter, geometric irregularities due to arc blow, and porosity in materials such as Inconel 825, Aluminium 4043, mild steel ER70S, and stainless steel 316L, showing that this approach can be used for different welding waveform which depends by both process and materials employed. However, limitations associated with manual feature extraction may impact the ultimate performance of the unsupervised algorithm, which serves as the initial layer of a defect detection module in an intelligent production system. To address this, future developments will explore the integration of image processing techniques and a comparison with more advanced time-frequency domain analyses to enhance the anomaly detection capabilities.

## Acknowledgment

The INVITALIA Project on *New Engineering & Manufacturing Enhanced System Innovation (NEMESI)* and POR Campania FESR 2014/2020 Project on *Tecnologie One Piece (TOP)* are gratefully acknowledged for their support to this research work.

## References

[1] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection. *ACM Computing Surveys*, 41(3), 1–58. https://doi.org/10.1145/1541880.1541882

[2] Cheepu, M. (2023). Machine Learning Approach for the Prediction of Defect Characteristics in Wire Arc Additive Manufacturing. *Transactions of the Indian Institute of Metals*, 76(2), 447–455. https://doi.org/10.1007/s12666-022-02715-1

[3] Li, Y., Polden, J., Pan, Z., Cui, J., Xia, C., He, F., Mu, H., Li, H., & Wang, L. (2022). A defect detection system for wire arc additive manufacturing using incremental learning. *Journal of Industrial Information Integration*, 27, 100291. https://doi.org/10.1016/j.jii.2021.100291

[4] Mattera, G., Nele, L., & Paolella, D. (2023). Monitoring and control the Wire Arc Additive Manufacturing process using artificial intelligence techniques: a review. *Journal of Intelligent Manufacturing*. https://doi.org/10.1007/s10845-023-02085-5

[5] Mattera, G., Polden, J., Caggiano, A., Commins, P., Nele, L., & Pan, Z. (2023). Anomaly Detection of Wire Arc Additively Manufactured Parts via Surface Tension Transfer through Unsupervised Machine Learning Techniques. *17th CIRP Conference on Intelligent Computation in Manufacturing Engineering*.

[6] Nele, L., Mattera, G., & Vozza, M. (2022). Deep Neural Networks for Defects Detection in Gas Metal Arc Welding. *Applied Sciences* (Switzerland), 12(7). https://doi.org/10.3390/app12073615
[7] Norrish, J., Polden, J., & Richardson, I. (2021). A review of wire arc additive manufacturing: development, principles, process physics, implementation and current status. *Journal of Physics D: Applied Physics*, 54(47), 473001. https://doi.org/10.1088/1361-6463/ac1e4a

[8] Omar, S., Ngadi, A., & H. Jebur, H. (2013). Machine Learning Techniques for Anomaly Detection: An Overview. *International Journal of Computer Applications*, 79(2), 33-41. https://doi.org/10.5120/13715-1478

[9] Pan, Z., Ding, D., Wu, B., Cuiuri, D., Li, H., & Norrish, J. (2018). *Arc Welding Processes for Additive Manufacturing: A Review* (pp. 3-24). https://doi.org/10.1007/978-981-10-5355-9_1

[10] Ramalho, A., Santos, T. G., Bevans, B., Smoqi, Z., Rao, P., & Oliveira, J. P. (2022). Effect of contaminations on the acoustic emissions during wire and arc additive manufacturing of 316L stainless steel. *Additive Manufacturing*, 51, 102585. https://doi.org/10.1016/j.addma.2021.102585

[11] Reisch, R., Hauser, T., Lutz, B., Pantano, M., Kamps, T., & Knoll, A. (2020). Distance-Based Multivariate Anomaly Detection in Wire Arc Additive Manufacturing. *2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)*, 659-664. https://doi.org/10.1109/ICMLA51294.2020.00109

[12] Wu, B., Pan, Z., Ding, D., Cuiuri, D., Li, H., Xu, J., & Norrish, J. (2018). A review of the wire arc additive manufacturing of metals: properties, defects and quality improvement. *Journal of Manufacturing Processes*, 35, 127-139.

[13] Zhang, T., Xu, C., Cheng, J., Chen, Z., Wang, L., & Wang, K. (2023). Research of surface oxidation defects in copper alloy wire arc additive manufacturing based on time-frequency analysis and deep learning method. *Journal of Materials Research and Technology*, 25, 511-521. https://doi.org/10.1016/j.jmrt.2023.05.227
[7] Norrish, J., Polden, J., & Richardson, I. (2021). A review of wire arc additive manufacturing: development, principles, process physics, implementation and current status. *Journal of Physics D: Applied Physics*, 54(47), 473001. https://doi.org/10.1088/1361-6463/ac1e4a

[8] Omar, S., Ngadi, A., & H. Jebur, H. (2013). Machine Learning Techniques for Anomaly Detection: An Overview. *International Journal of Computer Applications*, 79(2), 33–41.
https://doi.org/10.5120/13715-1478

[9] Pan, Z., Ding, D., Wu, B., Cuiuri, D., Li, H., & Norrish, J. (2018). *Arc Welding Processes for Additive Manufacturing: A Review* (pp. 3–24). https://doi.org/10.1007/978-981-10-5355-9_1

[10] Ramalho, A., Santos, T. G., Bevans, B., Smoqi, Z., Rao, P., & Oliveira, J. P. (2022). Effect of contaminations on the acoustic emissions during wire and arc additive manufacturing of 316L stainless steel. *Additive Manufacturing*, 51, 102585. https://doi.org/10.1016/j.addma.2021.102585

[11] Reisch, R., Hauser, T., Lutz, B., Pantano, M., Kamps, T., & Knoll, A. (2020). Distance-Based Multivariate Anomaly Detection in Wire Arc Additive Manufacturing. *2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)*, 659–664.
https://doi.org/10.1109/ICMLA51294.2020.00109

[12] Wu, B., Pan, Z., Ding, D., Cuiuri, D., Li, H., Xu, J., & Norrish, J. (2018). A review of the wire arc additive manufacturing of metals: properties, defects and quality improvement. *Journal of Manufacturing Processes*, 35, 127–139.

[13] Zhang, T., Xu, C., Cheng, J., Chen, Z., Wang, L., & Wang, K. (2023). Research of surface oxidation defects in copper alloy wire arc additive manufacturing based on time-frequency analysis and deep learning method. *Journal of Materials Research and Technology*, 25, 511–521.
https://doi.org/10.1016/j.jmrt.2023.05.227
# On the scalability of strength and presumed size effects in SLM-produced 316L stainless steel

VYSOCHINSKIY Dmitry<fnref n="1" />, a*, MOE Ole-Bjørn<fnref n="2" />

**Keywords:** Powder Bed Fusion, Selective Laser Melting, 316L, Metal Additive Manufacturing, Mechanical Properties, L-PBF

**Abstract.** As additive manufacturing (AM) technology advances toward the production of structurally utilized metallic components, an accurate evaluation of mechanical properties becomes crucial. Understanding the effect of the printed cross-section size on material strength is exceptionally important since AM technology, especially laser powder bed fusion (L-PBF), enables topology optimization and favors the production of thin struts, lattice structures, and other small cross-sections, as well as thin-walled profiles. Structural designers need to know to what degree the size of the printed cross-section might affect material strength. This study focuses on the effect of printed cross-section size on the mechanical properties of L-PBF-produced 316L stainless steel. The findings reported in existing literature so far are conflicting, indicating a need for further investigation. In the presented paper, the mechanical properties of net-shape printed samples with cross-section diameters ranging from 2.5 mm to 6.0 mm are compared. Tensile tests reveal that smaller diameter net-shape samples exhibit lower strength compared to larger diameters. Machined samples display higher strength than net-shape samples. The observed size-dependent strength variations in L-PBF-produced 316L stainless steel can be explained by the surface layer being weaker than the core of the sample. Whether the effect is due to differences in microstructure between the surface and core of the sample or is simply caused by a reduction of the effective cross-sectional area due to surface roughness could be debated, but both factors are likely to contribute to the phenomenon.

**Introduction**

As additive manufacturing technology progresses beyond modeling and rapid prototyping towards the production of structurally utilized parts, challenges related to the accurate evaluation of mechanical properties of AM-produced materials become more pronounced. When it comes to additive manufacturing of metallic materials, two common techniques allowing the production of essentially fully dense parts are Directed Energy Deposition (DED) and Powder Bed Fusion (PBF) using standard terminology [1]. The designation Laser-Powder Bed Fusion (L-PBF) is also commonly applied to distinguish processes where a laser is used as a power source, such as Selective Laser Melting (SLM) and Selective Laser Sintering (SLS), from processes where an electron beam is used as a power source, designated as Electron Beam Powder Bed Fusion (EBPBF). While DED has a melt pool is about 0.5-1.0 mm in diameter [2], the typical melt pool size for L-PBF is about 0.2-0.4 mm [3], allowing the printing of fine geometrical details and favoring the use of thin-walled and lattice structures to save weight. This underscores the importance of comprehending the effects of cross-section size on the mechanical properties of the produced printed material, especially in L-PBF processes.

Another issue tied to understanding the effect of printed cross-section size on mechanical properties is the need to comprehend how the rough surface produced by the L-PBF process affects

<figure><img src="image_67.png" /></figure>

<footnote n="1">¹Jon Lilletuns vei 9, 4879, Grimstad, University of Agder, Norway</footnote>

<footnote n="2">²Jon Lilletuns vei 9, 4879, Grimstad, Mechatronics Innovation Lab, Norway</footnote>

<footnote>ªdmitry.vysochinskiy@uia.no</footnote>
# On the scalability of strength and presumed size effects in SLM-produced 316L stainless steel

VYSOCHINSKIY Dmitry<fnref n="1" />,ᵃ*, MOE Ole-Bjørn<fnref n="2" />

**Keywords:** Powder Bed Fusion, Selective Laser Melting, 316L, Metal Additive Manufacturing, Mechanical Properties, L-PBF

**Abstract.** As additive manufacturing (AM) technology advances toward the production of structurally utilized metallic components, an accurate evaluation of mechanical properties becomes crucial. Understanding the effect of the printed cross-section size on material strength is exceptionally important since AM technology, especially laser powder bed fusion (L-PBF), enables topology optimization and favors the production of thin struts, lattice structures, and other small cross-sections, as well as thin-walled profiles. Structural designers need to know to what degree the size of the printed cross-section might affect material strength. This study focuses on the effect of printed cross-section size on the mechanical properties of L-PBF-produced 316L stainless steel. The findings reported in existing literature so far are conflicting, indicating a need for further investigation. In the presented paper, the mechanical properties of net-shape printed samples with cross-section diameters ranging from 2.5 mm to 6.0 mm are compared. Tensile tests reveal that smaller diameter net-shape samples exhibit lower strength compared to larger diameters. Machined samples display higher strength than net-shape samples. The observed size-dependent strength variations in L-PBF-produced 316L stainless steel can be explained by the surface layer being weaker than the core of the sample. Whether the effect is due to differences in microstructure between the surface and core of the sample or is simply caused by a reduction of the effective cross-sectional area due to surface roughness could be debated, but both factors are likely to contribute to the phenomenon.

**Introduction**

As additive manufacturing technology progresses beyond modeling and rapid prototyping towards the production of structurally utilized parts, challenges related to the accurate evaluation of mechanical properties of AM-produced materials become more pronounced. When it comes to additive manufacturing of metallic materials, two common techniques allowing the production of essentially fully dense parts are Directed Energy Deposition (DED) and Powder Bed Fusion (PBF) using standard terminology [1]. The designation Laser-Powder Bed Fusion (L-PBF) is also commonly applied to distinguish processes where a laser is used as a power source, such as Selective Laser Melting (SLM) and Selective Laser Sintering (SLS), from processes where an electron beam is used as a power source, designated as Electron Beam Powder Bed Fusion (EBPBF). While DED has a melt pool is about 0.5-1.0 mm in diameter [2], the typical melt pool size for L-PBF is about 0.2-0.4 mm [3], allowing the printing of fine geometrical details and favoring the use of thin-walled and lattice structures to save weight. This underscores the importance of comprehending the effects of cross-section size on the mechanical properties of the produced printed material, especially in L-PBF processes.

Another issue tied to understanding the effect of printed cross-section size on mechanical properties is the need to comprehend how the rough surface produced by the L-PBF process affects

<figure><img src="image_68.png" /></figure>

<footnote n="1">¹Jon Lilletuns vei 9, 4879, Grimstad, University of Agder, Norway</footnote>

<footnote n="2">²Jon Lilletuns vei 9, 4879, Grimstad, Mechatronics Innovation Lab, Norway</footnote>

<footnote>ᵃdmitry.vysochinskiy@uia.no</footnote>
the mechanical properties of the material. This is more critical for L-PBF processes where the rough surface is commonly retained, as opposed to DED processes where the rough surface is usually machined away in post-processing to meet surface quality and dimensional tolerance requirements. In L-PBF, two different laser scanning patterns are often used on the surface and in the middle of the cross-section, with a hatching pattern used in the bulk and a contour scanning used on the surface to improve surface quality. This is likely to result in differences in microstructure and mechanical properties between the surface and bulk material. Understanding these effects is essential for structural designers to determine when the inhomogeneous cross-section needs to be considered and when it is safe to treat the cross-section as homogeneous.

When it comes to SLM-produced 316L stainless steel, the material this article focuses on, there are studies that report strength to decrease with cross-section size, studies that report no difference in strength for different cross sections sizes and studies that report strength to increase with cross-section size. An example of a study that reports higher strength and ductility for smaller cross-section samples is study by Yu et al. where initial result were published in [4] and expanded study was published in [5]. The authors attribute the effect to finer grain structure of the surface layers [4], [5]. An example of study that finds no difference in strength for different diameters is study by Bültmann et al. [6] who reported the tensile and yield strength of 0.3 mm to 0.7 mm SLM-produced 316L micro-struts to be virtually independent of the strut diameter but lower than the strength of a larger diameter machined SLM-produced sample. There are several examples of studies that report lower strength for smaller cross-section size such as the study by Wang et al. [7] who tested struts of 0.25 mm to 2.0 mm diameter and observed increase in strength with the sample diameter and study by Roach et al. [8] who tested samples with square cross-section with dimensions from 0.4 mm up to 6.25 mm [8].

Thus, the results in the literature are contradictory, and more experimental evidence needs to be gathered in order to justify and understand the effect of printed cross-section size on the strength of SLM-produced 316L stainless steel in a systematic manner. In the presented paper, we compare the strength of SLM produced samples in the diameter range from 2.5 mm to 6.0 mm to quantify the effect of cross-section size on strength. We also compare machined samples with net-shape produced samples of the same diameter to observe the difference and justify the effect of the surface layer on strength.

## Materials and Methods

The tensile samples were designed for use with a 25 mm gauge length extensometer, with a total of 4 sets of 4 samples produced. The sample geometry is illustrated in Fig. 1. The machined samples were cut from 10 mm diameter printed cylinders using a lathe machine. Fig. 1 schematically shows the printed cylinder outline and the final sample geometry. The net-shape samples were tested as printed, meaning the required sample geometry was achieved through 3D printing without any additional machining. The raw material used for printing was PowderRange gas-atomized 316L stainless steel alloy powder designed for L-PBF, with a particle diameter range of 15-53 µm.
the mechanical properties of the material. This is more critical for L-PBF processes where the rough surface is commonly retained, as opposed to DED processes where the rough surface is usually machined away in post-processing to meet surface quality and dimensional tolerance requirements. In L-PBF, two different laser scanning patterns are often used on the surface and in the middle of the cross-section, with a hatching pattern used in the bulk and a contour scanning used on the surface to improve surface quality. This is likely to result in differences in microstructure and mechanical properties between the surface and bulk material. Understanding these effects is essential for structural designers to determine when the inhomogeneous cross-section needs to be considered and when it is safe to treat the cross-section as homogeneous.

When it comes to SLM-produced 316L stainless steel, the material this article focuses on, there are studies that report strength to decrease with cross-section size, studies that report no difference in strength for different cross sections sizes and studies that report strength to increase with cross-section size. An example of a study that reports higher strength and ductility for smaller cross-section samples is study by Yu et al. where initial result were published in [4] and expanded study was published in [5]. The authors attribute the effect to finer grain structure of the surface layers [4], [5]. An example of study that finds no difference in strength for different diameters is study by Bültmann et al. [6] who reported the tensile and yield strength of 0.3 mm to 0.7 mm SLM-produced 316L micro-struts to be virtually independent of the strut diameter but lower than the strength of a larger diameter machined SLM-produced sample. There are several examples of studies that report lower strength for smaller cross-section size such as the study by Wang et al. [7] who tested struts of 0.25 mm to 2.0 mm diameter and observed increase in strength with the sample diameter and study by Roach et al. [8] who tested samples with square cross-section with dimensions from 0.4 mm up to 6.25 mm [8].

Thus, the results in the literature are contradictory, and more experimental evidence needs to be gathered in order to justify and understand the effect of printed cross-section size on the strength of SLM-produced 316L stainless steel in a systematic manner. In the presented paper, we compare the strength of SLM produced samples in the diameter range from 2.5 mm to 6.0 mm to quantify the effect of cross-section size on strength. We also compare machined samples with net-shape produced samples of the same diameter to observe the difference and justify the effect of the surface layer on strength.

## Materials and Methods

The tensile samples were designed for use with a 25 mm gauge length extensometer, with a total of 4 sets of 4 samples produced. The sample geometry is illustrated in Fig. 1. The machined samples were cut from 10 mm diameter printed cylinders using a lathe machine. Fig. 1 schematically shows the printed cylinder outline and the final sample geometry. The net-shape samples were tested as printed, meaning the required sample geometry was achieved through 3D printing without any additional machining. The raw material used for printing was PowderRange gas-atomized 316L stainless steel alloy powder designed for L-PBF, with a particle diameter range of 15-53 µm.
<figure><img src="image_68.png" /><figcaption>Figure 1. Dog-bone samples geometry: machined, d=6.0 mm, d=5.0 mm and d=2.5 mm.</figcaption></figure>

The samples were printed on a 48 mm x 48 mm build plate using the SLM®280 metal printer. As Fig. 2 indicates, all samples were oriented vertically i.e. only vertical direction (also commonly called build direction) was tested. The vertical direction was chosen as SLM-produced metals are anisotropic, and the samples oriented in the vertical direction usually display lower strength, although there might be exceptions as shown by Hitzler et al. [9]. The printing layer thickness was set to 60 µm and argon gas was used as protective atmosphere. Build chamber temperature was 100 degrees centigrade and the substrate temperature was ambient. Different combinations of laser power and scanning speed were applied to different parts of the print, but the ones that were used to construct gauge section of the samples were 150 W laser power combined with 350 mm/s scanning speed for contour scanning and 350 W laser power combined with 950 mm/s scanning speed for hatching as Fig. 2 indicates.

The tensile tests were performed using 25 kN SI-plan tension machine with hydraulic grips. The machine is equipped with load cell, build-in position sensor and clip-on LVDT-based extensometer with gauge length of 25 mm. The target strain rate was 0.00025 s⁻¹, which is “Range 2, recommended rate unless otherwise specified” in accordance to the testing standard [10]. The specimen parallel length was 35 mm, thus the target strain rate was achieved by setting crosshead separating rate to 0.00875 mm/s. The elongation at fracture %EL was measured manually by applying markings on the specimen spaced l₀=5d₀ and using a caliper with 0.05 mm accuracy, plastic area reduction %RA was also measured using the same caliper.
<figure><img src="image_69.png" /><figcaption>Figure 1. Dog-bone samples geometry: machined, d=6.0 mm, d=5.0 mm and d=2.5 mm.</figcaption></figure>

The samples were printed on a 48 mm x 48 mm build plate using the SLM®280 metal printer. As Fig. 2 indicates, all samples were oriented vertically i.e. only vertical direction (also commonly called build direction) was tested. The vertical direction was chosen as SLM-produced metals are anisotropic, and the samples oriented in the vertical direction usually display lower strength, although there might be exceptions as shown by Hitzler et al. [9]. The printing layer thickness was set to 60 µm and argon gas was used as protective atmosphere. Build chamber temperature was 100 degrees centigrade and the substrate temperature was ambient. Different combinations of laser power and scanning speed were applied to different parts of the print, but the ones that were used to construct gauge section of the samples were 150 W laser power combined with 350 mm/s scanning speed for contour scanning and 350 W laser power combined with 950 mm/s scanning speed for hatching as Fig. 2 indicates.

The tensile tests were performed using 25 kN SI-plan tension machine with hydraulic grips. The machine is equipped with load cell, build-in position sensor and clip-on LVDT-based extensometer with gauge length of 25 mm. The target strain rate was 0.00025 s⁻¹, which is “Range 2, recommended rate unless otherwise specified” in accordance to the testing standard [10]. The specimen parallel length was 35 mm, thus the target strain rate was achieved by setting crosshead separating rate to 0.00875 mm/s. The elongation at fracture %EL was measured manually by applying markings on the specimen spaced l₀=5d₀ and using a caliper with 0.05 mm accuracy, plastic area reduction %RA was also measured using the same caliper.
<figure><img src="image_69.png" /><figcaption>Figure 2. Laser scanning pattern in the gauge part and samples on a build plate.</figcaption></figure>

<figure><img src="image_70.png" /><figcaption>Figure 3. Sample with an extensometer attached and marked samples during testing.</figcaption></figure>
<figure><img src="image_70.png" /><figcaption>Figure 2. Laser scanning pattern in the gauge part and samples on a build plate.</figcaption></figure>

<figure><img src="image_71.png" /><figcaption>Figure 3. Sample with an extensometer attached and marked samples during testing.</figcaption></figure>
Fig. 3 displays images of samples before and after testing, with visible markings applied for measuring %EL. In the figure, 5 mm diameter net-shape and machined samples have already been tested, while 2.5 mm and 6.0 mm samples are awaiting testing. It's important to note that the matte surface in the gauge part of machined samples is a result of plastic deformation (orange peel effect), while the original blank machined surface is visible at the transition from the gauge part to the grip part of the samples. To quantify the surface roughness, measurements were taken using a Mitutoyo SJ-210 machine. The surface roughness parameters for printed surfaces were measured to be Rz≈38.4 µm and Ra≈6.4 µm. Unfortunately, the machined gauge section of the sample could not be accessed by the Mitutoyo SJ-210 probe. Still, conservative estimates of roughness parameters, based on measurements of a cylinder machined by the same lathe machine, were Rz≈4.1 µm and Ra≈1.1 µm.

## Results

<table><caption>Table 1. Strength and ductility parameters summary.</caption><thead><tr><th>Sample series</th><th>Machined samples</th><th>Net-shape d=6.0 mm</th><th>Net-shape d=5.0 mm</th><th>Net-shape d=2.5 mm</th></tr></thead><tbody><tr><td>Yield Strength (YS) [MPa]</td><td>511</td><td>458</td><td>464</td><td>445</td></tr><tr><td>YS SD [MPa]</td><td>7</td><td>6</td><td>3</td><td>19</td></tr><tr><td>Tensile Strength (TS)</td><td>636</td><td>606</td><td>601</td><td>587</td></tr><tr><td>TS SD [MPa]</td><td>2</td><td>2</td><td>3</td><td>6</td></tr><tr><td>Elongation at fracture (%EL)</td><td>61.9</td><td>62.2</td><td>62.7</td><td>64.5</td></tr><tr><td>%EL SD</td><td>0.5</td><td>0.4</td><td>2.7</td><td>7.8</td></tr><tr><td>Area Reduction (%RA)</td><td>68.9</td><td>59.3</td><td>61.2</td><td>55.3</td></tr><tr><td>%RA SD</td><td>3.0</td><td>2.8</td><td>2.6</td><td>3.9</td></tr></tbody></table>

The results of the strength and ductility measurements are summarized in Table 1. The material does not display yield plateau; therefore, the yield strength was determined at 0.2% permanent elongation based on the extensometer data. The strength parameters reported in the Table 1 were determined using the nominal cross-section diameter i.e. the diameter specified in printing parameters for net-shape samples. Elongation at fracture %EL was determined over the length $l_0=5d_0$. Fig. 4 compares engineering stress-strain curves from one representative sample from each series. Table 1 also includes standard deviation (SD) in order to quantify the level of the scatter in the measurements, while Fig. 5 provides a visual illustration of the scatter by comparing engineering stress-strain curves from samples of the same series. Engineering stress-strain curves in Fig. 4 and Fig. 5 were constructed based on force and crosshead displacement data, with nominal sample diameters used for calculating engineering stress and a parallel length of 35.0 mm used for calculating engineering strain.

If we try to explain the difference in tensile strength between machined and net-shape samples of the same diameter by the reduction of initial cross section area as done by Salzbrenner et al. [11], a simple calculation shows that for the tensile strength to be equal, the actual load bearing area of net-shape sample would need to be:

$$A_{\text{corrected net shape}} = \frac{TS_{\text{net shape}}}{TS_{\text{machined}}} A_{\text{machined}} = \frac{601 \text{ MPa}}{636 \text{ MPa}} A_{\text{machined}} = 0.945 A_{\text{machined}}$$
Fig. 3 displays images of samples before and after testing, with visible markings applied for measuring %EL. In the figure, 5 mm diameter net-shape and machined samples have already been tested, while 2.5 mm and 6.0 mm samples are awaiting testing. It's important to note that the matte surface in the gauge part of machined samples is a result of plastic deformation (orange peel effect), while the original blank machined surface is visible at the transition from the gauge part to the grip part of the samples. To quantify the surface roughness, measurements were taken using a Mitutoyo SJ-210 machine. The surface roughness parameters for printed surfaces were measured to be Rz≈38.4 µm and Ra≈6.4 µm. Unfortunately, the machined gauge section of the sample could not be accessed by the Mitutoyo SJ-210 probe. Still, conservative estimates of roughness parameters, based on measurements of a cylinder machined by the same lathe machine, were Rz≈4.1 µm and Ra≈1.1 µm.

## Results

<table><caption>Table 1. Strength and ductility parameters summary.</caption><thead><tr><th>Sample series</th><th>Machined samples</th><th>Net-shape d=6.0 mm</th><th>Net-shape d=5.0 mm</th><th>Net-shape d=2.5 mm</th></tr></thead><tbody><tr><td>Yield Strength (YS) [MPa]</td><td>511</td><td>458</td><td>464</td><td>445</td></tr><tr><td>YS SD [MPa]</td><td>7</td><td>6</td><td>3</td><td>19</td></tr><tr><td>Tensile Strength (TS)</td><td>636</td><td>606</td><td>601</td><td>587</td></tr><tr><td>TS SD [MPa]</td><td>2</td><td>2</td><td>3</td><td>6</td></tr><tr><td>Elongation at fracture (%EL)</td><td>61.9</td><td>62.2</td><td>62.7</td><td>64.5</td></tr><tr><td>%EL SD</td><td>0.5</td><td>0.4</td><td>2.7</td><td>7.8</td></tr><tr><td>Area Reduction (%RA)</td><td>68.9</td><td>59.3</td><td>61.2</td><td>55.3</td></tr><tr><td>%RA SD</td><td>3.0</td><td>2.8</td><td>2.6</td><td>3.9</td></tr></tbody></table>

The results of the strength and ductility measurements are summarized in Table 1. The material does not display yield plateau; therefore, the yield strength was determined at 0.2% permanent elongation based on the extensometer data. The strength parameters reported in the Table 1 were determined using the nominal cross-section diameter i.e. the diameter specified in printing parameters for net-shape samples. Elongation at fracture %EL was determined over the length $l_0=5d_0$. Fig. 4 compares engineering stress-strain curves from one representative sample from each series. Table 1 also includes standard deviation (SD) in order to quantify the level of the scatter in the measurements, while Fig. 5 provides a visual illustration of the scatter by comparing engineering stress-strain curves from samples of the same series. Engineering stress-strain curves in Fig. 4 and Fig. 5 were constructed based on force and crosshead displacement data, with nominal sample diameters used for calculating engineering stress and a parallel length of 35.0 mm used for calculating engineering strain.

If we try to explain the difference in tensile strength between machined and net-shape samples of the same diameter by the reduction of initial cross section area as done by Salzbrenner et al. [11], a simple calculation shows that for the tensile strength to be equal, the actual load bearing area of net-shape sample would need to be:

$$A_{\text{corrected net shape}} = \frac{TS_{\text{net shape}}}{TS_{\text{machined}}} A_{\text{machined}} = \frac{601 \text{ MPa}}{636 \text{ MPa}} A_{\text{machined}} = 0.945 A_{\text{machined}}$$
<figure><img src="image_71.png" /><figcaption>Figure 4 Representative engineering stress-strain curves for each series.</figcaption></figure>

<figure><img src="image_72.png" /><figcaption>Figure 5 Illustration of scatter levels in different sample series.</figcaption></figure>
<figure><img src="image_72.png" /><figcaption>Figure 4 Representative engineering stress-strain curves for each series.</figcaption></figure>

<figure><img src="image_73.png" /><figcaption>Figure 5 Illustration of scatter levels in different sample series.</figcaption></figure>
resulting in an effective sample diameter

$$d_{corrected\ net\ shape} = \sqrt{0.945} d_0 = 0.972 d_0 = 4.86\ mm$$

This would require peak surface roughness to be $Rz \approx (5-4.86) mm/2 = 0.070 mm = 70 \ \mu m$ which is nearly twice the measured value of $Rz \approx 38.4 \ \mu m$. If we would try to achieve same yield strength instead of same tensile strength, the required value of roughness would be even greater. This indicates that the surface roughness is likely not the only cause of difference in strength.

## Discussions

Table 2 presents a summary of the literature finding with regard to effect of cross-section size on the strength of SLM-produced 316L steel and compares our findings with the results in the literature. It can be seen from Table 2 that, in the majority of cases, smaller diameter samples display lower strength than the larger diameter ones, making the findings of Yu et al. [4], [5] an anomaly. Our study is the only one that offers direct comparison between machined and net shape samples of the same diameter. As shown in the results section, the difference is unlikely to be explained purely by reduction of cross section area due to surface roughness and a second contributing factor must be that surface layer is to some degree weaker than the core of the sample e.g. contains more defects or has a different microstructure or texture. In the results presented in Table 2, only Yu et al. [5] explicitly discuss surface layer having different mechanical properties than the core of the cross section, but in their case surface layer appears to be stronger, not weaker. Microhardness plots of Wang et al. [7] indicate some lower hardness for near the surface for 1 mm and 2 mm struts, but no such effect is visible for 3 mm and 5 mm struts, however, average value of hardness increases with the strut diameter.

If we have a look at size effect in other common SLM-produced materials we find that in case of Ti-6Al-4V alloy, results in the literature are also conflicting Barba et al. [12] observed systematic increase in strength and decrease in ductility as the printed cross section is reduced while Dzugan et al [13] observed the opposite tendency i.e. lower strength in smaller thickness samples and larger strength in thicker samples. Despite reporting opposite effect of cross-section size on strength both [12] and [13] report that the size effect nearly disappears, when tensile samples are machined i.e. when the surface layer is removed. In case of AlSi10Mg another popular L-PBF alloy, only studies reporting reduction of strength for smaller diameters [14], [15] and reduction of hardness for smaller thicknesses [16] were found.

## Conclusion

This study was motivated by the need to acquire fist hand data on size effect in SLM-produced steel, as the literature results are contradictory (see Table 2) and the mechanical properties of SLM-produced metals are strongly affected by the process parameters [17]. Our experimental results along with the literature review leave us to conclude that reduction of strength for smaller diameter samples is a more common tendency for SLM-produced 316L steel, while the strengthening effect observed by Yu et al. [5] is a peculiarity yet to be reproduced by other researchers. The observed reduction of strength is likely to be attributed to surface roughness and defects in the surface area as smaller diameter samples have a larger surface to area ratio in comparison with larger diameter samples. In case of tested 316L steel, this conclusion is also supported by machined samples displaying higher strength than net-shape samples of the same diameter.
resulting in an effective sample diameter

$$d_{corrected\ net\ shape} = \sqrt{0.945} d_0 = 0.972 d_0 = 4.86\ mm$$

This would require peak surface roughness to be $Rz \approx (5-4.86) \ mm/2 = 0.070 \ mm = 70 \ \mu m$ which is nearly twice the measured value of $Rz \approx 38.4 \ \mu m$. If we would try to achieve same yield strength instead of same tensile strength, the required value of roughness would be even greater. This indicates that the surface roughness is likely not the only cause of difference in strength.

## Discussions

Table 2 presents a summary of the literature finding with regard to effect of cross-section size on the strength of SLM-produced 316L steel and compares our findings with the results in the literature. It can be seen from Table 2 that, in the majority of cases, smaller diameter samples display lower strength than the larger diameter ones, making the findings of Yu et al. [4], [5] an anomaly. Our study is the only one that offers direct comparison between machined and net shape samples of the same diameter. As shown in the results section, the difference is unlikely to be explained purely by reduction of cross section area due to surface roughness and a second contributing factor must be that surface layer is to some degree weaker than the core of the sample e.g. contains more defects or has a different microstructure or texture. In the results presented in Table 2, only Yu et al. [5] explicitly discuss surface layer having different mechanical properties than the core of the cross section, but in their case surface layer appears to be stronger, not weaker. Microhardness plots of Wang et al. [7] indicate some lower hardness for near the surface for 1 mm and 2 mm struts, but no such effect is visible for 3 mm and 5 mm struts, however, average value of hardness increases with the strut diameter.

If we have a look at size effect in other common SLM-produced materials we find that in case of Ti-6Al-4V alloy, results in the literature are also conflicting Barba et al. [12] observed systematic increase in strength and decrease in ductility as the printed cross section is reduced while Dzugan et al [13] observed the opposite tendency i.e. lower strength in smaller thickness samples and larger strength in thicker samples. Despite reporting opposite effect of cross-section size on strength both [12] and [13] report that the size effect nearly disappears, when tensile samples are machined i.e. when the surface layer is removed. In case of AlSi10Mg another popular L-PBF alloy, only studies reporting reduction of strength for smaller diameters [14], [15] and reduction of hardness for smaller thicknesses [16] were found.

## Conclusion

This study was motivated by the need to acquire fist hand data on size effect in SLM-produced steel, as the literature results are contradictory (see Table 2) and the mechanical properties of SLM-produced metals are strongly affected by the process parameters [17]. Our experimental results along with the literature review leave us to conclude that reduction of strength for smaller diameter samples is a more common tendency for SLM-produced 316L steel, while the strengthening effect observed by Yu et al. [5] is a peculiarity yet to be reproduced by other researchers. The observed reduction of strength is likely to be attributed to surface roughness and defects in the surface area as smaller diameter samples have a larger surface to area ratio in comparison with larger diameter samples. In case of tested 316L steel, this conclusion is also supported by machined samples displaying higher strength than net-shape samples of the same diameter.
<table><caption>Table2: Summary of literature findings on size effect in produced 316L steel</caption><thead><tr><td>Study, laser power and scanning speed</td><td>Sample diameter/thickness range</td><td>Results</td><td>Explanation</td></tr></thead><tbody><tr><td>Bültmann et al. [6]<br/>195 W laser power combined with 900mm/s scanning speed</td><td>0.25 mm-0.7 mm plus “standard” machined samples of unreported geometry presumably 5.0 mm in diameter</td><td>Nearly constant strength with size. Machined samples were stronger than the struts samples</td><td>Multiple, but the reduction of strength due to “surface defects” seems to be the main reason</td></tr><tr><td>Wang et al. [7]<br/>Max laser power 200 W no further details reported.</td><td>0.25 mm to 5.0 mm diameter for microhardness 0.25 mm to 2.0 mm for tensile testing</td><td>Smaller diameter samples display lower strength. Average microhardness increases with strut diameter. No comparison with machined samples</td><td>The difference is attributed to different texture in different strut diameters</td></tr><tr><td>Roach et al. [8]<br/>Nominal laser power 103 W and scanning speed of 1400 mm/s</td><td>0.4 mm to 6.25 mm square cross-section sample</td><td>Smaller cross-section samples display lower strength. No comparison with machined samples</td><td>The difference is attributed to the influence of surface roughness</td></tr><tr><td>Yu et al. [4], [5]<br/>150W laser power combined with 350 mm/s scan speed for both contour and hatching.</td><td>0.75 mm to 6.0 mm thickness samples</td><td>Smaller diameter samples display higher strength. No comparison with machined samples</td><td>The strengthening effect is explained by the formation of finer grains in the surface layer.</td></tr><tr><td>This study<br/>150 W laser power combined with 350 mm/s for border/contour and 350 W combined with 950 mm/s for hatching.</td><td>2.5 mm, to 6.0 mm diameter net shape and 5.0 mm diameter machined samples</td><td>Smaller diameter samples display lower strength. Machined samples were stronger than unmachined ones</td><td>Presumably surface roughness and defects in the surface layer</td></tr></tbody></table>

<footnote>* Recommended standard diameters for cylindrical samples are 5 mm, 10 mm, 14 mm and 20 mm [10]. It is reasonable to assume that the lowest diameter was used.</footnote>
<table><caption>Table2: Summary of literature findings on size effect in produced 316L steel</caption><thead><tr><td>Study, laser power and scanning speed</td><td>Sample diameter/thickness range</td><td>Results</td><td>Explanation</td></tr></thead><tbody><tr><td>Bültmann et al. [6]<br/>195 W laser power combined with 900mm/s scanning speed</td><td>0.25 mm-0.7 mm plus “standard” machined samples of unreported geometry presumably 5.0 mm in diameter</td><td>Nearly constant strength with size. Machined samples were stronger than the struts samples</td><td>Multiple, but the reduction of strength due to “surface defects” seems to be the main reason</td></tr><tr><td>Wang et al. [7]<br/>Max laser power 200 W no further details reported.</td><td>0.25 mm to 5.0 mm diameter for microhardness 0.25 mm to 2.0 mm for tensile testing</td><td>Smaller diameter samples display lower strength. Average microhardness increases with strut diameter. No comparison with machined samples</td><td>The difference is attributed to different texture in different strut diameters</td></tr><tr><td>Roach et al. [8]<br/>Nominal laser power 103 W and scanning speed of 1400 mm/s</td><td>0.4 mm to 6.25 mm square cross-section sample</td><td>Smaller cross-section samples display lower strength. No comparison with machined samples</td><td>The difference is attributed to the influence of surface roughness</td></tr><tr><td>Yu et al. [4], [5]<br/>150W laser power combined with 350 mm/s scan speed for both contour and hatching.</td><td>0.75 mm to 6.0 mm thickness samples</td><td>Smaller diameter samples display higher strength. No comparison with machined samples</td><td>The strengthening effect is explained by the formation of finer grains in the surface layer.</td></tr><tr><td>This study<br/>150 W laser power combined with 350 mm/s for border/contour and 350 W combined with 950 mm/s for hatching.</td><td>2.5 mm, to 6.0 mm diameter net shape and 5.0 mm diameter machined samples</td><td>Smaller diameter samples display lower strength. Machined samples were stronger than unmachined ones</td><td>Presumably surface roughness and defects in the surface layer</td></tr></tbody></table>

<footnote>* Recommended standard diameters for cylindrical samples are 5 mm, 10 mm, 14 mm and 20 mm [10]. It is reasonable to assume that the lowest diameter was used.</footnote>
## References

[1] N.-E. I. 52900, Additiv produksjon. Generelle prinsipper. Terminologi = Additive manufacturing. General principles. Terminology, vol. NS-EN ISO/. in Norsk Standard, vol. NS-EN ISO/. Lysaker: Norsk Standard, 2017.

[2] I. Jeon, L. Yang, K. Ryu, and H. Sohn, "Online melt pool depth estimation during directed energy deposition using coaxial infrared camera, laser line scanner, and artificial neural network," *Addit Manuf*, vol. 47, p. 102295, Nov. 2021. https://doi.org/10.1016/J ADDMA.2021.102295

[3] B. Cheng, J. Lydon, K. Cooper, V. Cole, P. Northrop, and K. Chou, "Melt pool sensing and size analysis in laser powder-bed metal additive manufacturing," *J Manuf Process*, vol. 32, pp. 744-753, Apr. 2018. https://doi.org/10.1016/j.jmapro.2018.04.002

[4] J. Yu, D. Kim, K. Ha, J. B. Jeon, and W. Lee, "Strong feature size dependence of tensile properties and its microstructural origin in selectively laser melted 316L stainless steel," *Mater Lett*, vol. 275, p. 128161, Sep. 2020. https://doi.org/10.1016/J.MATLET.2020.128161

[5] J. Yu, D. Kim, K. Ha, J. B. Jeon, D. J. Kim, and W. Lee, "Size effect due to contour laser scanning in 316L stainless steel produced by laser powder bed fusion," *Journal of Materials Research and Technology*, vol. 15, pp. 5554-5568, Nov. 2021. https://doi.org/10.1016/JJMRT.2021.11.034

[6] J. Bültmann, S. Merkt, C. Hammer, C. Hinke, and U. Prahl, "Scalability of the mechanical properties of selective laser melting produced micro-struts," *J Laser Appl*, vol. 27, p. null, 2015. https://doi.org/10.2351/1.4906392

[7] X. Wang, J. A. Muñiz-Lerma, O. Sanchez-Mata, M. A. Shandiz, and M. Brochu, "Microstructure and mechanical properties of stainless steel 316L vertical struts manufactured by laser powder bed fusion process," *Materials Science and Engineering: A*, vol. null, p. null, 2018. https://doi.org/10.1016/J.MSEA.2018.08.069

[8] A. M. Roach, B. C. White, A. Garland, B. H. Jared, J. D. Carroll, and B. L. Boyce, "Size-dependent stochastic tensile properties in additively manufactured 316L stainless steel," *Addit Manuf*, vol. 32, p. 101090, Mar. 2020. https://doi.org/10.1016/J ADDMA.2020.101090

[9] L. Hitzler, J. Hirsch, B. Heine, M. Merkel, W. Hall, and A. Öchsner, "On the anisotropic mechanical properties of selective laser-melted stainless steel," *Materials*, vol. 10, no. 10, 2017. https://doi.org/10.3390/ma10101136

[10] ISO 6892-1:2019(en) Metallic materials — Tensile testing — Part 1: Method of test at room temperature. 2019. Accessed: Nov. 19, 2021. [Online]. Available: https://www.iso.org/standard/61856.html

[11] B. C. Salzbrenner et al., "High-throughput stochastic tensile performance of additively manufactured stainless steel," *J Mater Process Technol*, vol. 241, pp. 1-12, Mar. 2017. https://doi.org/10.1016/j.jmatprotec.2016.10.023

[12] D. Barba, C. Alabort, Y. T. Tang, M. J. Viscasillas, R. C. Reed, and E. Alabort, "On the size and orientation effect in additive manufactured Ti-6Al-4V," *Mater Des*, vol. 186, p. 108235, Jan. 2020. https://doi.org/10.1016/J.MATDES.2019.108235

[13] J. Dzugan et al., "Effects of thickness and orientation on the small scale fracture behaviour of additively manufactured Ti-6Al-4V," *Mater Charact*, vol. 143, pp. 94-109, Sep. 2018. https://doi.org/10.1016/J.MATCHAR.2018.04.003
## References

[1] N.-E. I. 52900, Additiv produksjon. Generelle prinsipper. Terminologi = Additive manufacturing. General principles. Terminology, vol. NS-EN ISO/. in Norsk Standard, vol. NS-EN ISO/. Lysaker: Norsk Standard, 2017.

[2] I. Jeon, L. Yang, K. Ryu, and H. Sohn, "Online melt pool depth estimation during directed energy deposition using coaxial infrared camera, laser line scanner, and artificial neural network," *Addit Manuf*, vol. 47, p. 102295, Nov. 2021. https://doi.org/10.1016/J ADDMA.2021.102295

[3] B. Cheng, J. Lydon, K. Cooper, V. Cole, P. Northrop, and K. Chou, "Melt pool sensing and size analysis in laser powder-bed metal additive manufacturing," *J Manuf Process*, vol. 32, pp. 744-753, Apr. 2018. https://doi.org/10.1016/j.jmapro.2018.04.002

[4] J. Yu, D. Kim, K. Ha, J. B. Jeon, and W. Lee, "Strong feature size dependence of tensile properties and its microstructural origin in selectively laser melted 316L stainless steel," *Mater Lett*, vol. 275, p. 128161, Sep. 2020. https://doi.org/10.1016/J.MATLET.2020.128161

[5] J. Yu, D. Kim, K. Ha, J. B. Jeon, D. J. Kim, and W. Lee, "Size effect due to contour laser scanning in 316L stainless steel produced by laser powder bed fusion," *Journal of Materials Research and Technology*, vol. 15, pp. 5554-5568, Nov. 2021. https://doi.org/10.1016/JJMRT.2021.11.034

[6] J. Bültmann, S. Merkt, C. Hammer, C. Hinke, and U. Prahl, "Scalability of the mechanical properties of selective laser melting produced micro-struts," *J Laser Appl*, vol. 27, p. null, 2015. https://doi.org/10.2351/1.4906392

[7] X. Wang, J. A. Muñiz-Lerma, O. Sanchez-Mata, M. A. Shandiz, and M. Brochu, "Microstructure and mechanical properties of stainless steel 316L vertical struts manufactured by laser powder bed fusion process," *Materials Science and Engineering: A*, vol. null, p. null, 2018. https://doi.org/10.1016/J.MSEA.2018.08.069

[8] A. M. Roach, B. C. White, A. Garland, B. H. Jared, J. D. Carroll, and B. L. Boyce, "Size-dependent stochastic tensile properties in additively manufactured 316L stainless steel," *Addit Manuf*, vol. 32, p. 101090, Mar. 2020. https://doi.org/10.1016/J ADDMA.2020.101090

[9] L. Hitzler, J. Hirsch, B. Heine, M. Merkel, W. Hall, and A. Öchsner, "On the anisotropic mechanical properties of selective laser-melted stainless steel," *Materials*, vol. 10, no. 10, 2017. https://doi.org/10.3390/ma10101136

[10] ISO 6892-1:2019(en) Metallic materials — Tensile testing — Part 1: Method of test at room temperature. 2019. Accessed: Nov. 19, 2021. [Online]. Available: https://www.iso.org/standard/61856.html

[11] B. C. Salzbrenner et al., "High-throughput stochastic tensile performance of additively manufactured stainless steel," *J Mater Process Technol*, vol. 241, pp. 1-12, Mar. 2017. https://doi.org/10.1016/j.jmatprotec.2016.10.023

[12] D. Barba, C. Alabort, Y. T. Tang, M. J. Viscasillas, R. C. Reed, and E. Alabort, "On the size and orientation effect in additive manufactured Ti-6Al-4V," *Mater Des*, vol. 186, p. 108235, Jan. 2020. https://doi.org/10.1016/J.MATDES.2019.108235

[13] J. Dzugan et al., "Effects of thickness and orientation on the small scale fracture behaviour of additively manufactured Ti-6Al-4V," *Mater Charact*, vol. 143, pp. 94-109, Sep. 2018. https://doi.org/10.1016/J.MATCHAR.2018.04.003
[14] Z. Dong, X. Zhang, W. Shi, H. Zhou, H. Lei, and J. Liang, "Study of Size Effect on Microstructure and Mechanical Properties of AlSi10Mg Samples Made by Selective Laser Melting," *Materials 2018*, Vol. 11, Page 2463, vol. 11, no. 12, p. 2463, Dec. 2018. https://doi.org/10.3390/MA11122463

[15] D. Vysochinskiy and N. Akhtar, "On the Influence of Cross-Section Size on Measured Strength of SLM-Produced AlSi10Mg-Alloy," *Key Eng Mater*, vol. 926, pp. 132-140, Jul. 2022. https://doi.org/10.4028/P-G078SC

[16] N. Takata, H. Kodaira, A. Suzuki, and M. Kobashi, "Size dependence of microstructure of AlSi10Mg alloy fabricated by selective laser melting," *Mater Charact*, vol. 143, pp. 18-26, Sep. 2018. https://doi.org/10.1016/J.MATCHAR.2017.11.052

[17] A. Röttger et al., "Microstructure and mechanical properties of 316L austenitic stainless steel processed by different SLM devices," *The International Journal of Advanced Manufacturing Technology* 2020 108:3, vol. 108, no. 3, pp. 769-783, Jul. 2020. https://doi.org/10.1007/S00170-020-05371-1
[14] Z. Dong, X. Zhang, W. Shi, H. Zhou, H. Lei, and J. Liang, "Study of Size Effect on Microstructure and Mechanical Properties of AlSi10Mg Samples Made by Selective Laser Melting," *Materials 2018*, Vol. 11, Page 2463, vol. 11, no. 12, p. 2463, Dec. 2018. https://doi.org/10.3390/MA11122463

[15] D. Vysochinskiy and N. Akhtar, "On the Influence of Cross-Section Size on Measured Strength of SLM-Produced AlSi10Mg-Alloy," *Key Eng Mater*, vol. 926, pp. 132-140, Jul. 2022. https://doi.org/10.4028/P-G078SC

[16] N. Takata, H. Kodaira, A. Suzuki, and M. Kobashi, "Size dependence of microstructure of AlSi10Mg alloy fabricated by selective laser melting," *Mater Charact*, vol. 143, pp. 18-26, Sep. 2018. https://doi.org/10.1016/J.MATCHAR.2017.11.052

[17] A. Röttger et al., "Microstructure and mechanical properties of 316L austenitic stainless steel processed by different SLM devices," *The International Journal of Advanced Manufacturing Technology* 2020 108:3, vol. 108, no. 3, pp. 769-783, Jul. 2020. https://doi.org/10.1007/S00170-020-05371-1
# Thermal multi-sensor instrumentation for the enhancement of a directed energy deposition process

DE PEINDRAY D'AMBELLE Lilou¹,ᵃ*, CHERRIER Olivier¹,ᵇ, MOUSSAOUI Kamel¹,ᶜ and MABRU Catherine¹,ᵈ

¹ Institut Clément Ader (ICA), UMR-CNRS 5312, 3 rue Caroline Aigle, 31400 Toulouse, France

ᵃlilou.de-peindray-d-ambelle@isae-supaero.fr, ᵇolivier.cherrier@isae-supaero.fr,

ᶜkamel.moussaoui@isae-supaero.fr, ᵈcatherine.mabru@isae-supaero.fr

**Keywords:** Additive Manufacturing, Powder Laser Metal Deposition, Instrumentation

**Abstract.** Directed energy deposition (DED) is an additive manufacturing process with growing industrial interests. Nonetheless, its industrialization will not be possible until it is fully mature. Such maturity lies in the upstream research to optimize and control it. In DED, process parameters, physical quantities and parts properties are interrelated which makes it a complex process. To have a better understanding of these relations, the experimental approach of instrumentation has been chosen. Multi-sensor method has been implemented for its more extensive possibilities in comparison to single-sensor methods. A bichromatic pyrometer was coupled to an IR camera to measure the temperature distributions in real time. Post-process characterizations of the aspects and geometries of the parts were related to the sensors' measurements and consequently, to the process parameters. Twelves sets of parameters were tested to conclude that the energy input impacts the size of the melting pool and the temperature distribution. High energies lead to defects such as edge defects and layer thickening but can mitigate surface roughness. Both the pyrometer and camera proved to have a relevance in this study for the enhancement of the DED process.

**Introduction**

Emerging metal additive manufacturing (MAM) processes such as Directed energy deposition (DED) are developed to be industrialized in sectors such as aeronautics and space [1,2]. This freeform direct deposition technique enables the reparation of components, the fabrication of functionally graded materials and the production of parts larger than with Laser Powder-Bed Fusion (LPBF). Moreover, this process meets with the new requirements of sustainable manufacturing. DED involves several parameters (key parameters are laser power P, scanning speed V and powder feed rate Dm) which are governing the physical quantities. These quantities affect in-process and post-process part properties. Among in-process properties is the bead geometry influenced by thermal phenomena such as the cooling rate and thermal gradients. Bead geometry features are linked to the melting pool where the heat input is located and where melting takes place. Post-process part properties have been studied quite broadly in literature, giving the microstructural, roughness, hardness, geometrical aspects of the component [3,4]. Common defects have been brought to the fore: lack of fusion porosities, cracks, anisotropy, substrate separation, part distortion and surface roughness. Once again, these defects are mostly related to thermal phenomena creating residual stresses and heterogeneities [5,6].

From these considerations, thermal phenomena seem to be one key to better understand DED. Process instrumentation, alongside process simulation, is a suitable candidate for the traceability of physical quantities during fabrication. Experimental approaches have been implemented to acquire more knowledge about the relation between process parameters, physical quantities, and part properties. Most studies focus on thermal aspects using a single sensor, generally infrared (IR) cameras and pyrometers [7–10]. The observations made with these sensors give an insight to the causes of geometrical and/or microstructural defects. The use of a single sensor to study the

<figure><img src="image_73.png" /></figure>
# Thermal multi-sensor instrumentation for the enhancement of a directed energy deposition process

DE PEINDRAY D'AMBELLE Lilou¹,ᵃ*, CHERRIER Olivier¹,ᵇ, MOUSSAOUI Kamel¹,ᶜ and MABRU Catherine¹,ᵈ

¹ Institut Clément Ader (ICA), UMR-CNRS 5312, 3 rue Caroline Aigle, 31400 Toulouse, France

ᵃlilou.de-peindray-d-ambelle@isae-supaero.fr, ᵇolivier.cherrier@isae-supaero.fr,

ᶜkamel.moussaoui@isae-supaero.fr, ᵈcatherine.mabru@isae-supaero.fr

**Keywords:** Additive Manufacturing, Powder Laser Metal Deposition, Instrumentation

**Abstract.** Directed energy deposition (DED) is an additive manufacturing process with growing industrial interests. Nonetheless, its industrialization will not be possible until it is fully mature. Such maturity lies in the upstream research to optimize and control it. In DED, process parameters, physical quantities and parts properties are interrelated which makes it a complex process. To have a better understanding of these relations, the experimental approach of instrumentation has been chosen. Multi-sensor method has been implemented for its more extensive possibilities in comparison to single-sensor methods. A bichromatic pyrometer was coupled to an IR camera to measure the temperature distributions in real time. Post-process characterizations of the aspects and geometries of the parts were related to the sensors' measurements and consequently, to the process parameters. Twelves sets of parameters were tested to conclude that the energy input impacts the size of the melting pool and the temperature distribution. High energies lead to defects such as edge defects and layer thickening but can mitigate surface roughness. Both the pyrometer and camera proved to have a relevance in this study for the enhancement of the DED process.

**Introduction**

Emerging metal additive manufacturing (MAM) processes such as Directed energy deposition (DED) are developed to be industrialized in sectors such as aeronautics and space [1,2]. This freeform direct deposition technique enables the reparation of components, the fabrication of functionally graded materials and the production of parts larger than with Laser Powder-Bed Fusion (LPBF). Moreover, this process meets with the new requirements of sustainable manufacturing. DED involves several parameters (key parameters are laser power P, scanning speed V and powder feed rate Dm) which are governing the physical quantities. These quantities affect in-process and post-process part properties. Among in-process properties is the bead geometry influenced by thermal phenomena such as the cooling rate and thermal gradients. Bead geometry features are linked to the melting pool where the heat input is located and where melting takes place. Post-process part properties have been studied quite broadly in literature, giving the microstructural, roughness, hardness, geometrical aspects of the component [3,4]. Common defects have been brought to the fore: lack of fusion porosities, cracks, anisotropy, substrate separation, part distortion and surface roughness. Once again, these defects are mostly related to thermal phenomena creating residual stresses and heterogeneities [5,6].

From these considerations, thermal phenomena seem to be one key to better understand DED. Process instrumentation, alongside process simulation, is a suitable candidate for the traceability of physical quantities during fabrication. Experimental approaches have been implemented to acquire more knowledge about the relation between process parameters, physical quantities, and part properties. Most studies focus on thermal aspects using a single sensor, generally infrared (IR) cameras and pyrometers [7–10]. The observations made with these sensors give an insight to the causes of geometrical and/or microstructural defects. The use of a single sensor to study the

<figure><img src="image_74.png" /></figure>

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process is though limited by parameters' interrelations and interdependence that are characteristic of MAM. Consequently, multi-sensor methods have been newly explored to combine the observations of several process signatures [11].

In this work, a thermal multi-sensor instrumentation was implemented in a DED machine. Both sensors are dedicated to measure temperature evolutions and distributions in real-time. In-process observations were correlated to the part's geometrical features. On the one hand, relevance of thermal multi-sensor approach was explored. On the other hand, the effect of thermal quantities and process inputs were linked to the properties of the part. Perspectives for further investigation of this method will be given.

## Materials and methods

A 5-axis DED machine was used with argon as carrying and shielding gas. The power source is a Nd:YAG fiber laser with a maximal input power of 500 W. Building material was 316L stainless steel powder (Amperprint 0717 grade 1.4404, Höganäs) with particle size ranging from 45 to 90 µm. The powder composition is given in Table 1. 40 mm-long single-bead walls were constructed on 5 mm thick substrates. Each wall is programmed to reach a theoretical height of 15 mm. The deposition strategy (schemed Fig. 1) consisted of a two-way with a 3 s-dwell time between each layer. 12 walls were built according to a design of experiments varying P and V with a fixed value of Dm=6.5 g/min. In this study, P= {250,300,350,400} W and V= {600,900,1200} mm/min. Layer heights have been experimentally obtained with preliminary tests on single beads not described in this paper.

<table><caption>Table 1. Chemical composition of the 316L powder</caption><thead><tr><th>Chemical composition [w%]</th><th>Cr</th><th>Ni</th><th>Mo</th><th>Mn</th><th>Si</th><th>C</th><th>P</th><th>S</th><th>O</th><th>N</th><th>Fe</th></tr></thead><tbody><tr><th>Min</th><td>16.5</td><td>10.0</td><td>2.0</td><td>0.15</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td rowspan="2">Balance</td></tr><tr><th>Max</th><td>18.5</td><td>14.0</td><td>3.0</td><td>2.0</td><td>1.0</td><td>0.03</td><td>0.045</td><td>0.015</td><td>0.05</td><td>0.03</td></tr></tbody></table>

<figure><img src="image_74.png" /><figcaption>Figure 1. Deposition of the single-bead wall geometry</figcaption></figure>

The instrumentation of the process consisted of an IR camera (PI08M, Optris) and a bichromatic pyrometer (CTRatio 2MH1, Optris). The camera is mounted on a support to observe the wall perpendicularly at 200 mm (distance between lens and laser axis). The resolution of the camera is 764x480 pixels for a recording framerate of 32 Hz. Its spectral range is 780-820 nm with a temperature range of 625-1900 °C. The bichromatic pyrometer was mounted on the deposition head and adjusted to measure perpendicularly to the wall also. The laser spot was aligned to the deposition axis 150 mm away from the wall. The pyrometer's spot is positioned to measure right underneath the layer that is being deposited. It has a temperature range of 550-3000 °C and a time resolution of 1 msec. The entire setup is shown Fig. 2.
process is though limited by parameters' interrelations and interdependence that are characteristic of MAM. Consequently, multi-sensor methods have been newly explored to combine the observations of several process signatures [11].

In this work, a thermal multi-sensor instrumentation was implemented in a DED machine. Both sensors are dedicated to measure temperature evolutions and distributions in real-time. In-process observations were correlated to the part's geometrical features. On the one hand, relevance of thermal multi-sensor approach was explored. On the other hand, the effect of thermal quantities and process inputs were linked to the properties of the part. Perspectives for further investigation of this method will be given.

## Materials and methods

A 5-axis DED machine was used with argon as carrying and shielding gas. The power source is a Nd:YAG fiber laser with a maximal input power of 500 W. Building material was 316L stainless steel powder (Amperprint 0717 grade 1.4404, Höganäs) with particle size ranging from 45 to 90 µm. The powder composition is given in Table 1. 40 mm-long single-bead walls were constructed on 5 mm thick substrates. Each wall is programmed to reach a theoretical height of 15 mm. The deposition strategy (schemed Fig. 1) consisted of a two-way with a 3 s-dwell time between each layer. 12 walls were built according to a design of experiments varying P and V with a fixed value of Dm=6.5 g/min. In this study, P= {250,300,350,400} W and V= {600,900,1200} mm/min. Layer heights have been experimentally obtained with preliminary tests on single beads not described in this paper.

<table><caption>Table 1. Chemical composition of the 316L powder</caption><thead><tr><th>Chemical composition [w%]</th><th>Cr</th><th>Ni</th><th>Mo</th><th>Mn</th><th>Si</th><th>C</th><th>P</th><th>S</th><th>O</th><th>N</th><th>Fe</th></tr></thead><tbody><tr><th>Min</th><td>16.5</td><td>10.0</td><td>2.0</td><td>0.15</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td rowspan="2">Balance</td></tr><tr><th>Max</th><td>18.5</td><td>14.0</td><td>3.0</td><td>2.0</td><td>1.0</td><td>0.03</td><td>0.045</td><td>0.015</td><td>0.05</td><td>0.03</td></tr></tbody></table>

<figure><img src="image_75.png" /><figcaption>Figure 1. Deposition of the single-bead wall geometry</figcaption></figure>

The instrumentation of the process consisted of an IR camera (PI08M, Optris) and a bichromatic pyrometer (CTRatio 2MH1, Optris). The camera is mounted on a support to observe the wall perpendicularly at 200 mm (distance between lens and laser axis). The resolution of the camera is 764x480 pixels for a recording framerate of 32 Hz. Its spectral range is 780-820 nm with a temperature range of 625-1900 °C. The bichromatic pyrometer was mounted on the deposition head and adjusted to measure perpendicularly to the wall also. The laser spot was aligned to the deposition axis 150 mm away from the wall. The pyrometer's spot is positioned to measure right underneath the layer that is being deposited. It has a temperature range of 550-3000 °C and a time resolution of 1 msec. The entire setup is shown Fig. 2.
<figure><img src="image_75.png" /><figcaption>Figure 2. Experimental setup</figcaption></figure>

The pixel size was determined with the ImageJ software thanks to the known distances in mm. It has been concluded that each pixel has a size of 0.25x0.25 mm². On IR images, the melting pool was identified as the area where the temperature exceeds the melting point of the material. In this study, the melting pool was set at 1400 °C.

Optical microscopy was conducted using a numerical microscope (VHX-970F, Keyence) to observe the general aspects of built walls. Mean final height was determined for each wall using a measuring column (LH-600D/DG, Mitutoyo) with 5 measure points along the top of the wall.

# Results and discussion

## Height measurements

Fig.3 compares the results of the mean height to the theoretical height of the walls.

<figure><img src="image_76.png" /><figcaption>Figure 3. Final mean wall height as a function of process parameters P and V</figcaption></figure>
<figure><img src="image_76.png" /><figcaption>Figure 2. Experimental setup</figcaption></figure>

The pixel size was determined with the ImageJ software thanks to the known distances in mm. It has been concluded that each pixel has a size of 0.25x0.25 mm². On IR images, the melting pool was identified as the area where the temperature exceeds the melting point of the material. In this study, the melting pool was set at 1400 °C.

Optical microscopy was conducted using a numerical microscope (VHX-970F, Keyence) to observe the general aspects of built walls. Mean final height was determined for each wall using a measuring column (LH-600D/DG, Mitutoyo) with 5 measure points along the top of the wall.

# Results and discussion

## Height measurements

Fig.3 compares the results of the mean height to the theoretical height of the walls.

<figure><img src="image_77.png" /><figcaption>Figure 3. Final mean wall height as a function of process parameters P and V</figcaption></figure>
Out of the 12 tests, only 3 of them, P300V900 and P400V1200 and P300V1200, reached approximately the desired final height. The other tests lead to small to important defocusing of the laser during construction. Fig. 3 shows that increasing the energy input (ratio between laser power and scanning speed) leads to final heights that are smaller than the theoretical height. This phenomenon is due to the use of constant laser power during construction. Indeed, along the wall, there will be more and more radial heat dissipation leading to an increase of the melt pool flow due the Marangoni effect [12]. Moreover, it is possible to observe that with small scanning speed V=600 mm/min, the final height of the walls is more affected than with the other speeds. Slow speed can lead to the widening of the melt pool and the flattening of the deposit because of higher dilution. This phenomenon has be shown in [13–15] and is more important when increasing P. Hence, the layers will become uneven, and the overall height will be smaller than planned. The results in Fig. 3 bring to the foreground the impact of key parameters on part’s geometrical features. Nonetheless, an optimized set of parameters, such as P300V900 and P80V1200 in this study, could lead to components with final dimensions matching the model.

Optical microscopy

Examples of optical micrographs are shown Fig. 4. For this wall built with P=300 W and V=900 mm/min, the layers seem regular at the bottom of the part. The middle and top part of the wall have fewer regular layers and the latter form an interlacing. Along a single layer, the height varies. This effect is more visible on Fig. 5.

<figure><img src="image_77.png" /><figcaption>Figure 4. Example of a built wall (P300V900) and optical micrographs from the top middle part and the bottom middle part. Magnification x20</figcaption></figure>

On Fig. 5, the interlacing effect is important for P300V900 and P400V900. The input parameters influence the form and aspect of the built layers. When the energy is lower, for instance at P400V900, the layers are more regular and straighter. These interlacing are due to the important heat input and are visible on every wall except for the ones built with P=250 W. The hypothesis is that when the energy is too high, the deposited layer might significantly melt the previous one and leads to the thickening of the current layer while flattening the previous one. This effect is not visible on the first layers because there is no heat accumulation at the beginning due to the heat conduction of the substrate. In addition, the walls built with lower energy input seem to be more rectangular than the higher energy ones which are curved and wavier. The edge effect on high energy walls is because at the start and stop points, the bead has less time to cool down. Consequently, the heat builds up and both ends of the wall collapse [16].

On the first micrograph (P250V900), there is an effect every other layer where one layer is thicker than the following. The regularity of this phenomenon might be related to the two-way deposition strategy of this study. Moreover, in each of these 3 cases, fused-on and unmolten particles are visible to the surface of the walls. With low energy input, particles are ejected from the melting pool and fuse to the surface of the deposit. It is not clear for now why the roughness changes every other layer, even if it still might be explained by the phenomena caused by the two-way deposition.
Out of the 12 tests, only 3 of them, P300V900 and P400V1200 and P300V1200, reached approximately the desired final height. The other tests lead to small to important defocusing of the laser during construction. Fig. 3 shows that increasing the energy input (ratio between laser power and scanning speed) leads to final heights that are smaller than the theoretical height. This phenomenon is due to the use of constant laser power during construction. Indeed, along the wall, there will be more and more radial heat dissipation leading to an increase of the melt pool flow due the Marangoni effect [12]. Moreover, it is possible to observe that with small scanning speed V=600 mm/min, the final height of the walls is more affected than with the other speeds. Slow speed can lead to the widening of the melt pool and the flattening of the deposit because of higher dilution. This phenomenon has be shown in [13–15] and is more important when increasing P. Hence, the layers will become uneven, and the overall height will be smaller than planned. The results in Fig. 3 bring to the foreground the impact of key parameters on part’s geometrical features. Nonetheless, an optimized set of parameters, such as P300V900 and P80V1200 in this study, could lead to components with final dimensions matching the model.

Optical microscopy

Examples of optical micrographs are shown Fig. 4. For this wall built with P=300 W and V=900 mm/min, the layers seem regular at the bottom of the part. The middle and top part of the wall have fewer regular layers and the latter form an interlacing. Along a single layer, the height varies. This effect is more visible on Fig. 5.

<figure><img src="image_78.png" /><figcaption>Figure 4. Example of a built wall (P300V900) and optical micrographs from the top middle part and the bottom middle part. Magnification x20</figcaption></figure>

On Fig. 5, the interlacing effect is important for P300V900 and P400V900. The input parameters influence the form and aspect of the built layers. When the energy is lower, for instance at P400V900, the layers are more regular and straighter. These interlacing are due to the important heat input and are visible on every wall except for the ones built with P=250 W. The hypothesis is that when the energy is too high, the deposited layer might significantly melt the previous one and leads to the thickening of the current layer while flattening the previous one. This effect is not visible on the first layers because there is no heat accumulation at the beginning due to the heat conduction of the substrate. In addition, the walls built with lower energy input seem to be more rectangular than the higher energy ones which are curved and wavier. The edge effect on high energy walls is because at the start and stop points, the bead has less time to cool down. Consequently, the heat builds up and both ends of the wall collapse [16].

On the first micrograph (P250V900), there is an effect every other layer where one layer is thicker than the following. The regularity of this phenomenon might be related to the two-way deposition strategy of this study. Moreover, in each of these 3 cases, fused-on and unmolten particles are visible to the surface of the walls. With low energy input, particles are ejected from the melting pool and fuse to the surface of the deposit. It is not clear for now why the roughness changes every other layer, even if it still might be explained by the phenomena caused by the two-way deposition.
<figure><img src="image_78.png" /><figcaption>Figure 5. Optical micrographs of top right corner of walls for P250V900, P300V900 and P400V900. Magnification x30 with partial ring lighting</figcaption></figure>

Thermal sensors measurements

Fig. 6 shows the images taken with the IR camera. The obtained temperatures are no absolute temperatures but relative ones because of the chosen constant value of the emissivity. The raw images without treatment are given Fig.6-a) and c). Fig. 6-b) and d) depict the melting pool enclosed by the blue line. For comparison, images from the 10th layer with P300V900 and P400V900 are shown. Because of higher power input, the heated zone in Fig. 6-d) is larger and contains higher temperatures than in Fig. 6-b). The melting pool has been enclosed and was zoomed out on Fig. 7. An estimation of the height and the width of this melting pool areas was done thanks to the knowledge of the pixel size. This measurement shows that higher power might lead to wider melting pool. The size of the melting pool is a data that can be related to the layer dimensions and will help to understand the aspects observed previously. This treatment of IR images and the robustness of this sensing approach will be confronted to literature, where the IR camera is usually positioned coaxially [16–18].
<figure><img src="image_79.png" /><figcaption>Figure 5. Optical micrographs of top right corner of walls for P250V900, P300V900 and P400V900. Magnification x30 with partial ring lighting</figcaption></figure>

Thermal sensors measurements

Fig. 6 shows the images taken with the IR camera. The obtained temperatures are no absolute temperatures but relative ones because of the chosen constant value of the emissivity. The raw images without treatment are given Fig.6-a) and c). Fig. 6-b) and d) depict the melting pool enclosed by the blue line. For comparison, images from the 10th layer with P300V900 and P400V900 are shown. Because of higher power input, the heated zone in Fig. 6-d) is larger and contains higher temperatures than in Fig. 6-b). The melting pool has been enclosed and was zoomed out on Fig. 7. An estimation of the height and the width of this melting pool areas was done thanks to the knowledge of the pixel size. This measurement shows that higher power might lead to wider melting pool. The size of the melting pool is a data that can be related to the layer dimensions and will help to understand the aspects observed previously. This treatment of IR images and the robustness of this sensing approach will be confronted to literature, where the IR camera is usually positioned coaxially [16–18].
<figure><img src="image_79.png" /><figcaption>Figure 6. a-c) Images obtained with the IR camera for P300V900 and P400V900 respectively. b-d) Magnification of the previous images. The blue lines enclose the melting pools. The white dotted lines represent the edges of the walls.</figcaption></figure>

<figure><img src="image_80.png" /><figcaption>Figure 7. Estimation of melt pool height and width at the start of the 10th layer for a) P300V900 and b) P400V900.</figcaption></figure>

Fig. 8 gives an example of the pyrometer's measurements during construction. For an energy input of 17 J, Fig. 8-a) represents the temperature evolution over time for the entire process while Fig. 8-b) focuses on the 5th and the 6th layers. The dwell time of 3 s can be noticed. At the beginning of construction, the pyrometer is just underneath the deposition and it takes a certain time for it (according to layer height) to start to measure. In addition, not all of the pyrometer's data was exploitable for the 12 tests because of defocusing. When the distance between the nozzle and deposit increases, the pyrometer's measure spot goes upward, the signal starts to saturate and eventually, there is no more signal.
<figure><img src="image_80.png" /><figcaption>Figure 6. a-c) Images obtained with the IR camera for P300V900 and P400V900 respectively. b-d) Magnification of the previous images. The blue lines enclose the melting pools. The white dotted lines represent the edges of the walls.</figcaption></figure>

<figure><img src="image_81.png" /><figcaption>Figure 7. Estimation of melt pool height and width at the start of the 10th layer for a) P300V900 and b) P400V900.</figcaption></figure>

Fig. 8 gives an example of the pyrometer's measurements during construction. For an energy input of 17 J, Fig. 8-a) represents the temperature evolution over time for the entire process while Fig. 8-b) focuses on the 5th and the 6th layers. The dwell time of 3 s can be noticed. At the beginning of construction, the pyrometer is just underneath the deposition and it takes a certain time for it (according to layer height) to start to measure. In addition, not all of the pyrometer's data was exploitable for the 12 tests because of defocusing. When the distance between the nozzle and deposit increases, the pyrometer's measure spot goes upward, the signal starts to saturate and eventually, there is no more signal.
The temperature-time diagram depicts the successive increases and decreases of temperature with some variations during deposition. These variations can be explained by the changes of the melt pool area along the layer making variations of the pyrometer's measurements. This phenomenon is consistent with the work of [19,20]. As shown in Fig. 8-b), the temperature of the 6th layer is higher and reaches a maximum of 1750 °C while the 5th layer reaches a maximum of 1650 °C. Furthermore, there is an increase of the global temperature during the process due to heat accumulation [21,22]. The peaks observed at both ends of the layer are related to the melting pool area that is warmer and wider at these points [16].

<figure><img src="image_81.png" /><figcaption>Figure 8. Pyrometer temperature-time diagrams during deposition with E= 17 J. a) Entire construction. b) Layer 5 and layer 6</figcaption></figure>

Fig. 9 compares the temperature-time diagram of the pyrometer and the IR camera for the 5th and 6th layers. The diagram for the camera (in red) corresponds to the evolution of the maximum temperature over time. Both signals are similar and have the same order of magnitude. However, the pyrometer seems to measure more variations, and this might be caused by the fact that the pyrometer is attached to the deposition head and is probably submitted to more variations. This effect might also be due to the resolution of both sensors. One should also note that there is no noticeable work comparing pyrometry and IR thermography directly on the same build in literature to see if one sensor completes the other.

<figure><img src="image_82.png" /><figcaption>Figure 9. Temperature-time diagrams of 5th and 6th layers for E=17 J. Comparison between pyrometer and IR camera</figcaption></figure>
The temperature-time diagram depicts the successive increases and decreases of temperature with some variations during deposition. These variations can be explained by the changes of the melt pool area along the layer making variations of the pyrometer's measurements. This phenomenon is consistent with the work of [19,20]. As shown in Fig. 8-b), the temperature of the 6th layer is higher and reaches a maximum of 1750 °C while the 5th layer reaches a maximum of 1650 °C. Furthermore, there is an increase of the global temperature during the process due to heat accumulation [21,22]. The peaks observed at both ends of the layer are related to the melting pool area that is warmer and wider at these points [16].

<figure><img src="image_82.png" /><figcaption>Figure 8. Pyrometer temperature-time diagrams during deposition with E= 17 J. a) Entire construction. b) Layer 5 and layer 6</figcaption></figure>

Fig. 9 compares the temperature-time diagram of the pyrometer and the IR camera for the 5th and 6th layers. The diagram for the camera (in red) corresponds to the evolution of the maximum temperature over time. Both signals are similar and have the same order of magnitude. However, the pyrometer seems to measure more variations, and this might be caused by the fact that the pyrometer is attached to the deposition head and is probably submitted to more variations. This effect might also be due to the resolution of both sensors. One should also note that there is no noticeable work comparing pyrometry and IR thermography directly on the same build in literature to see if one sensor completes the other.

<figure><img src="image_83.png" /><figcaption>Figure 9. Temperature-time diagrams of 5th and 6th layers for E=17 J. Comparison between pyrometer and IR camera</figcaption></figure>
## Conclusions and future work

This work presents a thermal multi-sensor approach for a Directed energy deposition process. A series of twelve 316L single-bead walls varying the laser power and scanning speed was realized. Real-time measurements of the temperature with a fixed IR camera and an embedded pyrometer have been done. The aim of this study was to validate the relevance of the method, the sensors, and their combination. Each sensor gave exploitable results that were completed by post-process analyzes of geometrical features of the wall. With the design of experiments and the observations done, it is possible to relate some of the process parameters to the thermal distribution and eventually to the aspect of the wall:

* The increase of energy input has an influence on the size of the heated zone and the melting pool area which directly impacts the layer geometry. A high heat input leads to irregular, wavy and thick layers leading afterwards to a diminution of the overall height of the wall.

* Surface roughness of the wall is diminished by high energy input. It appeared that deposition strategy also has an important role in the parts final properties.

* The defocusing due to high energy input hindered a part of the measurements but the rest was exploitable. The signals depicted the influence of process parameters over heat accumulation and temperatures. Edge effects also could be related to the temperature peaks at both ends of the layer on the pyrometer's signal.

* The IR camera's images can be used to extract temperature data as well as the geometrical features of the heated zone and the melting area. This data corroborates the aspects of the wall and the measurements done with the pyrometer.

* The fixed position of the camera prevents it from getting some of the small variations observable with the pyrometer. It supports the use of both sensor for a more complete understanding of thermal phenomena. This approach will help to enhance the properties of DEDed parts.

Future work will be dedicated to several aspects:

* The development of an algorithm to have the evolution of the melting pool size

* The completion of the design of experiments with statistical study

* Further characterizations of the constructed wall thanks to cross-sectional views

## References

[1] P. Gradl, D.C. Tinker, A. Park, O.R. Mireles, M. Garcia, R. Wilkerson, C. McKinney, Robust Metal Additive Manufacturing Process Selection and Development for Aerospace Components, J. of Materi Eng and Perform 31 (2022) 6013-6044. https://doi.org/10.1007/s11665-022-06850-0

[2] B. Blakey-Milner, P. Gradl, G. Snedden, M. Brooks, J. Pitot, E. Lopez, M. Leary, F. Berto, A. du Plessis, Metal additive manufacturing in aerospace: A review, Materials & Design 209 (2021) 110008. https://doi.org/10.1016/j.matdes.2021.110008

[3] D. Herzog, V. Seyda, E. Wycisk, C. Emmelmann, Additive manufacturing of metals, Acta Materialia 117 (2016) 371-392. https://doi.org/10.1016/j.actamat.2016.07.019

[4] L. Zhu, P. Xue, Q. Lan, G. Meng, Y. Ren, Z. Yang, P. Xu, Z. Liu, Recent research and development status of laser cladding: A review, Optics & Laser Technology 138 (2021) 106915. https://doi.org/10.1016/j.optlastec.2021.106915

[5] S. Guo, C. Zamiela, L. Bian, Knowledge-transfer-enabled porosity prediction for new part geometry in laser metal deposition, Journal of Manufacturing Processes 103 (2023) 64-77. https://doi.org/10.1016/j.jmapro.2023.08.002
## Conclusions and future work

This work presents a thermal multi-sensor approach for a Directed energy deposition process. A series of twelve 316L single-bead walls varying the laser power and scanning speed was realized. Real-time measurements of the temperature with a fixed IR camera and an embedded pyrometer have been done. The aim of this study was to validate the relevance of the method, the sensors, and their combination. Each sensor gave exploitable results that were completed by post-process analyzes of geometrical features of the wall. With the design of experiments and the observations done, it is possible to relate some of the process parameters to the thermal distribution and eventually to the aspect of the wall:

* The increase of energy input has an influence on the size of the heated zone and the melting pool area which directly impacts the layer geometry. A high heat input leads to irregular, wavy and thick layers leading afterwards to a diminution of the overall height of the wall.

* Surface roughness of the wall is diminished by high energy input. It appeared that deposition strategy also has an important role in the parts final properties.

* The defocusing due to high energy input hindered a part of the measurements but the rest was exploitable. The signals depicted the influence of process parameters over heat accumulation and temperatures. Edge effects also could be related to the temperature peaks at both ends of the layer on the pyrometer's signal.

* The IR camera's images can be used to extract temperature data as well as the geometrical features of the heated zone and the melting area. This data corroborates the aspects of the wall and the measurements done with the pyrometer.

* The fixed position of the camera prevents it from getting some of the small variations observable with the pyrometer. It supports the use of both sensor for a more complete understanding of thermal phenomena. This approach will help to enhance the properties of DEDed parts.

Future work will be dedicated to several aspects:

* The development of an algorithm to have the evolution of the melting pool size

* The completion of the design of experiments with statistical study

* Further characterizations of the constructed wall thanks to cross-sectional views

## References

[1] P. Gradl, D.C. Tinker, A. Park, O.R. Mireles, M. Garcia, R. Wilkerson, C. Mckinney, Robust Metal Additive Manufacturing Process Selection and Development for Aerospace Components, J. of Materi Eng and Perform 31 (2022) 6013–6044. https://doi.org/10.1007/s11665-022-06850-0

[2] B. Blakey-Milner, P. Gradl, G. Snedden, M. Brooks, J. Pitot, E. Lopez, M. Leary, F. Berto, A. du Plessis, Metal additive manufacturing in aerospace: A review, Materials & Design 209 (2021) 110008. https://doi.org/10.1016/j.matdes.2021.110008

[3] D. Herzog, V. Seyda, E. Wycisk, C. Emmelmann, Additive manufacturing of metals, Acta Materialia 117 (2016) 371–392. https://doi.org/10.1016/j.actamat.2016.07.019

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[12] N. Ali, L. Tomesani, A. Ascari, A. Fortunato, Fabrication of Thin Walls with and without Close Loop Control as a Function of Scan Strategy Via Direct Energy Deposition, Lasers Manuf. Mater. Process. 9 (2022) 81-101. https://doi.org/10.1007/s40516-022-00164-8

[13] B.T. Gibson, Y.K. Bandari, B.S. Richardson, A.C. Roschli, B.K. Post, M.C. Borish, A. Thornton, W.C. Henry, M. Lamsey, L.J. Love, Melt Pool Monitoring for Control and Data Analytics in Large-Scale Metal Additive Manufacturing, in: University of Texas at Austin, 2019. https://doi.org/10.26153/tsw/17371

[14] M.F. Schneider, Laser Cladding with Powder: Effect of Some Machining Parameters on Clad Properties, 1998.

[15] B. Carcel, J. Sampedro, I. Perez, E. Fernandez, J.A. Ramos, Improved laser metal deposition (LMD) of nickel base superalloys by pyrometry process control, in: XVIII International Symposium on Gas Flow, Chemical Lasers, and High-Power Lasers, SPIE, 2010: pp. 643-651. https://doi.org/10.1117/12.878749

[16] D. Hu, R. Kovacevic, Sensing, modeling and control for laser-based additive manufacturing, International Journal of Machine Tools and Manufacture 43 (2003) 51-60. https://doi.org/10.1016/S0890-6955(02)00163-3

[17] M. Akbari, R. Kovacevic, Closed loop control of melt pool width in robotized laser powder- directed energy deposition process, Int J Adv Manuf Technol 104 (2019) 2887-2898. https://doi.org/10.1007/s00170-019-04195-y

[18] Y. Ding, J. Warton, R. Kovacevic, Development of sensing and control system for robotized laser-based direct metal addition system, Additive Manufacturing 10 (2016) 24-35. https://doi.org/10.1016/j.addma.2016.01.002

[19] A. Zapata, C. Bernauer, M. Hell, M.F. Zaeh, Studies on the direction-independent temperature measurement of a coaxial laser metal deposition process with wire, (n.d.).
[6] M. Liu, A. Kumar, S. Bukkapatnam, M. Kuttolamadom, A Review of the Anomalies in Directed Energy Deposition (DED) Processes & Potential Solutions - Part Quality & Defects, Procedia Manufacturing 53 (2021) 507-518. https://doi.org/10.1016/j.promfg.2021.06.093

[7] M. Mazzarisi, A. Angelastro, M. Latte, T. Colucci, F. Palano, S.L. Campanelli, Thermal monitoring of laser metal deposition strategies using infrared thermography, Journal of Manufacturing Processes 85 (2023) 594-611. https://doi.org/10.1016/j.jmapro.2022.11.067

[8] A.J. Myers, G. Quirarte, J.L. Beuth, J.A. Malen, Two-color thermal imaging of the melt pool in powder-blown laser-directed energy deposition, Additive Manufacturing (2023) 103855. https://doi.org/10.1016/j.addma.2023.103855

[9] S.J. Altenburg, A. Straße, A. Gumenyuk, C. Maierhofer, In-situ monitoring of a laser metal deposition (LMD) process: comparison of MWIR, SWIR and high-speed NIR thermography, Quantitative InfraRed Thermography Journal 19 (2022) 97-114. https://doi.org/10.1080/17686733.2020.1829889

[10] C. Hagenlocher, P. O'Toole, W. Xu, M. Brandt, M. Easton, A. Molotnikov, The Effect of Heat Accumulation on the Local Grain Structure in Laser-Directed Energy Deposition of Aluminium, Metals 12 (2022) 1601. https://doi.org/10.3390/met12101601

[11] A. Chabot, M. Rauch, J.-Y. Hascoët, Towards a multi-sensor monitoring methodology for AM metallic processes, Welding in the World 63 (2019) 759-769. https://doi.org/10.1007/s40194-019-00705-4

[12] N. Ali, L. Tomesani, A. Ascari, A. Fortunato, Fabrication of Thin Walls with and without Close Loop Control as a Function of Scan Strategy Via Direct Energy Deposition, Lasers Manuf. Mater. Process. 9 (2022) 81-101. https://doi.org/10.1007/s40516-022-00164-8

[13] B.T. Gibson, Y.K. Bandari, B.S. Richardson, A.C. Roschli, B.K. Post, M.C. Borish, A. Thornton, W.C. Henry, M. Lamsey, L.J. Love, Melt Pool Monitoring for Control and Data Analytics in Large-Scale Metal Additive Manufacturing, in: University of Texas at Austin, 2019. https://doi.org/10.26153/tsw/17371

[14] M.F. Schneider, Laser Cladding with Powder: Effect of Some Machining Parameters on Clad Properties, 1998.

[15] B. Carcel, J. Sampedro, I. Perez, E. Fernandez, J.A. Ramos, Improved laser metal deposition (LMD) of nickel base superalloys by pyrometry process control, in: XVIII International Symposium on Gas Flow, Chemical Lasers, and High-Power Lasers, SPIE, 2010: pp. 643-651. https://doi.org/10.1117/12.878749

[16] D. Hu, R. Kovacevic, Sensing, modeling and control for laser-based additive manufacturing, International Journal of Machine Tools and Manufacture 43 (2003) 51-60. https://doi.org/10.1016/S0890-6955(02)00163-3

[17] M. Akbari, R. Kovacevic, Closed loop control of melt pool width in robotized laser powder- directed energy deposition process, Int J Adv Manuf Technol 104 (2019) 2887-2898. https://doi.org/10.1007/s00170-019-04195-y

[18] Y. Ding, J. Warton, R. Kovacevic, Development of sensing and control system for robotized laser-based direct metal addition system, Additive Manufacturing 10 (2016) 24-35. https://doi.org/10.1016/j.addma.2016.01.002

[19] A. Zapata, C. Bernauer, M. Hell, M.F. Zaeh, Studies on the direction-independent temperature measurement of a coaxial laser metal deposition process with wire, (n.d.).
[20] I. Smurov, M. Doubenskaia, A. Zaitsev, Comprehensive analysis of laser cladding by means of optical diagnostics and numerical simulation, Surface and Coatings Technology 220 (2013) 112-121. https://doi.org/10.1016/j.surfcoat.2012.10.053

[21] B. Wu, D. Ding, Z. Pan, D. Cuiuri, H. Li, J. Han, Z. Fei, Effects of heat accumulation on the arc characteristics and metal transfer behavior in Wire Arc Additive Manufacturing of Ti6Al4V, Journal of Materials Processing Technology 250 (2017) 304-312. https://doi.org/10.1016/j.jmatprotec.2017.07.037

[22] D. Yang, G. Wang, G. Zhang, Thermal analysis for single-pass multi-layer GMAW based additive manufacturing using infrared thermography, Journal of Materials Processing Technology 244 (2017) 215-224. https://doi.org/10.1016/j.jmatprotec.2017.01.024
[20] I. Smurov, M. Doubenskaia, A. Zaitsev, Comprehensive analysis of laser cladding by means of optical diagnostics and numerical simulation, Surface and Coatings Technology 220 (2013) 112-121. https://doi.org/10.1016/j.surfcoat.2012.10.053

[21] B. Wu, D. Ding, Z. Pan, D. Cuiuri, H. Li, J. Han, Z. Fei, Effects of heat accumulation on the arc characteristics and metal transfer behavior in Wire Arc Additive Manufacturing of Ti6Al4V, Journal of Materials Processing Technology 250 (2017) 304-312. https://doi.org/10.1016/j.jmatprotec.2017.07.037

[22] D. Yang, G. Wang, G. Zhang, Thermal analysis for single-pass multi-layer GMAW based additive manufacturing using infrared thermography, Journal of Materials Processing Technology 244 (2017) 215-224. https://doi.org/10.1016/j.jmatprotec.2017.01.024
# Realization of functionally graded components with an optimized hybrid additive laminated tooling method

DARDAEI JOGHAN Hamed¹,ª*, AGBOOLA Ololade¹,ᵇ and TEKKAYA A. Erman¹,ᶜ

¹Institute of Forming Technology and Lightweight Components (IUL), TU Dortmund University, Baroper Str. 303, Dortmund 44227, Germany

ªhamed.dardaei@iul.tu-dortmund.de, ᵇololade.agboola@tu-dortmund.de,

ᶜerman.tekkaya@iul.tu-dortmund.de

**Keywords:** Laser Metal Deposition, Sheet Lamination, Press Hardening, Warm Forming

**Abstract.** The tool tempering method is used to manufacture the functionally graded components. In the manufactured combined tool, one side was enhanced with cooling channels to perform the press hardening process, and the other side of the tool included heating cartridges to practice warm forming. Hybrid additive laminated tooling is used to manufacture the combined tool in a short time, while conventional subtracting methods, such as milling, are costly and time-consuming. Initially, the cooling rate and the heat transfer coefficient of the laser metal deposited area by Ferro55 powder are determined. Also, functionally graded components are manufactured in which, on one side, the 22MnB5 sheets are press-hardened, whereas, on the other side, the blanks were warm formed at different elevated temperatures (150°C, 250°C, 350°C). The results show that besides the concept validation, the cooling rate on the press hardening side is higher than 27 K/s, and hardness values up to ca. 480 HV10 were achieved, while, in the wall of the formed part on the heating side, the hardness is below 300 HV10.

**Introduction**

The aerospace and automotive industries require components with graded mechanical, thermal, and tribological properties known as functionally graded components. These components provide multi-functional properties suitable for engineering applications. Thermal process control is one common production method for manufacturing tailored parts. In the tool tempering method, a combined die simultaneously has different temperatures on each side. As a result, after forming the blank in the combined die, the microstructure and the mechanical properties of the component at different sections are dissimilar [1].

In the indirect press hardening process, boron alloy steel blanks, mostly 22MnB5, are heated to the austenitization temperature (about 900 °C) and then transferred to the die, where they are cooled using a technique called die quenching, in which the forming and quenching processes occur simultaneously. The high blank temperature reduces the flow stress (lower forming forces) and increases the formability during the forming. The main advantages of this process are the possibility of producing parts with low weight (thin sheets), high strength (approx. 1500 MPa), and low spring back. From a tooling perspective, the key parameter in the press-hardening process is using the enhanced cooling system to achieve a cooling rate of at least 27 K/s [1]. The conventional method for manufacturing press-hardened dies with complex cooling channels includes machining, hardening, and, in most cases, coating. The most common technique is to design press hardening or hot stamping dies by integrating cooling channels drilled in multiple segments of the die body, which are then joined together to form a single unit. However, proper sealing is the main challenge. A further well-established technique is shell cooling, where the intended component is molded to its final shape on a shell die, with cooling fluid passing through the shell and core dies. An additional approach involves casting the die around cooling profiles. The high manufacturing time and cost are the main drawbacks of these methods [2].

<figure><img src="image_83.png" /></figure>

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under license by Materials Research Forum LLC.
# Realization of functionally graded components with an optimized hybrid additive laminated tooling method

DARDAEI JOGHAN Hamed¹,ª*, AGBOOLA Ololade¹,ᵇ and TEKKAYA A. Erman¹,ᶜ

¹Institute of Forming Technology and Lightweight Components (IUL), TU Dortmund University, Baroper Str. 303, Dortmund 44227, Germany

ªhamed.dardaei@iul.tu-dortmund.de, ᵇololade.agboola@tu-dortmund.de,

ᶜerman.tekkaya@iul.tu-dortmund.de

**Keywords:** Laser Metal Deposition, Sheet Lamination, Press Hardening, Warm Forming

**Abstract.** The tool tempering method is used to manufacture the functionally graded components. In the manufactured combined tool, one side was enhanced with cooling channels to perform the press hardening process, and the other side of the tool included heating cartridges to practice warm forming. Hybrid additive laminated tooling is used to manufacture the combined tool in a short time, while conventional subtracting methods, such as milling, are costly and time-consuming. Initially, the cooling rate and the heat transfer coefficient of the laser metal deposited area by Ferro55 powder are determined. Also, functionally graded components are manufactured in which, on one side, the 22MnB5 sheets are press-hardened, whereas, on the other side, the blanks were warm formed at different elevated temperatures (150°C, 250°C, 350°C). The results show that besides the concept validation, the cooling rate on the press hardening side is higher than 27 K/s, and hardness values up to ca. 480 HV10 were achieved, while, in the wall of the formed part on the heating side, the hardness is below 300 HV10.

**Introduction**

The aerospace and automotive industries require components with graded mechanical, thermal, and tribological properties known as functionally graded components. These components provide multi-functional properties suitable for engineering applications. Thermal process control is one common production method for manufacturing tailored parts. In the tool tempering method, a combined die simultaneously has different temperatures on each side. As a result, after forming the blank in the combined die, the microstructure and the mechanical properties of the component at different sections are dissimilar [1].

In the indirect press hardening process, boron alloy steel blanks, mostly 22MnB5, are heated to the austenitization temperature (about 900 °C) and then transferred to the die, where they are cooled using a technique called die quenching, in which the forming and quenching processes occur simultaneously. The high blank temperature reduces the flow stress (lower forming forces) and increases the formability during the forming. The main advantages of this process are the possibility of producing parts with low weight (thin sheets), high strength (approx. 1500 MPa), and low spring back. From a tooling perspective, the key parameter in the press-hardening process is using the enhanced cooling system to achieve a cooling rate of at least 27 K/s [1]. The conventional method for manufacturing press-hardened dies with complex cooling channels includes machining, hardening, and, in most cases, coating. The most common technique is to design press hardening or hot stamping dies by integrating cooling channels drilled in multiple segments of the die body, which are then joined together to form a single unit. However, proper sealing is the main challenge. A further well-established technique is shell cooling, where the intended component is molded to its final shape on a shell die, with cooling fluid passing through the shell and core dies. An additional approach involves casting the die around cooling profiles. The high manufacturing time and cost are the main drawbacks of these methods [2].

<figure><img src="image_84.png" /></figure>

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under license by Materials Research Forum LLC.
Additive manufacturing recently provided the possibility of manufacturing complex shapes that conventional methods cannot produce. The most common additive manufacturing method is powder bed fusion (PBF) to realize the complex conformal cooling channels in the dies or parts. In the PBF, the focused energy source (laser beam or electron beam) melts the powder layer by layer to build the final geometry. Müller et al. [3] have produced a tool insert with numerically optimized cooling channels to develop a new cooling system for hot forming. The die is produced by combining the conventional method and PBF. Gebauer et al. [4] focused on tool optimization for press hardening, where the die has a conformal cooling system. The press hardening experiments were conducted with an additively manufactured (PBF) tool. Yet, the main drawback of the PBF is long manufacturing time.

A solution for rapid tooling is the utilization of sheet lamination. Bryden et al. [5] showed the utilization of complex conformal cooling channels in metal laminated tools for the aerospace industry, where the tools should show a functionality test at 900 °C. The main issues of conventional laminated tooling technology are the poor bonding of laminae, challenges in removing the stair step effect, and hardening of the tool surface. Dardaei Joghan et al. [6] performed an investigation on a combination of the sheet lamination method with the direct energy deposition (DED) process, which is patented by Hölker and Tekkaya [7]. In this process route, the tool's core is manufactured out of laminated sheets, and the laser metal deposition (LMD) process is utilized to reduce the stair-step effect (Fig. 1). This concept is successfully used to manufacture the deep drawing dies. In this method, the hardening step is no longer required, and it allows the placement of the sensors or other functional elements, such as cooling or heating elements, inside the tool during manufacturing.

<figure><img src="image_84.png" /><figcaption>Figure 1: Process route of the hybrid additive laminated tooling [6]</figcaption></figure>

The present work aims to prove the manufacturing tool tempering concept by hybrid additive laminated tooling as introduced in Fig. 1. The main motivation is to manufacture the hybrid die in a short time. For this, a hot stamping insert with the near-surface cooling channels on one side and a warm forming insert with two heating cartridges on the other are manufactured to realize the functionally graded components. The following are the manufacturing and measurement steps, as well as the preliminary results of the formed components.

## Experimental work

In all experiments, a 22MnB5 steel sheets with a thickness of 1.5 mm is used as a blank for forming and determining the heat transfer coefficient. The as-received 22MnB5 blank had a hardness of 134 HV10. The ferritic HC420LA (1.0556) sheets are used with thicknesses of 0.5 and 1.0 mm, and the hot-rolled and high-strength steel S355MC (1.0976) with a thickness of 2.0 mm is used as tool laminae. The stair step area is filled with Ferro55 powder (tool steel) with a grain size range of 45 to 80 µm. The chemical composition of the powder is mentioned in Table 1. The laser metal deposition and post-processing are carried out with the Lasertec 65 3D hybrid machine from the company DMG MORI with the laser head COAX14. The laser diameter of 1.6 mm with a focus distance of 7 mm is used.
Additive manufacturing recently provided the possibility of manufacturing complex shapes that conventional methods cannot produce. The most common additive manufacturing method is powder bed fusion (PBF) to realize the complex conformal cooling channels in the dies or parts. In the PBF, the focused energy source (laser beam or electron beam) melts the powder layer by layer to build the final geometry. Müller et al. [3] have produced a tool insert with numerically optimized cooling channels to develop a new cooling system for hot forming. The die is produced by combining the conventional method and PBF. Gebauer et al. [4] focused on tool optimization for press hardening, where the die has a conformal cooling system. The press hardening experiments were conducted with an additively manufactured (PBF) tool. Yet, the main drawback of the PBF is long manufacturing time.

A solution for rapid tooling is the utilization of sheet lamination. Bryden et al. [5] showed the utilization of complex conformal cooling channels in metal laminated tools for the aerospace industry, where the tools should show a functionality test at 900 °C. The main issues of conventional laminated tooling technology are the poor bonding of laminae, challenges in removing the stair step effect, and hardening of the tool surface. Dardaei Joghan et al. [6] performed an investigation on a combination of the sheet lamination method with the direct energy deposition (DED) process, which is patented by Hölker and Tekkaya [7]. In this process route, the tool's core is manufactured out of laminated sheets, and the laser metal deposition (LMD) process is utilized to reduce the stair-step effect (Fig. 1). This concept is successfully used to manufacture the deep drawing dies. In this method, the hardening step is no longer required, and it allows the placement of the sensors or other functional elements, such as cooling or heating elements, inside the tool during manufacturing.

<figure><img src="image_85.png" /><figcaption>Figure 1: Process route of the hybrid additive laminated tooling [6]</figcaption></figure>

The present work aims to prove the manufacturing tool tempering concept by hybrid additive laminated tooling as introduced in Fig. 1. The main motivation is to manufacture the hybrid die in a short time. For this, a hot stamping insert with the near-surface cooling channels on one side and a warm forming insert with two heating cartridges on the other are manufactured to realize the functionally graded components. The following are the manufacturing and measurement steps, as well as the preliminary results of the formed components.

## Experimental work

In all experiments, a 22MnB5 steel sheets with a thickness of 1.5 mm is used as a blank for forming and determining the heat transfer coefficient. The as-received 22MnB5 blank had a hardness of 134 HV10. The ferritic HC420LA (1.0556) sheets are used with thicknesses of 0.5 and 1.0 mm, and the hot-rolled and high-strength steel S355MC (1.0976) with a thickness of 2.0 mm is used as tool laminae. The stair step area is filled with Ferro55 powder (tool steel) with a grain size range of 45 to 80 µm. The chemical composition of the powder is mentioned in Table 1. The laser metal deposition and post-processing are carried out with the Lasertec 65 3D hybrid machine from the company DMG MORI with the laser head COAX14. The laser diameter of 1.6 mm with a focus distance of 7 mm is used.
<table><caption>Table 1: Ferro55 powder chemical composition in weight percentage %</caption><thead><tr><th>Powder</th><th>C</th><th>Cr</th><th>Mo</th><th>Si</th><th>Mn</th><th>Fe</th></tr></thead><tbody><tr><td>Ferro55</td><td>0.35</td><td>7</td><td>2.2</td><td>0.3</td><td>1.1</td><td>rest</td></tr></tbody></table>

### Determination of the cooling rate and heat transfer coefficient

To determine the effect of the number of deposited layers on the cooling rate and heat transfer coefficient, dies are manufactured with cooling channels and different layers of the depositions with Ferro55 powder (tool steel). Two dies (upper and bottom) are manufactured from horizontally laminated 2 mm thick sheets (S355MC) laser cut by the Trumpf Lasercell 1005 machine available at IUL. The laminated tool is assembled on a 10 mm base sheet (S355MC). Three cooling channels made of copper pipes are used. Since the higher heat transfer can be achieved by a rectangular cooling profile (due to higher contact area), a circular tube with a 12 mm diameter was formed with biaxial compression equipment (self-made at IUL) to 10 × 10 mm² square cross-section profile (Fig. 2a). After assembly, the surface of the dies are deposited in three times. The first time, only one layer is deposited. Afterward, the upper and bottom dies are assembled on the Zwick250 universal testing machine from the company Zwick Roell, and the heat transfer coefficient and the cooling rate are determined. Then, the setup is disassembled and placed on the Lasertec 65 machine for the next layer's deposition. This procedure is repeated till the maximum deposition layer of three with a laser power of 800W, feed rate of 1000 mm/min, and powder mass flow of 4 g/min. The deposition area was 72.5 × 47.5 mm². To avoid the thin sheet deformation during the deposition, in-situ cooling is applied to reduce the sheet's thermal deformation. During the experiments, the water is circulated in the cooling channels by cooling aggregate from the company Walter Heller GmbH (Fig. 2b). A 4 mm thick cover sheet is also used to reduce the thermal deformation of the thin sheets during the deposition (Fig. 2c).

<figure><img src="image_85.png" /><figcaption>Figure 2: (a) Placing the cooling channels, (b) deposition of the Ferro55 layers on the dies for determination of the coefficient of heat transfer and the cooling rate, (c) using of a cover sheet during the deposition</figcaption></figure>

A self-made setup at IUL is used to determine the cooling rate and the heat transfer coefficient (Fig. 3). The whole setup is assembled on the Zwick250, and the 22MnB5 blanks were heated for 5 minutes to austenitization temperature (900 °C) in a Nabertherm N161 furnace. The blanks were then transferred manually to the setup and placed on the bottom die. The transfer and the placement time were under five seconds. The temperature loss by the transport was around 175°C. Then, the
<table><caption>Table 1: Ferro55 powder chemical composition in weight percentage %</caption><thead><tr><th>Powder</th><th>C</th><th>Cr</th><th>Mo</th><th>Si</th><th>Mn</th><th>Fe</th></tr></thead><tbody><tr><td>Ferro55</td><td>0.35</td><td>7</td><td>2.2</td><td>0.3</td><td>1.1</td><td>rest</td></tr></tbody></table>

### Determination of the cooling rate and heat transfer coefficient

To determine the effect of the number of deposited layers on the cooling rate and heat transfer coefficient, dies are manufactured with cooling channels and different layers of the depositions with Ferro55 powder (tool steel). Two dies (upper and bottom) are manufactured from horizontally laminated 2 mm thick sheets (S355MC) laser cut by the Trumpf Lasercell 1005 machine available at IUL. The laminated tool is assembled on a 10 mm base sheet (S355MC). Three cooling channels made of copper pipes are used. Since the higher heat transfer can be achieved by a rectangular cooling profile (due to higher contact area), a circular tube with a 12 mm diameter was formed with biaxial compression equipment (self-made at IUL) to 10 × 10 mm² square cross-section profile (Fig. 2a). After assembly, the surface of the dies are deposited in three times. The first time, only one layer is deposited. Afterward, the upper and bottom dies are assembled on the Zwick250 universal testing machine from the company Zwick Roell, and the heat transfer coefficient and the cooling rate are determined. Then, the setup is disassembled and placed on the Lasertec 65 machine for the next layer's deposition. This procedure is repeated till the maximum deposition layer of three with a laser power of 800W, feed rate of 1000 mm/min, and powder mass flow of 4 g/min. The deposition area was 72.5 × 47.5 mm². To avoid the thin sheet deformation during the deposition, in-situ cooling is applied to reduce the sheet's thermal deformation. During the experiments, the water is circulated in the cooling channels by cooling aggregate from the company Walter Heller GmbH (Fig. 2b). A 4 mm thick cover sheet is also used to reduce the thermal deformation of the thin sheets during the deposition (Fig. 2c).

<figure><img src="image_86.png" /><figcaption>Figure 2: (a) Placing the cooling channels, (b) deposition of the Ferro55 layers on the dies for determination of the coefficient of heat transfer and the cooling rate, (c) using of a cover sheet during the deposition</figcaption></figure>

A self-made setup at IUL is used to determine the cooling rate and the heat transfer coefficient (Fig. 3). The whole setup is assembled on the Zwick250, and the 22MnB5 blanks were heated for 5 minutes to austenitization temperature (900 °C) in a Nabertherm N161 furnace. The blanks were then transferred manually to the setup and placed on the bottom die. The transfer and the placement time were under five seconds. The temperature loss by the transport was around 175°C. Then, the
bottom die is closed with a speed of 10 mm/s, and a contact pressure of 10 MPa for 15 seconds is applied. During the experiments, the thermographic camera VarioCam HD head 680 S from the company Infratec GmbH measured the blanks' and dies' temperatures. The emissivity value is experimentally determined and set at 0.85. Type-K thermocouples with a 2 mm diameter also measured the temperature near the surface (0.5 mm distance). The geometrical accuracy measurements of the formed parts were measured by a 3D-Profilometer, VR-5200, from the company Keyence.

<figure><img src="image_86.png" /><figcaption>Figure 3: Test setup for determination of the heat transfer coefficient</figcaption></figure>

### Manufacturing the hybrid tool

The combined tool (hot stamping and warm forming) is manufactured for the demonstrator component (Fig. 4a) with a radius of 6 mm, as illustrated schematically in Fig. 4b. The position of the six cooling channels with a 6 × 6 mm² square cross-section and the two heating cartridges are shown in Fig. 4c.

<figure><img src="image_87.png" /><figcaption>Figure 4: (a) Nominal geometry for demonstrator component, (b) perspective view of the hybrid tool, (c) cross-section of the hybrid tool</figcaption></figure>

During the placement of cooling channels, the most possible position close to the tool surface is selected. The cooling channels are up to 73% closer to the forming zone than a similar conventionally manufactured die. The laminae are positioned in the proper location with positioning pins. On the heating side, two heating cartridges out of stainless steel with a 6 × 6 mm² square cross-section, a length of 400 mm, and a maximum power of 650W at 230V are placed (Fig. 4c). The cartridges are formable with a minimum bending radius of 23 mm from the company
bottom die is closed with a speed of 10 mm/s, and a contact pressure of 10 MPa for 15 seconds is applied. During the experiments, the thermographic camera VarioCam HD head 680 S from the company Infratec GmbH measured the blanks' and dies' temperatures. The emissivity value is experimentally determined and set at 0.85. Type-K thermocouples with a 2 mm diameter also measured the temperature near the surface (0.5 mm distance). The geometrical accuracy measurements of the formed parts were measured by a 3D-Profilometer, VR-5200, from the company Keyence.

<figure><img src="image_87.png" /><figcaption>Figure 3: Test setup for determination of the heat transfer coefficient</figcaption></figure>

### Manufacturing the hybrid tool

The combined tool (hot stamping and warm forming) is manufactured for the demonstrator component (Fig. 4a) with a radius of 6 mm, as illustrated schematically in Fig. 4b. The position of the six cooling channels with a 6 × 6 mm² square cross-section and the two heating cartridges are shown in Fig. 4c.

<figure><img src="image_88.png" /><figcaption>Figure 4: (a) Nominal geometry for demonstrator component, (b) perspective view of the hybrid tool, (c) cross-section of the hybrid tool</figcaption></figure>

During the placement of cooling channels, the most possible position close to the tool surface is selected. The cooling channels are up to 73% closer to the forming zone than a similar conventionally manufactured die. The laminae are positioned in the proper location with positioning pins. On the heating side, two heating cartridges out of stainless steel with a 6 × 6 mm² square cross-section, a length of 400 mm, and a maximum power of 650W at 230V are placed (Fig. 4c). The cartridges are formable with a minimum bending radius of 23 mm from the company
Heizelemente Industrie Maxiwatt. The stair-step areas for both sides are laser metal deposited. As mentioned in [6], a two-step strategy is used for deposition. An offset of 1 mm is applied to ensure enough space to fill the deposited material during the LMD. The process parameters during deposition and laser polishing are in Table 2. The die has a corner outer radius $R_o = 6$ mm, so an optimized sheet arrangement based on the economic criterion [8] is used. Using the economic criterion reduces the 520 possible sheet combinations (0.5, 1, and 2 mm sheets) for $R_o$ of 6 mm to 24. The order of the sheets is 1, 1, 0.5, 1, 0.5, and 2 mm (from bottom to top of the tool surface in the radius area). This combination is preferred because it is preferable to have a thicker sheet at the top, as the top sheet is subjected to the highest thermal load during LMD and post-processing (laser polishing). The remaining sections (i.e., after the die corner radius) are discretized to match the cooling channels' preconceived design and the heating cartridges' design. After the deposition of both sides, laser polishing is used to improve the surface roughness with the process parameters in Table 2.

<table><caption>Table 2: Process parameters for laser metal deposition and laser polishing</caption><thead><tr><th>Process</th><th>Step</th><th>Power P (W)</th><th>Feed rate f (mm/min)</th><th>Powder mass flow ṁ (g/min)</th></tr></thead><tbody><tr><td rowspan="2">Laser metal deposition</td><td>Bonding</td><td>1400</td><td>1000</td><td>7</td></tr><tr><td>Filling</td><td>1200</td><td>1000</td><td>4</td></tr><tr><td>Laser polishing</td><td>Post-processing</td><td>800</td><td>400</td><td>-</td></tr></tbody></table>

Both sides are assembled on two 10 mm thick base plates (in sum 20 mm thickness). In the separation area of the assembled combined die (except for face sheets), isolator plates were used on both sides to protect the heat exchange with a press-hardened segment (Fig. 5).

<figure><img src="image_88.png" /><figcaption>Figure 5: Manufactured hybrid die for the production of the functionally graded components</figcaption></figure>

Forming process

The forming experiments were performed in HYDRAP HPSZK 100-1000/650 hydraulic drawing press with a maximum force of 1000 kN from SCHULER-HYDRAP GmbH & Co. KG. The 22MnB5 blanks with 184 × 95 mm² dimensions were formed. The blanks were formed at a speed of 30 mm/s with two different blank holder forces, 50 kN and 75 kN. The blanks were heated in a chamber furnace at 900°C for five minutes to ensure complete austenitization (i.e., change in crystal structure from ferrite to austenite) and manually positioned on the die with a temperature drop of 120°C. A contact thermometer is used to monitor the blank temperature before and after forming. A thermal imaging camera is used to observe the temperature in different die sections. Ionized water is pumped through the cooled section of the die. A second pump from Hyfra Industriekühlanlagen GmbH was also used to cool the punch. Due to technical issues, cooling only
Heizelemente Industrie Maxiwatt. The stair-step areas for both sides are laser metal deposited. As mentioned in [6], a two-step strategy is used for deposition. An offset of 1 mm is applied to ensure enough space to fill the deposited material during the LMD. The process parameters during deposition and laser polishing are in Table 2. The die has a corner outer radius $R_o = 6$ mm, so an optimized sheet arrangement based on the economic criterion [8] is used. Using the economic criterion reduces the 520 possible sheet combinations (0.5, 1, and 2 mm sheets) for $R_o$ of 6 mm to 24. The order of the sheets is 1, 1, 0.5, 1, 0.5, and 2 mm (from bottom to top of the tool surface in the radius area). This combination is preferred because it is preferable to have a thicker sheet at the top, as the top sheet is subjected to the highest thermal load during LMD and post-processing (laser polishing). The remaining sections (i.e., after the die corner radius) are discretized to match the cooling channels' preconceived design and the heating cartridges' design. After the deposition of both sides, laser polishing is used to improve the surface roughness with the process parameters in Table 2.

<table><caption>Table 2: Process parameters for laser metal deposition and laser polishing</caption><thead><tr><th>Process</th><th>Step</th><th>Power P (W)</th><th>Feed rate f (mm/min)</th><th>Powder mass flow ṁ (g/min)</th></tr></thead><tbody><tr><td rowspan="2">Laser metal deposition</td><td>Bonding</td><td>1400</td><td>1000</td><td>7</td></tr><tr><td>Filling</td><td>1200</td><td>1000</td><td>4</td></tr><tr><td>Laser polishing</td><td>Post-processing</td><td>800</td><td>400</td><td>-</td></tr></tbody></table>

Both sides are assembled on two 10 mm thick base plates (in sum 20 mm thickness). In the separation area of the assembled combined die (except for face sheets), isolator plates were used on both sides to protect the heat exchange with a press-hardened segment (Fig. 5).

<figure><img src="image_89.png" /><figcaption>Figure 5: Manufactured hybrid die for the production of the functionally graded components</figcaption></figure>

### Forming process

The forming experiments were performed in HYDRAP HPSZK 100-1000/650 hydraulic drawing press with a maximum force of 1000 kN from SCHULER-HYDRAP GmbH & Co. KG. The 22MnB5 blanks with 184 × 95 mm² dimensions were formed. The blanks were formed at a speed of 30 mm/s with two different blank holder forces, 50 kN and 75 kN. The blanks were heated in a chamber furnace at 900°C for five minutes to ensure complete austenitization (i.e., change in crystal structure from ferrite to austenite) and manually positioned on the die with a temperature drop of 120°C. A contact thermometer is used to monitor the blank temperature before and after forming. A thermal imaging camera is used to observe the temperature in different die sections. Ionized water is pumped through the cooled section of the die. A second pump from Hyfra Industriekühlanlagen GmbH was also used to cool the punch. Due to technical issues, cooling only
one side of the punch was impossible. The blanks were formed and held between the die, punch, and blank holder for 15 seconds. After opening the die, the sheets' temperature is measured at various points. The influence of the three preheating temperature values (150, 250, and 350°C) of the heating side of the combined die on the accuracy of the final part geometry, hardness, and microstructure was investigated.

## Results and discussions

### Determination of the cooling rate and the heat transfer coefficient

The heat transfer coefficient ($\alpha$) is calculated based on the Newton's law of cooling as:

$$ \alpha = -\ln\left(\frac{T_o - T_u}{T(t) - T_u}\right) \cdot \frac{c_p \cdot t}{A}, \qquad (1) $$

where $T_o$ is the initial temperature of the blank, and $T(t)$ is the temperature of the blank as a function of time. $T_u$ represents the die temperature on the surface. The time duration of the experiment is represented with $t$, and $A$ is the contact area. The blank's heat capacity ($c_p$) is considered 650 J/kg [9]. The determined heat transfer coefficient ($\alpha$) and corresponding cooling rate ($T'$) are presented in Fig. 6. A steep decrease in $\alpha$ is observed after deposition of the first layer. Increasing the number of layers shows a slow, steady decrease of 10 K/s for the cooling rate. It can be concluded that the deposited Ferro55 has a lower heat transfer coefficient compared to base sheets (S355MC). The cooling rates are still higher than the critical rates of 27 K/s for press hardening.

<figure><img src="image_89.png" /><figcaption>Figure 6: Heat transfer coefficient and cooling rate for die without ($n = 0$) and with several deposition layers</figcaption></figure>

### Manufacturing of functionally graded components

Before assembling the two sides of the die on the base plate, the temperature distribution of the heating side is captured by the thermographic camera at different preheating temperatures ($T_o = 150°C, 250°C, and 350°C$), as shown in Fig. 7. The temperature distribution is non-uniform in the heating side of the die at the different temperature levels. The top surface shows much lower temperatures than the forming zone. Yet, the temperature distribution is fairly homogeneous across the forming zone (radius area). However, increasing the preheating temperature increases the temperature difference even in the forming zone to ca. 14 °C (Fig. 7).
one side of the punch was impossible. The blanks were formed and held between the die, punch, and blank holder for 15 seconds. After opening the die, the sheets' temperature is measured at various points. The influence of the three preheating temperature values (150, 250, and 350°C) of the heating side of the combined die on the accuracy of the final part geometry, hardness, and microstructure was investigated.

## Results and discussions

### Determination of the cooling rate and the heat transfer coefficient

The heat transfer coefficient ($\alpha$) is calculated based on the Newton's law of cooling as:

$$ \alpha = -\ln\left(\frac{T_o - T_u}{T(t) - T_u}\right) \cdot \frac{c_p \cdot t}{A}, \qquad (1) $$

where $T_o$ is the initial temperature of the blank, and $T(t)$ is the temperature of the blank as a function of time. $T_u$ represents the die temperature on the surface. The time duration of the experiment is represented with $t$, and $A$ is the contact area. The blank's heat capacity ($c_p$) is considered 650 J/kg [9]. The determined heat transfer coefficient ($\alpha$) and corresponding cooling rate ($T'$) are presented in Fig. 6. A steep decrease in $\alpha$ is observed after deposition of the first layer. Increasing the number of layers shows a slow, steady decrease of 10 K/s for the cooling rate. It can be concluded that the deposited Ferro55 has a lower heat transfer coefficient compared to base sheets (S355MC). The cooling rates are still higher than the critical rates of 27 K/s for press hardening.

<figure><img src="image_90.png" /><figcaption>Figure 6: Heat transfer coefficient and cooling rate for die without ($n = 0$) and with several deposition layers</figcaption></figure>

### Manufacturing of functionally graded components

Before assembling the two sides of the die on the base plate, the temperature distribution of the heating side is captured by the thermographic camera at different preheating temperatures ($T_o = 150°C, 250°C, and 350°C$), as shown in Fig. 7. The temperature distribution is non-uniform in the heating side of the die at the different temperature levels. The top surface shows much lower temperatures than the forming zone. Yet, the temperature distribution is fairly homogeneous across the forming zone (radius area). However, increasing the preheating temperature increases the temperature difference even in the forming zone to ca. 14 °C (Fig. 7).
<figure><img src="image_90.png" /><figcaption>Figure 7: Thermographic camera results for different preheating temperatures ($T_0$), (a) 150°C, (b) 250°C, (c) 350°C</figcaption></figure>

The initial forming experiments were carried out with the blank holder force of 75 kN with a distance of 1.65 mm between the blank holder and the die. The preheating temperature of the heating side of the die is set to 150°C. It was observed that a fracture had occurred in the formed part during the forming process at the heating side of the die. The blank holder force is reduced to 50 kN to avoid the fracture, and the distance between the die and the blank holder is increased to 1.8 mm. The formed blanks with the new parameters were without any failure. Next, three components were formed for each preheating temperature ($T_0$). An overview of the tool and specimen temperatures after forming processes is presented in **Table 3**. The small increase in the temperature of the specimen (ca. 9%) on the press-hardened side is due to an increase in the preheating temperature of the heated side. It also led to an increase in the blank holder temperature.

<table><caption>Table 3: Temperature of the tool and blank after forming process</caption><thead><tr><th rowspan="2">$T_0$</th><th>Die temperature (°C)</th><th colspan="2">Specimen temperature after forming (°C)</th><th rowspan="2">Blank holder temperature (°C)</th></tr><tr><th>Press-hardened side</th><th>Heating side</th><th>Press-hardened side</th></tr></thead><tbody><tr><td>150°C</td><td>23 ± 1</td><td>166 ± 2.5</td><td>74 ± 10</td><td>41 ± 1</td></tr><tr><td>250°C</td><td>30 ± 1</td><td>181 ± 2.5</td><td>79 ± 10</td><td>54 ± 0.5</td></tr><tr><td>350°C</td><td>39 ± 1</td><td>197 ± 2.2</td><td>81 ± 10</td><td>58 ± 1</td></tr></tbody></table>

The geometrical accuracy measurements show that the heating side has a significant effect on the deviation of inclination angle ($\Delta\theta = (\theta - \theta_T)/\theta$, where $\theta$ is the nominal angle and $\theta_T$ is the angle after the experiment, see **Fig. 4a**), where the press-hardened side shows lower deviation compared to heating side (**Fig. 8a**). The inner radii show significantly lower geometric inaccuracy than the outer radii regardless of the die temperature (errors less than 3%). The reason for higher accuracy is that the inner radius of the specimen is formed by the punch, which is manufactured by the milling process. The outer radius deviation ($\Delta R_o = (R_o - R_T)/R_o$, where $R_o$ is the nominal outer radius, and $R_T$ is the radius after the experiment, shows inaccuracy by a maximum of 8%. Increasing the preheating temperature also leads to an increase in the outer radius deviation (**Fig. 8b**). The hardness ($H$) value at the different points in the formed part shows a consistent hardness at ca. 480 HV10 (an increase of ca. 260%) except at the specimen wall at all preheating temperatures (**Fig. 9**). At point 3, at all preheating temperatures, the hardness decreases to around 400 HV10, 60% less than in other areas (except point 8). At point 8, the blank is in full contact with the portion of the heated side with the highest temperature during the forming (**Fig. 7**). This results in lower hardness (ca. 280 HV10). However, because of the inhomogeneous temperature distribution on the heating side of the die, the hardness in other areas is the same as quenching.
<figure><img src="image_91.png" /><figcaption>Figure 7: Thermographic camera results for different preheating temperatures ($T_0$), (a) 150°C, (b) 250°C, (c) 350°C</figcaption></figure>

The initial forming experiments were carried out with the blank holder force of 75 kN with a distance of 1.65 mm between the blank holder and the die. The preheating temperature of the heating side of the die is set to 150°C. It was observed that a fracture had occurred in the formed part during the forming process at the heating side of the die. The blank holder force is reduced to 50 kN to avoid the fracture, and the distance between the die and the blank holder is increased to 1.8 mm. The formed blanks with the new parameters were without any failure. Next, three components were formed for each preheating temperature ($T_0$). An overview of the tool and specimen temperatures after forming processes is presented in **Table 3**. The small increase in the temperature of the specimen (ca. 9%) on the press-hardened side is due to an increase in the preheating temperature of the heated side. It also led to an increase in the blank holder temperature.

<table><caption>Table 3: Temperature of the tool and blank after forming process</caption><thead><tr><th rowspan="2">$T_0$</th><th>Die temperature (°C)</th><th colspan="2">Specimen temperature after forming (°C)</th><th rowspan="2">Blank holder temperature (°C)</th></tr><tr><th>Press-hardened side</th><th>Heating side</th><th>Press-hardened side</th></tr></thead><tbody><tr><td>150°C</td><td>23 ± 1</td><td>166 ± 2.5</td><td>74 ± 10</td><td>41 ± 1</td></tr><tr><td>250°C</td><td>30 ± 1</td><td>181 ± 2.5</td><td>79 ± 10</td><td>54 ± 0.5</td></tr><tr><td>350°C</td><td>39 ± 1</td><td>197 ± 2.2</td><td>81 ± 10</td><td>58 ± 1</td></tr></tbody></table>

The geometrical accuracy measurements show that the heating side has a significant effect on the deviation of inclination angle ($Δθ = (θ - θ_T)/θ$, where $θ$ is the nominal angle and $θ_T$ is the angle after the experiment, see **Fig. 4a**), where the press-hardened side shows lower deviation compared to heating side (**Fig. 8a**). The inner radii show significantly lower geometric inaccuracy than the outer radii regardless of the die temperature (errors less than 3%). The reason for higher accuracy is that the inner radius of the specimen is formed by the punch, which is manufactured by the milling process. The outer radius deviation ($ΔR_o = (R_o - R_T)/R_o$, where $R_o$ is the nominal outer radius, and $R_T$ is the radius after the experiment, shows inaccuracy by a maximum of 8%. Increasing the preheating temperature also leads to an increase in the outer radius deviation (**Fig. 8b**). The hardness ($H$) value at the different points in the formed part shows a consistent hardness at ca. 480 HV10 (an increase of ca. 260%) except at the specimen wall at all preheating temperatures (**Fig. 9**). At point 3, at all preheating temperatures, the hardness decreases to around 400 HV10, 60% less than in other areas (except point 8). At point 8, the blank is in full contact with the portion of the heated side with the highest temperature during the forming (**Fig. 7**). This results in lower hardness (ca. 280 HV10). However, because of the inhomogeneous temperature distribution on the heating side of the die, the hardness in other areas is the same as quenching.
<figure><img src="image_91.png" /><figcaption>Figure 8: Effect of preheating on (a) inclination angle deviation, (b) outer radius deviation</figcaption></figure>

The other possible reason is due to lower strain hardening due to the variations in strain levels, part-die thermal contact, and consequent cooling rate in the walls of the formed parts as Grydin et al. [10], Taylor and McCulloch [11], and Naderi et al. [12] mentioned. Another reason for the high hardness in the heating area is the usage of the punch with the cooling channel. Since it was not possible to circulate the cooling fluid only in one side of the punch, the phase transformation also happened in this area.

<figure><img src="image_92.png" /><figcaption>Figure 9: Hardness distribution at different preheating temperatures</figcaption></figure>

The microstructure distribution of the as-received 22MnB5 sheet is the ferritic-pearlitic structure. It consists of interlocked cementite lamellae, which are randomly distributed and oriented in a ferrite matrix (Fig. 10a). At all preheating temperatures, the phase transformation to martensite in the press-hardened section can be observed (Fig. 10b, c, d). However, except for the wall area on the heating side, other areas also have unwanted phase transformation to martensite structure, which confirms the hardness measurements results. Another reason for this unwanted phase transformation is the low preheating temperature, as seen at preheating temperature $T_0 = 150°C$ (Fig. 10b), where the transformation happens even in the wall. The martensite structure in the base and transition zone also explains the high hardness values on the heated side in Fig. 9.
<figure><img src="image_92.png" /><figcaption>Figure 8: Effect of preheating on (a) inclination angle deviation, (b) outer radius deviation</figcaption></figure>

The other possible reason is due to lower strain hardening due to the variations in strain levels, part-die thermal contact, and consequent cooling rate in the walls of the formed parts as Grydin et al. [10], Taylor and McCulloch [11], and Naderi et al. [12] mentioned. Another reason for the high hardness in the heating area is the usage of the punch with the cooling channel. Since it was not possible to circulate the cooling fluid only in one side of the punch, the phase transformation also happened in this area.

<figure><img src="image_93.png" /><figcaption>Figure 9: Hardness distribution at different preheating temperatures</figcaption></figure>

The microstructure distribution of the as-received 22MnB5 sheet is the ferritic-pearlitic structure. It consists of interlocked cementite lamellae, which are randomly distributed and oriented in a ferrite matrix (Fig. 10a). At all preheating temperatures, the phase transformation to martensite in the press-hardened section can be observed (Fig. 10b, c, d). However, except for the wall area on the heating side, other areas also have unwanted phase transformation to martensite structure, which confirms the hardness measurements results. Another reason for this unwanted phase transformation is the low preheating temperature, as seen at preheating temperature T₀ = 150°C (Fig. 10b), where the transformation happens even in the wall. The martensite structure in the base and transition zone also explains the high hardness values on the heated side in Fig. 9.
<figure><img src="image_93.png" /><figcaption>Figure 10: Microstructure of (a) as-received blank, and the formed parts at (b) T₀ = 150°C, (c) T₀ = 250°C, and (d) T₀ = 350°C</figcaption></figure>

## Summary

The cooling rate and heat transfer coefficient for deposited Ferro55 layers were determined, and it was shown that the cooling rate is sufficiently quick for the press hardening process. The tool for functionally graded components is produced quickly and costlessly without the hardening process by hybrid additive laminated tools, where one side of the tool is equipped with cooling channels and the other with heating cartridges. The application of hybrid additive manufacturing offers the
<figure><img src="image_94.png" /><figcaption>Figure 10: Microstructure of (a) as-received blank, and the formed parts at (b) T₀ = 150°C, (c) T₀ = 250°C, and (d) T₀ = 350°C</figcaption></figure>

## Summary

The cooling rate and heat transfer coefficient for deposited Ferro55 layers were determined, and it was shown that the cooling rate is sufficiently quick for the press hardening process. The tool for functionally graded components is produced quickly and costlessly without the hardening process by hybrid additive laminated tools, where one side of the tool is equipped with cooling channels and the other with heating cartridges. The application of hybrid additive manufacturing offers the
possibility of positioning the cooling channels nearer to the tool surface while reducing costs compared to conventionally manufactured tools. Although this preliminary study has proven the concept for the production of hybrid tools for functionally graded components, further studies are required to optimize the position and size of the conformal cooling channels and heating cartridges to improve the cooling rate and, at the same time, heating rate.

## Acknowledgments

The authors would like to kindly acknowledge the support of the German Research Foundation (DFG) for funding the project "Reducing the stair step effect for dies manufactured by layer-laminated manufacturing by additive and formative post-processing" with funding number 426515407. Mr. Sven Lukies is acknowledged for his technical support with the metallography.

## References

[1] H. Karbasian, A.E. Tekkaya, A review on hot stamping. J. of Materials Processing Technology. 210 (2010) 2103-2118. https://doi.org/10.1016/j.jmatprotec.2010.07.019

[2] E. Billur, Hot Stamping of Ultra High-Strength Steels, first ed., Springer Cham, 2019. https://doi.org/10.1007/978-3-319-98870-2

[3] B. Müller, M. Gebauer, R. Malek, N. Gerth, Metal Additive Manufacturing for tooling applications Beam Melting technology increases the efficiency of dies and molds. In Proceedings of the Metal Additive Manufacturing Conference MAMC, Vienna, Austria, 20–21 November 2014.

[4] P. Stoll, A. Spierings, M. Gebauer, B. Müller, S. Polster, T. Feld, M. Klinger, A. Zurbrügg, High performance sheet metal forming tooling by additive manufacturing. Proceedings of the 6th International Conference on Additive Technologies (2016) 354-361.

[5] B.G. Bryden, D.I. Wimpenny, I.R. Pashby, Manufacturing production tooling using metal laminations. Rapid Prototyping Journal 7 (2001) 52-59.

[6] H. Dardaei Joghan, M. Hahn, J. T. Sehrt, A. E. Tekkaya, Hybrid additive manufacturing of metal laminated forming tools, CIRP Annals, 71(2022), 225-228. https://doi.org/10.1016/j.cirp.2022.03.018

[7] R. Hölker, A. E. Tekkaya, Verfahren zur Reduzierung des Treppenstufenef-fekts bei aus Blechlamellen geschichteten Werkzeugen/Bauteilen mittels additiver und umformtechnischer Nachbearbeitung. German patent DE102018004294 (A1), priority date: 17.05.2018.

[8] H. Dardaei Joghan, M. Hahn, O. Agboola, A.E. Tekkaya, Resources and manufacturing technology evaluation of hybrid additive metal laminated tooling for forming. Materials Research Proceedings, (2023) 21-30. https://doi.org/10.21741/9781644902479-3

[9] J. Lechler, M. Merklein, M. Geiger, Determination of thermal and mechanical material properties of ultra-high strength steels for hot stamping. Steel research international. 79 (2008) 98-104. https://doi.org/10.2374/SRI07SP131-79-2008-98-104

[10] O. Grydin, A. Andreiev, M.J. Holzweißig, C.J. Rüsing, K. Duschik, Y. Frolov, Short austenitization treatment with subsequent press hardening: correlation between process parameters, microstructure and mechanical properties. Mater Sci Eng A. 749 (2019), 176-195. https://doi.org/10.1016/j.msea.2019.02.025

[11] T. Taylor, J. McCulloch, Effect of part/die boundary conditions on microstructural evolution during hot stamping 2000 MPa class boron steel. Steel Res Int. 89 (2018). https://doi.org/10.1002/srin.201700495

[12] M. Naderi, M. Ketabchi, M. Abbasi, W. Bleck. Analysis of microstructure and mechanical properties of different hot stamped B-bearing steels. Steel Res Int. 81 (2010), 216-223. https://doi.org/10.1016/j.proeng.2011.04.078
possibility of positioning the cooling channels nearer to the tool surface while reducing costs compared to conventionally manufactured tools. Although this preliminary study has proven the concept for the production of hybrid tools for functionally graded components, further studies are required to optimize the position and size of the conformal cooling channels and heating cartridges to improve the cooling rate and, at the same time, heating rate.

## Acknowledgments

The authors would like to kindly acknowledge the support of the German Research Foundation (DFG) for funding the project "Reducing the stair step effect for dies manufactured by layer-laminated manufacturing by additive and formative post-processing" with funding number 426515407. Mr. Sven Lukies is acknowledged for his technical support with the metallography.

## References

[1] H. Karbasian, A.E. Tekkaya, A review on hot stamping. J. of Materials Processing Technology. 210 (2010) 2103-2118. https://doi.org/10.1016/j.jmatprotec.2010.07.019

[2] E. Billur, Hot Stamping of Ultra High-Strength Steels, first ed., Springer Cham, 2019. https://doi.org/10.1007/978-3-319-98870-2

[3] B. Müller, M. Gebauer, R. Malek, N. Gerth, Metal Additive Manufacturing for tooling applications Beam Melting technology increases the efficiency of dies and molds. In Proceedings of the Metal Additive Manufacturing Conference MAMC, Vienna, Austria, 20–21 November 2014.

[4] P. Stoll, A. Spierings, M. Gebauer, B. Müller, S. Polster, T. Feld, M. Klinger, A. Zurbrügg, High performance sheet metal forming tooling by additive manufacturing. Proceedings of the 6th International Conference on Additive Technologies (2016) 354-361.

[5] B.G. Bryden, D.I. Wimpenny, I.R. Pashby, Manufacturing production tooling using metal laminations. Rapid Prototyping Journal 7 (2001) 52-59.

[6] H. Dardaei Joghan, M. Hahn, J. T. Sehrt, A. E. Tekkaya, Hybrid additive manufacturing of metal laminated forming tools, CIRP Annals, 71(2022), 225-228. https://doi.org/10.1016/j.cirp.2022.03.018

[7] R. Hölker, A. E. Tekkaya, Verfahren zur Reduzierung des Treppenstufenef-fekts bei aus Blechlamellen geschichteten Werkzeugen/Bauteilen mittels additiver und umformtechnischer Nachbearbeitung. German patent DE102018004294 (A1), priority date: 17.05.2018.

[8] H. Dardaei Joghan, M. Hahn, O. Agboola, A.E. Tekkaya, Resources and manufacturing technology evaluation of hybrid additive metal laminated tooling for forming. Materials Research Proceedings, (2023) 21-30. https://doi.org/10.21741/9781644902479-3

[9] J. Lechler, M. Merklein, M. Geiger, Determination of thermal and mechanical material properties of ultra-high strength steels for hot stamping. Steel research international. 79 (2008) 98-104. https://doi.org/10.2374/SRI07SP131-79-2008-98-104

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# Experimental evaluation of the maximal force before debonding a part from the build platform of an AM printer

SPITAELS Laurent<fnref n="1" />,ª*, ALDEITURRIAGA OLABARRI Naiara<fnref n="1" />,<fnref n="2" />, BOSSU Julien<fnref n="1" />, MARTIC Gregory<fnref n="3" />, JUSTE Enrique<fnref n="3" />, RIVIERE-LORPHEVRE Edouard<fnref n="1" />, ARRAZOLA Pedro-José<fnref n="2" />, DUCOBU François<fnref n="1" />

**Keywords:** Additive Manufacturing, Peel Test, Hybrid Manufacturing, Material Extrusion

**Abstract.** Hybrid manufacturing relies on the combination of different processes to overcome their own limitations. Combining additive manufacturing such as material extrusion (MEX) and subtractive conventional processes such as milling enables the production of complex design parts with smooth surface and tight tolerances to be foreseen. However, finishing a part directly in the printer by milling operations brings new questions as the maximal force the part can withstand before debonding from the build platform. This paper proposes an experimental method to determine this force by studying the influence of several parameters: the adhesion strategy, the infill pattern and density, the zone of the part where the force is applied as well as the influence of the part design. The method was successfully tested with Polylactic Acid (PLA) parts on a standard MEX printer: the Ultimaker 2+. In the best configuration: no brim, cubic infill of 95% and a force applied on an entire part face, the parts resisted to a force of 50.59 N ± 1.97 N.

## Introduction

Additive Manufacturing processes are seen as game changers for the near future [1]. Indeed, from the first developments in the 1980s to 2023, the annual average growth rate of the AM market reached 26.7% [1]. This new way of producing goods begins to be adopted not only for prototyping or modelling, but also now for producing end-user parts [1]. Among the seven families defined by ISO 52900, Material Extrusion (MEX) and Binder Jetting (BJ) are gaining great interest from the industries [2]. They are seen as the two most promising processes in a ten-year horizon for the manufacturing of metallic or ceramics components [2,3]. Indeed, compared to the conventional manufacturing processes (as injection or machining, e.g.), the AM processes enable the production of complex shaped parts which were not possible to achieve before [4]. Moreover, the layer-by-layer nature of AM allows to bring complexity in the material and functions of the parts [4].

However, despite the large progresses achieved in the previous years, the AM processes still lack of capability for producing smooth surfaces with tight tolerances [3–5]. Arithmetic roughness (Ra) values from 9 µm to 40 µm are typical in MEX process [5], while widespread MEX printers produce parts with very large tolerances compared to conventional subtractive processes [6]:

- IT between 12 and 16 of ISO 286-1 for dimensional features.

- L class of ISO 2768-2 for geometrical features.

Post finishing operations (sanding or machining, e.g.) are then required to match the customer specifications [4]. Performing the finishing operations when the part is already totally produced brings new constraints and makes impossible the finishing of complex designs such as lattice, for example. Indeed, the subtractive processes require to access the surfaces to be treated and lack of

<figure><img src="image_94.png" /></figure>

<footnote n="1">¹ Machine Design and Production Engineering Lab - Research Institute for Science and Material Engineering - University of Mons, Place du Parc 20, Mons, Belgium</footnote>

<footnote n="2">² Mondragon Unibertsitatea, Loramendi 4, Arrasate-Mondragón, 20500, Spain</footnote>

<footnote n="3">³ Belgium Ceramic Research Centre, Avenue Gouverneur Cornez 4, Mons 7000, Belgium</footnote>

<footnote>ª laurent.spitaels@umons.ac.be</footnote>