bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sustainable Product Design Decision-Making Through Integrated Risk Likelihood and Impact Analyses
Authors: Enyoghasi Christian , Badurdeen Fazleena 
Published: September 2023

Abstract
Various decision-making methods are available for identifying sustainable designs that meet desired performance objectives. When any such method is used and a selected design is launched in the market, numerous uncertainties can affect its total lifecycle performance, giving rise to different risks that can impact expected performance. These potential risks must be identified, evaluated for possible impact, and suitable countermeasures considered, if necessary, prior to selecting any product design. The risks can be measured by their probability of occurrence (likelihood) and consequence when occurred (impact). Risk assessment literature, however, mostly focuses on developing tools and methods to analyze the probability of risks during product design decision-making. No emphasis has been laid on methods for impact analysis in the event of risk occurrence. There is also a marked lack of literature combining quantified likelihood and impact of risks for decision-making. In this paper, an approach to integrate risk likelihood results and findings from risk impact analysis is presented to facilitate better sustainable product design decision-making. Risk identification, likelihood, and impact analyses are conducted following ISO 31000 Risk Management Guidelines. Then two operational risk indices, one for likelihood and another for impact, are defined to consolidate the likelihood and impact of a design(s) on performance objectives, respectively. A Risk Level Quadrant is then developed to visualize the variation of risk impact and likelihood for different designs and identify a suitable design. An industrial case study is used to demonstrate the use of these indices for a set of Pareto design solutions identified through multi-objective optimization. The result shows that these metrics and the RLQ simplify understanding the risk levels of alternate product designs and help to make better design choices.
An Inexpensive 3D Printing System for Functional Inks Used In Electronics and Bio Applications
Authors: Thakkar Achint , Jamghare Ritik , Mishra Ravi , Emon Omar Faruk 
Published: September 2023

Abstract
Numerous extrusion-based 3D printing processes for electronics and bio-applications have been reported in the past decade. However, these direct ink writing (DIW) systems are mostly expensive and complex to build. Here, we report an inexpensive and open-source extrusion-based 3d printer that could be employed for printing functional inks, especially for various electronics and bio fabrication. A low-cost fused filament fabrication (FFF) 3D printer was modified to build the DIW system. An extrusion mechanism was developed with a stepper motor, a lead screw, a syringe-plunger system, and spur gears. The existing FFF heated extruder was replaced by a new extrusion mechanism. The printer was utilized to print thermosetting viscous pre-polymers. A photocurable resin was thickened using fumed silica to induce non-Newtonian shear thinning properties, and it was used for printing experiments. G-code for printing was generated using an open-source slicer software. Different extrusion rates were examined by controlling the extrusion motor rotation and speed. Also, various print parameters were investigated such as travel speed, ink viscosity, and nozzle size. The dimension of the printed geometry was studied with the change of print parameters. The printed structures were photocured using ultraviolet light. The printed geometries before and after curing were compared. To check printability for electronics, a soft pressure-sensitive membrane was 3D printed using the developed system. An acrylate-based monomer was mixed with a photoinitiator, crosslinker, fumed silica, and ionic liquid to prepare the functional ink for sensor fabrication. The printed sensor was tested under different load conditions.
Physics-Based Feedforward Control of Thermal History in Laser Powder Bed Fusion Additive Manufacturing
Authors: Riensche Alexander , Bevans Benjamin , Smoqi Ziyad , Yavari Reza , Krishnan Ajay , Gilligan Josie , Piercy Nicholas , Cole Kevin , Rao Prahalada 
Published: September 2023

Abstract
We developed and applied a model-based feedforward control approach to reduce temperature-induced flaw formation in the laser powder bed fusion (LPBF) additive manufacturing process. The feedforward control is built upon three basic steps. First, the thermal history of the part is rapidly predicted using a mesh-free graph theory model. Second, thermal history metrics are extracted from the model to identify regions of heat buildup, symptomatic of flaw formation. Third, process parameters are changed layer-by-layer based on insights from the thermal model. This technique was validated with two identical build plates (Inconel 718). Parts on the first build plate were made under manufacturer recommended nominal process parameters. Parts on the second build plate were made with model optimized process parameters. Results were validated with in-situ infrared thermography, and materials characterization techniques. Parts produced under controlled processing exhibited superior geometric accuracy and resolution, finer grain size, and increased microhardness.
A Fringe Pattern Analysis Technique for Photomask Line-Edge-Roughness Characterization
Authors: Wang Zhikun , Lu Kuan , Lee ChaBum 
Published: September 2023

Abstract
Line-edge roughness (LER) characterization is vital in modern semiconductor industry. An EKEI (enhanced knife edge interferometry)-based method for photomask LER characterization, being capable of a 0.01μm LER measurement accuracy, was proposed in this paper. The changes in fringe pattern caused by edge roughness were derived and analyzed through cross-correlation index. A negative relationship between the cross-correlation and the LER value was observed. The results are validated by both simulation and experiment, indicating that the proposed method can detect both roughness changes and residual pollution on photomask edge.
Thermal and Fast Neutron Irradiation Effects on Additively Manufactured and Wrought Inconel 625
Authors: Andurkar Mohanish , O’Donnell Valentina , Keya Tahmina , Prorok Bart , Gahl John , Thompson Scott M. 
Published: September 2023

Abstract
Since their onset, additive manufacturing (AM) technologies are experiencing substantial growth in their development and practical applications in various industries. AM components of different materials are in demand for building next generation advanced nuclear reactors. In this study, Inconel 625 (IN625) samples fabricated using laser powder bed fusion (L-PBF) AM process were subjected to two types of neutron irradiation experiments. Samples were first irradiated for a continuous 310 hours using full spectrum neutrons, ranging in energy from thermal to fast. Secondly, another set of samples were exposed to 7–17 weeks of only fast neutron irradiations in short sessions. The total fluence experienced during full spectrum and fast neutron irradiations were 7.37 × 1019 neutrons/cm2 and 2.74 × 1015−6.61 × 1015 neutrons/cm2 respectively. Along with L-PBF IN625 samples, wrought IN625 samples were used as a reference material in both radiation experiments. The effect of radiation damage in the form of radiation hardening or embrittlement was quantified by measuring microhardness before and after irradiation. In general, irrespective of neutron irradiation type, results indicate the AM IN625 samples showed more resistance towards radiation hardening or embrittlement defect compared to its counterparts wrought IN625 samples. The results reported in this study will increase the confidence in adopting AM technologies to build critical advance nuclear components in the future.
3D Printed Bioinspired Hierarchical Surface Structure With Tunable Wettability
Authors: Rahman M. M. Towfiqur , Joyee Erina Baynojir 
Published: September 2023

Abstract
Nature has examples of impressive surfaces and interfaces with diverse wettability stemming from superhydrophilicity to superhydrophobicity. The multiscale surface structures found in biological systems generally have high geometric complexity, which makes it challenging to replicate their characteristics, especially using traditional fabrication techniques. It is even more challenging to fabricate such complex microstructures with tunable wettability. In this paper, we propose a method to tune the wettability of a micro-scale surface by changing the geometrical parameters of embedded micro-structures in the surface. By taking inspiration from an insect (springtails), we designed micro pillar arrays with different roughness by adjusting geometric parameters such as reentrant angle, pitch distance, and the number of spikes and pillars. This study shows that, by changing geometrical parameters in micro-scale, the apparent contact angle, and hence the surface wettability can be calibrated. The micro-scale pillars were fabricated using a precise micro direct light processing (μDLP) three-dimensional (3D) printer. Different printing parameters were studied to optimize the geometric parameters to fabricate 3D hierarchical structures with high accuracy and resolution. The relationship between the wettability of the surface and the geometrical parameters was predicted using a modified Cassie’s model and experimentally validated. The largest apparent contact angle in our experiments is up to 160°, with pillars of 0.17mm height and 0.5 mm diameter, 55° reentrant angle, and a spacing of 0.36 mm between pillars. The lowest contact angle is ∼35° by reducing the pillar size and spacing. By controlling the size of different features of the pillar, pillar number, and layout of the mushroom shaped micropillars, the wettability of the surface is possible to be tuned from a highly nonwetting liquid/material combination to highly wetting material. Such wettability tuning capability expands the design space for many biomedical and thermofluidic applications.
Rapid 3D Printing of Nanoporous Copper Powders via Micro-Clip
Authors: Liu Luyang , Kublik Natalya , Azeredo Bruno , Chen Xiangfan 
Published: September 2023

Abstract
Three-dimensional (3D) printing of metal components through powder bed fusion, material extrusion, and vat photopolymerization, has attracted interest continuously. Particularly, extrusion-based and photopolymerization-based processes employ metal particle-reinforced polymer matrix composites (PMCs) as raw materials. However, the resolution for extrusion-based printing is limited by the speed-accuracy tradeoff. In contrast, photopolymerization-based processes can significantly improve the printing resolution, but the filler loading of the PMC is typically low due to the critical requirement on raw materials’ rheological properties. Herein, we develop a new metal 3D printing strategy by utilizing micro-continuous liquid interface printing (μCLIP) to print PMC resins comprising nanoporous copper (NP-Cu) powders. By balancing the need for higher filler loading and the requirements on rheological properties to enable printability for the μCLIP, the compositions of PMC resin were optimized. In detail, the concentration of the NP-Cu powders in the resins can reach up to 40 wt% without sacrificing the printability and printing speed (10 μm·s−1). After sintering, 3D copper structures with microscale features (470 ± 140 μm in diameter) manifesting an average resistivity of 150 kΩ·mm can be realized. In summary, this new strategy potentially benefits the rapid prototyping of metal components with higher resolution at faster speeds.
Job Placement for Cooperative 3D Printing
Authors: Weber Daniel H. , Zhou Wenchao , Sha Zhenghui 
Published: September 2023

Abstract
Cooperative 3D Printing (C3DP), an additive manufacturing platform consisting of a swarm of mobile printing robots, is an emerging technology designed to address the size and printing speed limitations of conventional, gantry-based 3D printers. A typical C3DP process often involves several interconnected stages, including project/job partitioning, job placement on the floor, task scheduling, path planning, and motion planning. In our previous work on project partitioning, we presented a Z-Chunker, which vertically divides a tall print project into multiple jobs to overcome the physical constraints of printers in the Z direction, and an XY Chunker, to partition jobs into discrete chunks, which are allocated to individual printing robots for parallel printing. These geometry partitioning algorithms determine what is to be printed, but other information, such as when, where, and in what order chunks should be printed, is required to carry out the print physically. This paper introduces the first Job Placement Optimizer for C3DP based on Dynamic Dependency List schedule assignment and Conflict-Based Search path planning. Our algorithm determines the optimal locations for all jobs and chunks (i.e., subtasks of a job) on the factory floor to minimize the makespan for C3DP. To validate the proposed approach, we conduct three case studies: a simple geometry with homogeneous jobs in the Z direction and two complex geometries (one with moderate complexity and one relatively more complex) with non-homogeneous jobs in the Z direction. We also performed simulations to understand the impact of other factors, such as the number of robots, the number of jobs, chunking orientation, and the heterogeneity of prints (e.g., when chunks are different in size and materials), on the effectiveness of this placement optimizer.
Evolution of Grain Refinement and Phase Transformation Based on Stress-Strain Induced Incremental Model in Machining of Titanium Alloy
Authors: Yang Peiqiang , Zhang Xueping , Yao Zhenqiang , Shivpuri Rajiv 
Published: September 2023

Abstract
Grain refinement occurs on the machined surface and adiabatic shear band (ASB) through severe plastic deformation (SPD) in the high-speed machining (HSM) Ti-6Al-4V alloy, resulting in the formation of desirable ultrafine grain structure. The extremely high temperature changing rate induces the transformation of the original α-phase into β-phase, which then forms martensitic phase after cooling process. The altered microstructure has different effects on the material properties and machined surface integrity. Therefore, the established model has received the increasing attentions to accurately predict microstructural evolution. This research proposes a strain-induced incremental model of grain dynamic recrystallization (DRX) evolution based on the conventional Johnson-Mehl-Avrami-Kolmogorov (JMAK) model. The model emphasizes the contribution of large strain to grain refinement during the machining of Ti-6Al-4V alloy. In addition, a stress-induced phase transformation increment model is proposed to describe the phase evolution in machining since high stress can effectively reduce the initial temperature in transiting α-phase to β-phase and the martensite phase transition temperature. Subsequently, the orthogonal cutting simulations of Ti-6Al-4V alloy are carried out to validate the proposed model by comparing with the existing research results. The simulation results indicate that the average grain size in the ASB is increased from 46.2% to 54.2% and the average grain size at the machined surface decreased from 85% to 83% with the cutting speed range of 5–20 m/s. The average grain size at the machined surface is decreased from 83% to 78% with the tool flank face wear (VB) ranging from 0.00 mm (fresh) to 0.08 mm. The decreased temperature has an inhibitory effect on the grain refinement process of the ASB and the machined surface within the range of initial temperatures from −150 °C to 25 °C.
Submicron Motion Measurement Based on Nonlinear Identification of Diffractive Optical Features
Authors: Su Taiyu , Zhang Zhijing , Xiong Jian , Li Erbo , Saren Qimuge 
Published: September 2023

Abstract
Precision positioning is a critical technology in precision machinery manufacturing, and precision displacement measurement is the basis of precision positioning. Optical displacement measurement technology currently occupies the dominant position of precision displacement measurement. In contrast, the existing precision optical measurement methods are easily restricted by environmental interference, high cost, complex structure, and difficult integration. A submicron-level accuracy motion measurement method proposed in this paper is based on the nonlinear identification of diffractive optical features with the help of spatial information contained in the phenomenon of Fresnel diffraction. According to this method, the relationship between the aperture position and the diffraction image in Fresnel circular aperture diffraction is used to establish a nonlinear mapping model of the relationship between diffractive optical features and the aperture position by a convolutional neural network to convert the plane displacement measurement into the problem of diffractive optical features recognition, to realize the high-precision motion displacement measurement of two degrees of freedom on the same plane. An experimental setup is developed and experimentally tested to verify the scheme’s feasibility. The experimental results show that the mapping relationship between diffractive optical features and aperture position can be adequately fitted by CNN to achieve motion measurement with submicron-level accuracy. This method of precision measurement based on diffractive optical features provides a new research idea and solution for motion measurement with submicron-level accuracy.
Design and Construction of a Belt-Assisted Vertical Extrusion Based Fused Deposition Modeling 3D Printer for Automated Part Removal
Authors: Brooks Jacob , Rahman M. M. Towfiqur , Joyee Erina Baynojir 
Published: September 2023

Abstract
In recent years, Fused Deposition Modeling (FDM) has become one of the most used three-dimensional (3D) printing processes because of its cost effectiveness and universal approach towards user-friendliness. FDM uses a layering process to build 3D models layer-upon-layer using a varied range of materials from thermoplastic to pharmaceutical materials. However, FDM has a considerable number of challenges to overcome, especially for its use in line production. One possible solution for a more line-production friendly FDM process could be the use of a continuous moving build surface, similar to more conventional line production processes. In this paper, we propose a customized FDM 3D printing system with a moving belt-style build surface and kinematics that allow layers to be printed parallel to the build surface. Unlike other existing belt-FDM systems, the developed FDM printer will deposit filament perpendicular to the bed, allowing the user to utilize conventional 3D printing slicing software, depend less on support structures, and design parts in a traditional manner. Moreover, after completion, these parts can be ejected automatically to allow repeated prints without human intervention, making the printing process faster and more cost-effective. A belt-assisted vertical extrusion based FDM prototype 3D printer has been designed, constructed, and then tested to gain experimental data on continuous 3D printing using thermoplastic polymers as a printing material. Results of testing and experimentation verified certain key design elements and how they could improve continuous printing and automated 3D printed part removal. Experiments also revealed how nozzle temperature, bed temperature, and the moving speed of the nozzle introduce challenges not seen in traditional FDM printers with static beds.
Anomaly Detection for Industrial Robot Prognostics and Health Management
Authors: Fattah Gerald , Newton David , Qiao Guixiu , Leber Dennis D. 
Published: September 2023

Abstract
Industrial robots have continued to play important roles in automation for smart manufacturing. Effective maintenance strategies are essential for successful robot operation. However, maintenance can be costly which has led manufacturers to explore different ways to monitor and evaluate the health of their robot workcell during operation. Successful methods to assess robot health rely on capturing and analyzing significant amounts of continuously generated data from the workcell. Various data streams relevant to a robot workcell may be available such as robot controller data, programmable logic controller data, maintenance logs, and part quality data. Although these data streams can reveal information about the workcell, the large volume and complexity of the data can prove difficult to analyze. Researchers at the National Institute of Standards and Technology (NIST) have developed a test method and accompanying unsupervised learning anomaly detection procedure to assess the health of robot workcells. The approach involves defining a distance metric which is then passed to a robust outlier detection step. Performance of the approach demonstrated on several types of simulated robot failures and compared against different popular anomaly detection algorithms.
Magnetically Assisted Laser Induced Plasma Micromachining (M-LIPMM)
Authors: Cleeman Jeremy , Guo Weihong , Malhotra Rajiv 
Published: September 2023

Abstract
Laser Induced Plasma MicroMachining (LIPMM) uses a pulsed laser to create a plasma spot inside a dielectric liquid and then uses this plasma for machining. Compared to direct laser machining LIPMM enables a wider multimaterial capability, greater machined depth for the same width, lesser damage, and greater throughput. This paper explores Magnetically assisted Laser Induced Plasma MicroMachining (M-LIPMM) in which an external magnetic field is applied to the plasma during machining. We focus on the effects of using a single magnet. The dimensions of the machined features are characterized as a function of the magnetic pole orientation, pulse energy, laser speed, and pulse frequency. We find that M-LIPMM adds an effective magnetic lens to a laser that allows concurrent width reduction and depth increase, greater throughput, and enhanced geometric flexibility, beyond the capabilities of LIPMM.
A Monte-Carlo Method for Evaluating the Economic Performance of Plastics Recycling Systems Using Historical Pricing
Authors: Nandimandalam Hariteja , Costello Christine , Mendis Gamini P. 
Published: September 2023

Abstract
The management of plastic waste is a considerable emerging global concern. Conventional plastics recycling rates range from 5–10% in the US, which results in considerable material being sent to landfill. New sorting technologies using artificial intelligence have the potential to dramatically increase the ability of waste management companies to sort out valuable fractions of plastic waste and create high-purity streams for secondary markets. However, several fractions of the plastics recycling stream, i.e., low-density polyethylene, polypropylene, and polystyrene, do not have well-developed markets. These materials may be sent to pyrolysis facilities for thermal recycling, but the economics of the pyrolysis industry are currently uncertain. This work aims to identify the breakeven price of the plastic fractions that would be sent to pyrolysis in order to determine the economic viability of the sorting facility. The work will use Monte Carlo analysis to evaluate several scenarios and understand how compositional variation, price variability, and facility attributes affect the breakeven price. The presorting conditions in Materials Recovery Facilities strongly affects the profitability of the sorting facility. High residual high density polyethylene (HDPE) and polyethylene terephthalate (PET) content in presorted bales are important revenue drivers for the facility, even in presorted 3-7 bales. Key variables that lead to profitability include a high sale price of polyethylene terephthalate, a high sale price of high-density polyethylene, and if polypropylene can be sold to recycling markets (as opposed to pyrolysis facilities).