bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of Metal Mixing on Mechanical Performance of Laser Keyhole Welding of Nickel and Copper
Authors: Huang Wenkang , Tan Wenda , Cai Wayne , Bracey Jennifer 
Published: September 2022

Abstract
In laser keyhole welding of dissimilar metals, metal mixing in the molten pool is critical to the microstructure and mechanical performance of the welds. In this study, metal mixing and its effects on the mechanical performance of the miscible Nickel-Copper welds are investigated. Experiments were carried out to fabricate samples with different welding parameters. Energy-dispersive X-ray spectroscopy is used to characterize the metal distribution in the fusion zone of the post welds. Mechanical strength testing and fractographic analysis are performed to characterize the strength of the welds and the fracture mode. Two regions of different concentrations can be found in the welds, and the concentrations of these two regions vary significantly with welding parameters. The weld strength is dependent on the interfacial region concentration, and the welds undergo a mixture of shear fracture and tensile fracture during the mechanical strength testing process. The interfacial concentration and the weld strength can be controlled by tuning the concentration of the two concentration regions in the fusion zone and the location of the boundary between the two regions. This study provides insights for industries regarding the design and optimization of the laser welding process to achieve welds with optimal mechanical performance.
Thermal Analyses of Electrically Assisted Forming
Authors: Grimm Tyler J. , Mears Laine 
Published: September 2022

Abstract
Electrically assisted manufacturing (EAM) is defined as the direct application of electricity to a workpiece in situ with a manufacturing process. This is commonly used in forming to reduce the flow stress and increase the ductility of metals. Under certain conditions, there seem to be effects of the electricity that occur in addition to the inherent resistive heating in metals. This electroplastic effect is often deduced by estimating temperatures through analytical or numerical simulations and comparing this to the temperatures required to effect thermal stress reductions observed in experimental tests.
For tests which utilized pulsed or AC currents, an RMS current value may be used to simplify simulations since current transience can be averaged to a constant representative value. However, there is often no justification of this assumption and it is possible that assumption could lead to erroneous results. Various assumptions applied to EAM research are explicitly explored herein to determine their validity in thermal estimations. It was concluded that AC, square wave, and sawtooth currents at frequencies greater than 1 Hz, or pulses from power supplies with significant ripple, can be approximated with a DC current of similar RMS value to obtain similar thermal estimations. Simulation geometries should incorporate as much of the experimental setup as possible. An example from literature was used to test several other assumptions as well, including the use of analytical simulations, rather than numerical.
Product Life-Cycle Energy Framework in Friction Scenarios
Authors: Linke Barbara S. , Gupta Shivam 
Published: September 2022

Abstract
Manufacturers have great power to change the sustainability of products over the whole life cycle, but they need holistic life cycle models to guide those decisions. Challenges exist in connecting the product’s life cycle data to model-based sustainability metrics and in quantifying uncertainty in the product data. This study develops a life-cycle energy framework around two application cases to showcase informed and transparent decision-making. The case studies investigate additively manufactured parts in two friction scenarios, one where low friction is desired and one where high friction is preferred. The layer height is chosen as process parameter of additive manufacturing that changes the surface roughness of the sample parts, but also the manufacturing time and energy. The use phase energy in the first friction scenario is influenced by the user behavior, and by a random input function in the second scenario. The life-cycle energy framework is used to discuss total life cycle energy for each scenario. In general, this framework should be used to better connect product use phase and manufacturing phase, in particular by examining the interconnections of part design, manufacturing phase impacts, and use performance. Product quality is the central aspect of optimization. The framework can be used for engineering education and be expanded to study data uncertainty, user behavior, system complexity, process chains, machine learning, sustainability metrics, and more.
Synthesis and Fabrication of Single Walled Carbon Nanotube Microelectrode Arrays on Flexible Probes for Neurotransmitter Detection
Authors: Park Sei Jin , Ivanovskaya Anna , Yorita Allison 
Published: September 2022

Abstract
Implantable microelectrode arrays are an effective method for understanding neurotransmitter dynamics with high spatial resolution. In particular, carbon-based electrodes are efficient for electrochemical detection of dopamine, a neurotransmitter studied for its role in motor movement and reward-seeking behavior. However, very few options exist for arrayed carbon microelectrodes, specifically on flexible polymeric probes. We demonstrate fabrication of polyimide probes featuring single walled carbon nanotube (SWCNT) microelectrode arrays and characterize their dopamine detection performance. First, SWCNT synthesis parameters were optimized to grow high density SWCNT “forests” that have uniform height with electrode diameters ranging from 15 μm to 100 μm, as these dimensions are spatially relevant to chemical sensing in an animal model. These SWCNT microelectrodes were then incorporated into a microfabrication process involving deposition and patterning of polyimide substrate and metal traces. The process flow was designed such that the polyimide was not exposed to the high temperatures required to grow SWCNTs. Instead, a bottom-up approach was utilized, in which the SWCNT catalyst was first patterned, the SWCNTs were synthesized on a silicon substrate, then polyimide and trace metal layers were deposited and patterned. Prototype probes were fabricated containing the same range of electrode diameters as those used for SWCNT synthesis development to determine the effect of electrode diameter on ease of microfabrication. Microelectrodes ranging from 15 μm to 50 μm in diameter were found to release from the carrier wafer more easily, while larger electrodes demonstrated poor release. These probes demonstrate a concentration-dependent response to dopamine, with high sensitivity compared to microelectrode arrays consisting of bare metal. Further development of this electrode material will enable neuroscientists to study dopamine at higher spatial resolution, with the benefit of utilizing flexible probes.
Primary Chatter and Limiting Chip Load in Turning Under Negative Process Damping
Authors: Hsu Ming-Jen , Wang Jiunn-Jyh 
Published: September 2022

Abstract
This paper presents an analysis of primary chatter under velocity-induced negative process damping in the peripheral outer diameter turning of medium carbon steel. A first-order approximation model of the instant specific cutting force with respect to dynamic cutting speed was established and the slope was defined as the specific process damping coefficient (SPDC) to investigate the negative process damping with respect to cutting speed, depth of cut, and chip thickness. The process damping coefficient was defined as the product of the specific process damping coefficient and chip load. The total system damping coefficient as the sum of the process damping coefficient and structural damping coefficient determines the system stability and predict primary chatter. The SPDCs were obtained through experiments under various speeds, feeds, and depths of cut by using a tool system with force sensors and accelerometers. The SPDCs were insensitive to cutting speeds of 2.5 to 5.5 m/sec and ranged from −1514 and −716 MPa·s/m for feeds per revolution of 0.058 to 0.118 mm, respectively. The higher negative SPDC at smaller chip thickness reduces the limiting stable chip load. Equations for the limiting chip load and limiting depth of cut were derived and validated by experiments. Stability diagrams of limiting chip load and limiting depth with respect to feed per revolution were created to provide guidance on preventing primary chatter.
A Comparative Study on the Effects of an Advanced Scan Pattern and Intelligent Scan Sequence on Thermal Distribution, Part Deformation, and Printing Time in PBF Additive Manufacturing
Authors: He Chuan , Tsai Yueh-Lin , Okwudire Chinedum E. 
Published: September 2022

Abstract
Parts made using powder bed fusion (PBF) additive manufacturing often suffer from deformation, residual stresses, cracks, and other defects stemming from non-uniform thermal distribution during the printing process. Scan pattern (i.e., the geometric pattern of an infill) and scan sequence (i.e., the order in which features of a geometric pattern are scanned) are among the approaches that have been explored to achieve more uniform thermal distribution and reduce thermally-induced defects. The authors have recently proposed an intelligent approach (called SmartScan) for generating scan sequences. SmartScan is model-based and optimization-driven. However, it has only been applied to the most rudimentary scan patterns. This paper compares the separate and combined effects of an advanced scan pattern (the varying-helix pattern) and SmartScan on thermal distribution, part deformation, and printing time in PBF additive manufacturing. Simulations and experiments involving laser marking of AISI 316L stainless steel plates are employed for the comparison. Using SmartScan applied to a rudimentary pattern as a benchmark, the experimental results demonstrate that the application of the advanced pattern without SmartScan improved both temperature uniformity and reduced deformations by 20%, at the cost of 7% increase in printing time. The combination of the advanced pattern and SmartScan yielded 28% and 33% improvement in thermal uniformity and reduction in deformation, respectively, at the cost of 18% increase in scanning time.
Evaluation of Process Parameters Modifications on Directed Energy Deposition Manufactured Parts Obtained in a Hybrid Additive Manufacturing Machine
Authors: Henriques Ana Beatriz B. , de Aguiar Paola L. , dos Santos Raphael G. , Miagava Joice 
Published: September 2022

Abstract
In order to combine advantages of both additive and subtractive manufacturing, hybrid machine tools have been developed. In the hybrid process, directed energy deposition (DED) is the most used additive manufacturing technology due to its adaptability to CNC milling centers. However, in order to assure the integrity of a printed part, several process parameters must be set appropriately. Not only there are several parameters, but also some of these parameters influence different variables — e.g.: scan speed influences both the energy input per unit area and the powder volume that is deposited. In addition, another fact that complicates the achievement of a good quality in a workpiece is that some relevant parameters for additive manufacturing cannot be controlled due to CNC milling center constraints (e.g.: atmosphere). In this work, laser power (280 to 340 W) and scan speed (5 to 7 mm/s) were systematically varied to print 316L test samples with the aim of building a quality matrix. In the future, this matrix will be used to create strategies to optimize the quality of printed parts. Optical stereoscopy shows that the higher the laser power, the higher the sample, indicating that more powder is melted and deposited with an increasing laser power. By fixing the laser power and increasing the scan speed, printed samples were lower, indicating that less powder was deposited. Other parameters were preliminarily tested — e.g.: sample size and shield gas flow. Decreasing the sample size from 9 to 6 mm was sufficient to double the sample height, showing that the heat transfer rate was dramatically changed. Findings of this study shows that all process parameters act together and are determining factors for a good quality printed part. Moreover, it was noted that sample integrity is very sensitive to minimal changes in some process parameters.
Improving Geometric Accuracy in Wire and Arc Additive Manufacturing With Engineering-Informed Machine Learning
Authors: Ruiz Cesar , Jafari Davoud , Subramanian Vignesh Venkata , Vaneker Tom H. J. , Ya Wei , Huang Qiang 
Published: September 2022

Abstract
Wire and arc additive manufacturing (WAAM) is a promising technology for fast and cost-effective fabrication of large-scale components made of high-value materials for industries such as petroleum and aerospace. By using robotic arc welding and wire filler materials, WAAM can fabricate complex large near-net shape parts with high deposition rates, short lead times and millimeter resolution. However, due to high temperature gradients and residual stresses, current WAAM technologies suffer from high surface roughness and poor shape accuracy. This limits the adoption of these technologies in industry and complicates process control and optimization. Since its conception, considerable research efforts have been made on improving the mechanical and microstructural performance of WAAM components while few studies have investigated their geometric accuracy. In this work, we propose an engineering-informed machine learning (ML) framework for predicting and compensating for the geometric deformation of WAAM fabricated products based on a few sample parts. The proposed ML algorithm efficiently separates geometric shape deviation into deformation and surface roughness. Then, the predicted shape deformation of a new product is minimized by applying optimal geometric compensation to the product design. Experimental validation on cylindrical shapes showed that the proposed methodology can effectively reduce product shape deviation, which facilitates the widespread adoption of WAAM.
Enhanced Three-Dimensional Surface Profiling Technique Based on a Feature-Selective Segmentation and Merging
Authors: Guo Xiangyu , Lee ChaBum 
Published: September 2022

Abstract
This paper introduces an imaging technique to enhance three-dimensional (3D) surface profiling of the machined part by using a feature-selective segmentation (FSS) and merging technique. Spatially-resolved 3D stereoscopic images were achieved compared with those of the conventional filtering-based imaging process. Two identical vision cameras simultaneously take images of the parts at different angles, and 3D images can be reconstructed by stereoscopy algorithm. High-pass and low-pass filtering of the images involves data loss and lowers the spatial resolution of the image. In this study, the 3D reconstructed image resolution was significantly improved by automatically classifying and selectively segmenting the features on the 2D images, locally and adaptively applying super-resolution algorithm to the segmented images based on the classified features, and then merging those filtered segments. Here, the features are transformed into masks that selectively separate the features and background images for segmentation. The measurement system scanned the machined part with various shape and height information. The experimental results were compared with those of a conventional high-pass and low-pass filtering method in terms of spatial frequency and profile accuracy. As a result, the selective feature segmentation technique was capable of spatially-resolved 3D stereoscopic imaging while preserving imaging features. The proposed imaging method will be implemented with strobo-stereoscopy for in-process 3D surface imaging.
An Intelligent Switching Method of Multi-Resolution Models Oriented for Complex System Co-Simulation
Authors: Liu Wenzheng , Zhao Chun , Zhang Heming 
Published: September 2022

Abstract
With the development of Cyber-Physical Systems (CPS) technologies, many multi-disciplinary collaboration of complex systems that are difficult to solve in a single field can be addressed by simulation in the cyber world. Co-simulation is a typical solution to collaboration of distributed heterogeneous models for complex system. However, the problem of how to select proper simulation models with multi-resolution remains a technical gap for heterogeneous collaboration, which needs more intelligent simulation support provided in the cyber world. In this paper, an intelligent switching method of multi-resolution heterogeneous models oriented for co-simulation of complex systems is proposed. Timing advancing of models with more than one resolution level of simulation resources are incorporated in this architecture. Simulation resources in the form of plug-in-play function blocks can be scheduled by intelligent decision, which can extend the diversity of simulation tasks. After experiments of typical collaborative simulation of a robotic arm in an automatic cruise application scenario, analysis and results show that the proposed method can switch the multi-level simulation resources and trade off well between simulation efficiency and accuracy effectively.
Separation Process Comparison of Hydrogel Film and PTFE Film in Vat Photopolymerization
Authors: Yang Feimo , Kazi Aamer , Liu Caleb , Tai Bruce L. 
Published: September 2022

Abstract
In constrained surface vat photopolymerization, the separation process between a newly printed layer and the vat film has long been a limiting factor for printing speed and feature size. This paper aims to compare the performance of a hydrogel film and a conventionally used polytetrafluoroethylene (PTFE) in terms of separation forces, vertical separation distances, and dimensional accuracies of the printed parts. PTFE is commonly adopted because of its low surface energy and thus low separation force, while the hydrogel film is hypothetically effective because of its repelling nature to the non-polar characteristic in most photopolymers. A custom-designed building platform with an integrated sensor is used to continuously sample the force at 1,000Hz with 0.1N resolution. The separation distance is calculated based on the ascending and descending force profiles. The results show a 26% reduction in separation forces and a 60% reduction in vertical separation distances, with 95% statistical significance when comparing the hydrogel film to the PTFE film. The dimensional accuracies of produced parts in both films are similar.
Characterization of the Direct Write Inkjet Printing Process for Automated Fabrication of PEDOT: PSS Thin Films
Authors: Morice Sara , Sherehiy Andriy , Wei Danming , Popa Dan O. 
Published: September 2022

Abstract
Direct write Inkjet Printing is a versatile additive manufacturing technology that allows for the fabrication of multiscale structures with dimensions spanning from nano to cm scale. This is made possible due to the development of novel dispensing tools, enabling controlled and precise deposition of fluid with a wide range of viscosities (1 – 50 000 mPas) in nanoliter volumes. As a result, Inkjet printing has been recognized as a potential low-cost alternative for several established manufacturing methods, including cleanroom fabrication. In this paper, we present a characterization study of PEDOT: PSS polymer ink deposition printing process realized with the help of an automated, custom Direct Write Inkjet system. PEDOT: PSS is a highly conductive ink that possesses good film forming capabilities. Applications thus include printing thin films on flexible substrates for tactile (touch) sensors. We applied the Taguchi Design of Experiment (DOE) method to produce the optimal set of PEDOT:PSS ink dispensing parameters, to study their influence on the resulting ink droplet diameter. We experimentally determined that the desired outcome of a printed thin film with minimum thickness is directly related to 1) the minimum volume of dispensed fluid and 2) the presence of a preprocessing step, namely air plasma treatment of the Kapton substrate. Results show that an ink deposit with a minimum diameter of 482 μm, and a thin film with approximately 300 nm thickness were produced with good repeatability.
Cutting Process in Slot Milling of AISI420 Stainless Steel Manufactured in Additive Process
Authors: Tamura Shoichi , Ishikuri Takeru , Inazawa Katsufumi , Matsumura Takashi 
Published: September 2022

Abstract
Hybrid manufacturing involving additive and subtractive process in the same platform has been attracted to produce complex shaped parts with excellent surface finish and accuracy in surgical, aerospace, tool and die industries. The microstructures of metals fabricated by laser powder bed fusion are different from the wrought material. The machining of the material hardened in situ locally heat treated due to laser scanning is conducted without lubrication in the hybrid manufacturing. The paper discusses the milling process of AISI 420 martensitic stainless steel manufactured in additive process with comparing to a quenched wrought material and a quenched-tempered material. The cutting tests were conducted in slotting with a helical square end mill. The chip and the burr formations are discussed with the measured cutting forces. Then, the cutting forces are analyzed in a chip flow model, in which the three-dimensional chip flow is interpreted as a piling up of orthogonal cuttings in the planes containing cutting and chip flow velocities. The shear angle and the shear stress on the shear plane during chip formation are associated with the cutting force.
Learning the Temporal Effect in Infrared Thermal Videos With Long Short-Term Memory for Quality Prediction in Resistance Spot Welding
Authors: Guo Shenghan , Wang Dali , Chen Jian , Feng Zhili , Guo Weihong “Grace” 
Published: September 2022

Abstract
With the advances of sensing technology, in-situ infrared thermal videos can be collected from Resistance Spot Welding (RSW) processes. Each video records the formulation process of a weld nugget. The nugget evolution creates a “temporal effect” across the frames, which can be leveraged for real-time, nondestructive evaluation (NDE) of the weld quality. Currently, quality prediction with imaging data mainly focuses on optical feature extraction with Convolutional Neural Network (CNN) but does not make the most of such temporal effect. In this study, pixels corresponding to critical locations on the weld nugget surface are extracted from a video to form multivariate time series (MTS). Multivariate Adaptive Regression Splines (MARS) is used in MTS processing to remove noisy signals related to uninformative frames. A Stacked Long Short-Term Memory (LSTM) model is developed to learn from the processed MTS and then predicts weld nugget size and thickness in real-time NDE. Results from a case study on RSW of Boron steel demonstrates the improvement in prediction accuracy and computational time with the proposed method, as compared to CNN-based weld quality prediction.