F. Ducobu, Nithyaraaj Kugalur Palanisamy, G. Briffoteaux, M. Gobert, Daniel Tuyttens, Pedro Arrazola Arriola, E. Rivière-Lorphèvre
The evolution of high-performance computing facilitates the simulation of manufacturing processes. The prediction accuracy of a numerical model of the cutting process is closely associated with the selection of constitutive and friction models. The reliability and the accuracy of these models highly depend on the value of the parameters involved in the definition of the cutting process. These model parameters are determined using a direct method or an inverse method. However, these identification procedures often neglect the link between the parameters of the material and the friction models. This paper introduces a novel approach to inversely identify the best parameters value for both models at the same time and by taking into account multiple cutting conditions in the optimization routine. An Artificial Intelligence (AI) framework that combines the finite element modeling with an Adaptive Bayesian Multi-objective Evolutionary Algorithm (AB-MOEA) is developed, where the objective is to minimize the deviation between the experimental and the numerical results. The Arbitrary Lagrangian Eulerian (ALE) formulation and the Ti6Al4V alloy are selected to demonstrate its applicability. The investigation shows that the developed AI platform can identify the best parameters values with low computational time and resources. The identified parameters values predicted the cutting and feed forces within a deviation of less than 4% from the experiments for all the cutting conditions considered in this work.
{"title":"Identification of the Constitutive and Friction Models Parameters via a Multi-Objective Surrogate-Assisted Algorithm for the Modeling of Machining - Application to ALE orthogonal cutting of Ti6Al4V","authors":"F. Ducobu, Nithyaraaj Kugalur Palanisamy, G. Briffoteaux, M. Gobert, Daniel Tuyttens, Pedro Arrazola Arriola, E. Rivière-Lorphèvre","doi":"10.1115/1.4065223","DOIUrl":"https://doi.org/10.1115/1.4065223","url":null,"abstract":"\u0000 The evolution of high-performance computing facilitates the simulation of manufacturing processes. The prediction accuracy of a numerical model of the cutting process is closely associated with the selection of constitutive and friction models. The reliability and the accuracy of these models highly depend on the value of the parameters involved in the definition of the cutting process. These model parameters are determined using a direct method or an inverse method. However, these identification procedures often neglect the link between the parameters of the material and the friction models. This paper introduces a novel approach to inversely identify the best parameters value for both models at the same time and by taking into account multiple cutting conditions in the optimization routine. An Artificial Intelligence (AI) framework that combines the finite element modeling with an Adaptive Bayesian Multi-objective Evolutionary Algorithm (AB-MOEA) is developed, where the objective is to minimize the deviation between the experimental and the numerical results. The Arbitrary Lagrangian Eulerian (ALE) formulation and the Ti6Al4V alloy are selected to demonstrate its applicability. The investigation shows that the developed AI platform can identify the best parameters values with low computational time and resources. The identified parameters values predicted the cutting and feed forces within a deviation of less than 4% from the experiments for all the cutting conditions considered in this work.","PeriodicalId":507815,"journal":{"name":"Journal of Manufacturing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140769685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. P. Mahto, Md Perwej Iqbal, Kanchan Kumari, S. K. Pal
Friction Stir Welding (FSW) produces inhomogeneous mechanical and metallurgical properties in the weld, which further require post-weld processing to control the heterogeneity. In the present study, the heterogeneity in the weld is reduced through counter variable rotation friction stir welding (CVRFSW). The material flow and temperature distribution significantly affect the inhomogeneity of the FSWed properties which has been studied by developing a three-dimensional Lagrangian method-based viscoplastic model. The material flow, strain rate, and temperature distribution in conventional FSW (CFSW) and CVRFSW is studied quantitatively. The study revealed that CVRFSW improved joint strength and reduced the inhomogeneity of temperature, strain, and hardness. At a 10% lower shoulder speed than pin, the weld strength improved by 16%. The simulation predicted that the temperature difference between the advancing side (AS) and the retreating side (RS) was 36°C in CFSW, which reduced to 8°C in CVRFSW. Material deformation in CVRFSW occurred at a strain rate more than twice that of CFSW, and the asymmetry of strain rate between AS and RS reduced to one-fifth. Microstructures and their orientations of the welds were studied in detail. These findings contribute to the understanding of CVRFSW processes for enhanced weld quality and mechanical performance for industrial applications.
{"title":"Control of the Weld Heterogeneity by using A Novel Counter Variable Pin Shoulder Rotation Friction Stir Welding A Simulation and Experimental study","authors":"R. P. Mahto, Md Perwej Iqbal, Kanchan Kumari, S. K. Pal","doi":"10.1115/1.4065182","DOIUrl":"https://doi.org/10.1115/1.4065182","url":null,"abstract":"\u0000 Friction Stir Welding (FSW) produces inhomogeneous mechanical and metallurgical properties in the weld, which further require post-weld processing to control the heterogeneity. In the present study, the heterogeneity in the weld is reduced through counter variable rotation friction stir welding (CVRFSW). The material flow and temperature distribution significantly affect the inhomogeneity of the FSWed properties which has been studied by developing a three-dimensional Lagrangian method-based viscoplastic model. The material flow, strain rate, and temperature distribution in conventional FSW (CFSW) and CVRFSW is studied quantitatively. The study revealed that CVRFSW improved joint strength and reduced the inhomogeneity of temperature, strain, and hardness. At a 10% lower shoulder speed than pin, the weld strength improved by 16%. The simulation predicted that the temperature difference between the advancing side (AS) and the retreating side (RS) was 36°C in CFSW, which reduced to 8°C in CVRFSW. Material deformation in CVRFSW occurred at a strain rate more than twice that of CFSW, and the asymmetry of strain rate between AS and RS reduced to one-fifth. Microstructures and their orientations of the welds were studied in detail. These findings contribute to the understanding of CVRFSW processes for enhanced weld quality and mechanical performance for industrial applications.","PeriodicalId":507815,"journal":{"name":"Journal of Manufacturing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140380892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kuo King (“K.K.”) Wang was a pioneer in injection molding of polymers and non-polymer materials that helped the manufacturing industry around the world evolve to its current practices. He built an interdisciplinary research team at Cornell University that used a scientific base for analyzing the injection molding process by integrating and extending existing knowledge. He also pioneered and established a renowned consortium of global corporations. The corporations incorporated the research findings into their manufacturing processes. The relationships continued for decades and the research results are widely implemented in manufacturing around the world.
Kuo King（"K.K."）Wang 是聚合物和非聚合物材料注塑成型领域的先驱，帮助全球制造业发展到目前的做法。他在康奈尔大学建立了一个跨学科研究团队，通过整合和扩展现有知识，利用科学基础分析注塑成型工艺。他还开创并建立了一个著名的全球企业联盟。这些公司将研究成果融入其生产工艺中。这种合作关系持续了几十年，研究成果被广泛应用于世界各地的制造业。
{"title":"Kuo King Wang Memorial Tribute","authors":"Albert Shih","doi":"10.1115/1.4065181","DOIUrl":"https://doi.org/10.1115/1.4065181","url":null,"abstract":"\u0000 Kuo King (“K.K.”) Wang was a pioneer in injection molding of polymers and non-polymer materials that helped the manufacturing industry around the world evolve to its current practices. He built an interdisciplinary research team at Cornell University that used a scientific base for analyzing the injection molding process by integrating and extending existing knowledge. He also pioneered and established a renowned consortium of global corporations. The corporations incorporated the research findings into their manufacturing processes. The relationships continued for decades and the research results are widely implemented in manufacturing around the world.","PeriodicalId":507815,"journal":{"name":"Journal of Manufacturing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140378859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Boyang Xu, Hasnaa Ouidadi, Nicole Van Handel, Shenghan Guo
Defects shape, volume, and orientation all have a direct impact on the mechanical properties of Laser Powder Bed Fused (L-PBF-ed) parts. Therefore, it is necessary to evaluate and analyze the 3-dimensional (3D) geometrical characteristics of these defects. X-ray Computed Tomography (XCT) can reveal an object's internal structure by volumetric scanning through its building direction. Point clouds are 3D data that can be extracted from the stack of XCT images taken from a part to perform further analysis. This study presents a novel approach for 3D segmentation and geometrical analysis of L-PBF defect structures from XCT images. The proposed method integrates Voronoi labeling and 3D point cloud reconstruction to reveal individual defect characteristics from the XCT image stack of a part. A case study showed the proposed methodology's effectiveness to identify and characterize defect regions in L-PBF-ed Cobalt Chrome (CoCr) parts.
{"title":"3D X-ray Computed Tomography (XCT) Image Segmentation and Point Cloud Reconstruction for Internal Defect Identification in Laser Powder Bed Fused Parts","authors":"Boyang Xu, Hasnaa Ouidadi, Nicole Van Handel, Shenghan Guo","doi":"10.1115/1.4065179","DOIUrl":"https://doi.org/10.1115/1.4065179","url":null,"abstract":"\u0000 Defects shape, volume, and orientation all have a direct impact on the mechanical properties of Laser Powder Bed Fused (L-PBF-ed) parts. Therefore, it is necessary to evaluate and analyze the 3-dimensional (3D) geometrical characteristics of these defects. X-ray Computed Tomography (XCT) can reveal an object's internal structure by volumetric scanning through its building direction. Point clouds are 3D data that can be extracted from the stack of XCT images taken from a part to perform further analysis. This study presents a novel approach for 3D segmentation and geometrical analysis of L-PBF defect structures from XCT images. The proposed method integrates Voronoi labeling and 3D point cloud reconstruction to reveal individual defect characteristics from the XCT image stack of a part. A case study showed the proposed methodology's effectiveness to identify and characterize defect regions in L-PBF-ed Cobalt Chrome (CoCr) parts.","PeriodicalId":507815,"journal":{"name":"Journal of Manufacturing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140379901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a new method to efficiently update workpiece and determine cutter-workpiece engagement (CWE) in multi-axis milling simulation based on a uniform voxel modeling space. At each cutter location, a novel algorithm named as direct voxel tracing is developed and used to generate a functional cutter surface voxel model to reliably establish the internal space of the milling cutter. The cutter internal space is represented by its voxel boundary with small memory usage. Through the Boolean subtraction between two successive voxel boundaries of the cutter internal space, a minimal voxel deactivation region is attained within which all active workpiece voxels are deactivated (removed) to update the workpiece model. To determine the associated CWE map, a 3D circle voxelization algorithm is employed. By slicing the cutter surface by a sequence of planes perpendicular with and along the cutter axis, CWE can be determined as the sliced 3D circles are voxelized. Quantitative comparisons of the proposed method against existing voxel modeling and vector modeling based methods have been made. The results have demonstrated much improved computational efficiency of the proposed method in simulating the complex multi-axis milling operations.
{"title":"Efficient Voxel-Based Workpiece Update and Cutter-Workpiece Engagement Determination in Multi-Axis Milling","authors":"Zhengwen Nie, Hsi-Yung Feng","doi":"10.1115/1.4065180","DOIUrl":"https://doi.org/10.1115/1.4065180","url":null,"abstract":"\u0000 This paper presents a new method to efficiently update workpiece and determine cutter-workpiece engagement (CWE) in multi-axis milling simulation based on a uniform voxel modeling space. At each cutter location, a novel algorithm named as direct voxel tracing is developed and used to generate a functional cutter surface voxel model to reliably establish the internal space of the milling cutter. The cutter internal space is represented by its voxel boundary with small memory usage. Through the Boolean subtraction between two successive voxel boundaries of the cutter internal space, a minimal voxel deactivation region is attained within which all active workpiece voxels are deactivated (removed) to update the workpiece model. To determine the associated CWE map, a 3D circle voxelization algorithm is employed. By slicing the cutter surface by a sequence of planes perpendicular with and along the cutter axis, CWE can be determined as the sliced 3D circles are voxelized. Quantitative comparisons of the proposed method against existing voxel modeling and vector modeling based methods have been made. The results have demonstrated much improved computational efficiency of the proposed method in simulating the complex multi-axis milling operations.","PeriodicalId":507815,"journal":{"name":"Journal of Manufacturing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140378482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The in-situ imaging of the cutting process exhibits outstanding advantages in reconstructing the precise and visual thermoplastic deformation fields. The physical and geometric characteristics of deformation fields provide a deeper understanding of the cutting processes. In this paper, a mechanism-image hybrid analysis method is proposed to acquire the characteristics of the serrated chip deformation in the orthogonal cutting of TA15 titanium alloy based on in-situ imaging. The established hybrid analysis method combines the shear-plane theory with the streamline method and image segmentation method, which realizes the identification of pixel coordinates of the main shear plane (MSP) and the primary shear zone (PSZ) and then the extraction of the physical and geometric variables from the digital image correlation (DIC) full-field measurements. Consequently, the variations of equivalent strain rate, strain, temperature, and the geometric characterizations of MSP and PSZ during an individual serration formation of TA15 titanium alloy were quantitatively investigated. It was found that the physical and geometric variables reached stability in the final stage of serration evolution and were averaged as the DIC-based equivalent characterizations to analyze the impact of cutting depth and tool rake angle. Meanwhile, the DIC-based equivalent characterizations were compared with the results obtained by the classical analytical models to illustrate the advantages of the DIC-based analysis. The findings also support that the established hybrid analysis method holds the potential to characterize the serrated chip formation of other materials and improve the models of PSZ.
{"title":"Characterization of serrated chip formation based on in-situ imaging analysis in orthogonal cutting","authors":"Minghui Yang, Yufei Tang, Chaoqun Wu, Shiyu Cao, Wenjian Huang, Xuyan Zhang","doi":"10.1115/1.4065136","DOIUrl":"https://doi.org/10.1115/1.4065136","url":null,"abstract":"\u0000 The in-situ imaging of the cutting process exhibits outstanding advantages in reconstructing the precise and visual thermoplastic deformation fields. The physical and geometric characteristics of deformation fields provide a deeper understanding of the cutting processes. In this paper, a mechanism-image hybrid analysis method is proposed to acquire the characteristics of the serrated chip deformation in the orthogonal cutting of TA15 titanium alloy based on in-situ imaging. The established hybrid analysis method combines the shear-plane theory with the streamline method and image segmentation method, which realizes the identification of pixel coordinates of the main shear plane (MSP) and the primary shear zone (PSZ) and then the extraction of the physical and geometric variables from the digital image correlation (DIC) full-field measurements. Consequently, the variations of equivalent strain rate, strain, temperature, and the geometric characterizations of MSP and PSZ during an individual serration formation of TA15 titanium alloy were quantitatively investigated. It was found that the physical and geometric variables reached stability in the final stage of serration evolution and were averaged as the DIC-based equivalent characterizations to analyze the impact of cutting depth and tool rake angle. Meanwhile, the DIC-based equivalent characterizations were compared with the results obtained by the classical analytical models to illustrate the advantages of the DIC-based analysis. The findings also support that the established hybrid analysis method holds the potential to characterize the serrated chip formation of other materials and improve the models of PSZ.","PeriodicalId":507815,"journal":{"name":"Journal of Manufacturing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140231301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shu-Kai S. Fan, Pei-Chen Chen, Chih-Hung Jen, Kanchana Sethanan
Semiconductor manufacturing technology has been developing rapidly in the last decade, and the advanced process control methodology has also made considerable progress due to the birth of machine learning and deep learning. In practical semiconductor processes, the defect analysis for wafer map is a critical step for improving product quality and yield. These defect patterns can provide important process information so that the process engineers can identify the key cause of process anomalies. To capture the expert knowledge, the methods in ML/DL are applied extensively such that a robust and long-lasting effect in APC can be established. However, in supervised learning, the manual annotation for wafer map is an extremely exhausting task, and it can also induce the misjudgment when a long-term operation is implemented. This end, this paper proposes a new auto-labeling system based on ensemble classification. The noted VGG16 model is used in ensemble learning as the building block to train the classifier via a limited number of labeled data. Through the model being trained, the auto-labeling procedure is executed to annotate abundant unlabeled data. Therefore, the classification performances between the models trained by supervised and semi-supervised learning can be compared. In addition, the gradient weighted class activation mapping is also adopted to analyze and verify the quality of auto-labeling by visual inspection. The classification performance for wafer defect patterns can be further assured by providing confidence scores of specific defect patterns.
{"title":"Auto-Labeling for Pattern Recognition of Wafer Defect Maps in Semiconductor Manufacturing","authors":"Shu-Kai S. Fan, Pei-Chen Chen, Chih-Hung Jen, Kanchana Sethanan","doi":"10.1115/1.4065118","DOIUrl":"https://doi.org/10.1115/1.4065118","url":null,"abstract":"\u0000 Semiconductor manufacturing technology has been developing rapidly in the last decade, and the advanced process control methodology has also made considerable progress due to the birth of machine learning and deep learning. In practical semiconductor processes, the defect analysis for wafer map is a critical step for improving product quality and yield. These defect patterns can provide important process information so that the process engineers can identify the key cause of process anomalies. To capture the expert knowledge, the methods in ML/DL are applied extensively such that a robust and long-lasting effect in APC can be established. However, in supervised learning, the manual annotation for wafer map is an extremely exhausting task, and it can also induce the misjudgment when a long-term operation is implemented. This end, this paper proposes a new auto-labeling system based on ensemble classification. The noted VGG16 model is used in ensemble learning as the building block to train the classifier via a limited number of labeled data. Through the model being trained, the auto-labeling procedure is executed to annotate abundant unlabeled data. Therefore, the classification performances between the models trained by supervised and semi-supervised learning can be compared. In addition, the gradient weighted class activation mapping is also adopted to analyze and verify the quality of auto-labeling by visual inspection. The classification performance for wafer defect patterns can be further assured by providing confidence scores of specific defect patterns.","PeriodicalId":507815,"journal":{"name":"Journal of Manufacturing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140232594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The spindle determines the performance of machine tools, hence monitoring its health is essential to maintain the machining productivity and avoid costly downtimes. The magnitudes and locations of wear and cracks in the bearing balls and races gradually develop which are difficult to detect. This article presents a physics-based digital model of the spindle with bearing faults, worn contact interface between the shaft and tool holder, and spindle imbalance. The wear of races and balls is considered in the bearing model. The worn taper contact interface and the spindle imbalance are included in the digital model. The spindle's dynamic model is used to simulate the vibrations at any location in the spindle assembly where sensors can be mounted for online monitoring. The wear type and bearing location is correlated with the frequency spectrum of vibrations at operating speeds. The proposed fault models are used to analyzed the critical signal features and experimentally validated by the frequency extracted from a damaged spindle in Part II. The physics-based digital model is used to train data analytic models to detect spindle faults in Part III.
{"title":"A Physics-based Model-data-driven Method for Spindle Health Diagnosis, Part I: Modeling of Geometric Faults","authors":"Chung-Yu Tai, Yusuf Altintas","doi":"10.1115/1.4065062","DOIUrl":"https://doi.org/10.1115/1.4065062","url":null,"abstract":"\u0000 The spindle determines the performance of machine tools, hence monitoring its health is essential to maintain the machining productivity and avoid costly downtimes. The magnitudes and locations of wear and cracks in the bearing balls and races gradually develop which are difficult to detect. This article presents a physics-based digital model of the spindle with bearing faults, worn contact interface between the shaft and tool holder, and spindle imbalance. The wear of races and balls is considered in the bearing model. The worn taper contact interface and the spindle imbalance are included in the digital model. The spindle's dynamic model is used to simulate the vibrations at any location in the spindle assembly where sensors can be mounted for online monitoring. The wear type and bearing location is correlated with the frequency spectrum of vibrations at operating speeds. The proposed fault models are used to analyzed the critical signal features and experimentally validated by the frequency extracted from a damaged spindle in Part II. The physics-based digital model is used to train data analytic models to detect spindle faults in Part III.","PeriodicalId":507815,"journal":{"name":"Journal of Manufacturing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140243484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shiwei Deng, Yancheng Wang, Jiafeng Cheng, Wenjie Shen, Deqing Mei
Silicon carbide (SiC) has been widely utilized in semiconductor industry for the development of high power electrical devices. Using chemical vapor deposition (CVD) to grow a thin epitaxial layer onto SiC substrate surface with orderly lattice arrangement, good surface morphology and low doping concentration is required. During epitaxial growth, the high reaction temperature and its distribution are generally difficult to measure and will affect the properties of epitaxial growth layer. This study presents a thermal-field testing method based on process temperature control rings (PTCRs) to measure the high temperature distribution inside the epitaxial growth reaction chamber, and to study the effects of reaction chamber structure and epitaxial growth parameters on the quality of epitaxial layer. The measurement accuracy of PTCRs was characterized using silicon melting experiments and the measuring principle of PTCRs was presented. The thermal field of the reaction chamber was then numerically simulated and compared with experimental results. The experiment results exhibit a temperature gradient of less than 0.4 °C/mm on surface, indicating good temperature uniformity. Epitaxial growth is an essential process in the fabrication of SiC devices, as it enables the production of layers with precise doping density and thickness. The SiC epitaxial growth experiments were conducted to study the effects of gas flow ratio and doping flow ratio of three inlet flow channels on the thickness and doping concentration distributions. The results demonstrated that the non-uniformity of thickness and doping concentration of epitaxial layer was below 1.5 % and 4.0 %, respectively.
{"title":"Measurement of Thermal Field Temperature Distribution Inside Reaction Chamber for Epitaxial Growth of Silicon Carbide Layer","authors":"Shiwei Deng, Yancheng Wang, Jiafeng Cheng, Wenjie Shen, Deqing Mei","doi":"10.1115/1.4065021","DOIUrl":"https://doi.org/10.1115/1.4065021","url":null,"abstract":"\u0000 Silicon carbide (SiC) has been widely utilized in semiconductor industry for the development of high power electrical devices. Using chemical vapor deposition (CVD) to grow a thin epitaxial layer onto SiC substrate surface with orderly lattice arrangement, good surface morphology and low doping concentration is required. During epitaxial growth, the high reaction temperature and its distribution are generally difficult to measure and will affect the properties of epitaxial growth layer. This study presents a thermal-field testing method based on process temperature control rings (PTCRs) to measure the high temperature distribution inside the epitaxial growth reaction chamber, and to study the effects of reaction chamber structure and epitaxial growth parameters on the quality of epitaxial layer. The measurement accuracy of PTCRs was characterized using silicon melting experiments and the measuring principle of PTCRs was presented. The thermal field of the reaction chamber was then numerically simulated and compared with experimental results. The experiment results exhibit a temperature gradient of less than 0.4 °C/mm on surface, indicating good temperature uniformity. Epitaxial growth is an essential process in the fabrication of SiC devices, as it enables the production of layers with precise doping density and thickness. The SiC epitaxial growth experiments were conducted to study the effects of gas flow ratio and doping flow ratio of three inlet flow channels on the thickness and doping concentration distributions. The results demonstrated that the non-uniformity of thickness and doping concentration of epitaxial layer was below 1.5 % and 4.0 %, respectively.","PeriodicalId":507815,"journal":{"name":"Journal of Manufacturing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140258421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Laser shock peening (LSP) is investigated for its use in altering the electrochemical and wetting behavior of 316L stainless steel made with laser powder bed fusion (LPBF). The corrosion performance of LPBF stainless steel varies between studies and build parameters, thus motivating the search for postprocessing methods that enable wetted surface applications. Compressive surface stress has been demonstrated to reduce corrosion rate in additively manufactured metal and LSP is known to impart compressive residual stress into metal targets. Wettability also affects corrosion behavior and LSP induces hydrophobicity. LSP is therefore a promising tool for improving corrosion behavior of LPBF stainless steel. This paper examines the electrochemical properties of LPBF stainless steel before and after LSP with electrochemical impedance spectroscopy and potentiokinetic measurements. Contact angle, surface free energy, and surface finish are studied with dynamic contact angle measurements and profilometry. X-ray diffraction and energy-dispersive X-ray spectroscopy measures residual stress and surface chemistry. The top surface perpendicular to the build direction (XY) and the wall surface parallel with the build direction (XZ) are studied for all measurements due to the large differences in roughness and mechanical properties between these surfaces. LSP increases pitting potential for both XY and XZ surfaces and causes an increase to the surface electrochemical impedance. LSP also increases the contact angle of liquids on both surfaces. These changes to electrochemistry and wettability are attributed in part to surface morphology and surface chemistry alterations as well as the inducement of compressive residual stress.
{"title":"Effect of Laser Shock Peening on Electrochemistry and Wettability of Additively Manufactured Stainless Steel","authors":"Veronica Over, Y. L. Yao","doi":"10.1115/1.4065022","DOIUrl":"https://doi.org/10.1115/1.4065022","url":null,"abstract":"\u0000 Laser shock peening (LSP) is investigated for its use in altering the electrochemical and wetting behavior of 316L stainless steel made with laser powder bed fusion (LPBF). The corrosion performance of LPBF stainless steel varies between studies and build parameters, thus motivating the search for postprocessing methods that enable wetted surface applications. Compressive surface stress has been demonstrated to reduce corrosion rate in additively manufactured metal and LSP is known to impart compressive residual stress into metal targets. Wettability also affects corrosion behavior and LSP induces hydrophobicity. LSP is therefore a promising tool for improving corrosion behavior of LPBF stainless steel. This paper examines the electrochemical properties of LPBF stainless steel before and after LSP with electrochemical impedance spectroscopy and potentiokinetic measurements. Contact angle, surface free energy, and surface finish are studied with dynamic contact angle measurements and profilometry. X-ray diffraction and energy-dispersive X-ray spectroscopy measures residual stress and surface chemistry. The top surface perpendicular to the build direction (XY) and the wall surface parallel with the build direction (XZ) are studied for all measurements due to the large differences in roughness and mechanical properties between these surfaces. LSP increases pitting potential for both XY and XZ surfaces and causes an increase to the surface electrochemical impedance. LSP also increases the contact angle of liquids on both surfaces. These changes to electrochemistry and wettability are attributed in part to surface morphology and surface chemistry alterations as well as the inducement of compressive residual stress.","PeriodicalId":507815,"journal":{"name":"Journal of Manufacturing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140258919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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