Cutting force identification is critical to improving industrial robot performance and reducing machining vibration. However, most indirect identification methods of cutting force are not applicable since the dynamic characteristics of the robotic milling system vary with the robot pose. In this paper, a novel pose-dependent method is proposed to identify the cutting force using the acceleration signal generated by robotic milling. Firstly, the modal parameters of the robot at different machining points are used as a training dataset to develop the Gaussian Process Regression (GPR) model. Next, the modal parameters predicted by the GPR model are used to optimize the cutting force estimation based on the minimum variance unbiased estimate method. Then, the Kalman filter method is used to update the covariance matrix of the cutting force identification error and the state estimation error. Lastly, the proposed method is verified with the experiment, and the results show that the identification error and time are acceptable under the condition of variable robot pose.
{"title":"Pose-dependent Cutting Force Identification for Robotic Milling","authors":"Maxiao Hou, Hongru Cao, Yang Luo, Yanjie Guo","doi":"10.1115/1.4062145","DOIUrl":"https://doi.org/10.1115/1.4062145","url":null,"abstract":"\u0000 Cutting force identification is critical to improving industrial robot performance and reducing machining vibration. However, most indirect identification methods of cutting force are not applicable since the dynamic characteristics of the robotic milling system vary with the robot pose. In this paper, a novel pose-dependent method is proposed to identify the cutting force using the acceleration signal generated by robotic milling. Firstly, the modal parameters of the robot at different machining points are used as a training dataset to develop the Gaussian Process Regression (GPR) model. Next, the modal parameters predicted by the GPR model are used to optimize the cutting force estimation based on the minimum variance unbiased estimate method. Then, the Kalman filter method is used to update the covariance matrix of the cutting force identification error and the state estimation error. Lastly, the proposed method is verified with the experiment, and the results show that the identification error and time are acceptable under the condition of variable robot pose.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44107443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chatter is one of the major issues that cause undesirable effects limiting machining productivity. Passive control devices, such as tuned mass dampers (TMDs), have been widely employed to increase machining stability by suppressing chatter. More recently, inerter-based devices have been developed for a wide variety of engineering vibration mitigation applications. However, no experimental study for the application of inerters to the machining stability problem has yet been conducted. This paper presents an implementation of an inerter-based dynamic vibration absorber (IDVA) to the problem of chatter stability, for the first time. For this, it employs the IDVA with a pivoted-bar inerter developed in [1] to mitigate the chatter effect under cutting forces in milling. Due to the nature of machining stability, the optimal design parameters for the IDVA are numerically obtained by considering the real part of the frequency response function (FRF) which enables the absolute stability limit in a single degree-of-freedom (SDOF) to be maximised for a milling operation. Chatter performance is experimentally validated through milling trials using the prototype IDVA and a flexible workpiece. The experimental results show that the IDVA provides more than 15% improvement in the absolute stability limit compared to a classical TMD.
{"title":"Implementation of inerter-based dynamic vibration absorber for chatter suppression","authors":"H. Dogan, N. Sims, D. Wagg","doi":"10.1115/1.4062118","DOIUrl":"https://doi.org/10.1115/1.4062118","url":null,"abstract":"\u0000 Chatter is one of the major issues that cause undesirable effects limiting machining productivity. Passive control devices, such as tuned mass dampers (TMDs), have been widely employed to increase machining stability by suppressing chatter. More recently, inerter-based devices have been developed for a wide variety of engineering vibration mitigation applications. However, no experimental study for the application of inerters to the machining stability problem has yet been conducted. This paper presents an implementation of an inerter-based dynamic vibration absorber (IDVA) to the problem of chatter stability, for the first time. For this, it employs the IDVA with a pivoted-bar inerter developed in [1] to mitigate the chatter effect under cutting forces in milling. Due to the nature of machining stability, the optimal design parameters for the IDVA are numerically obtained by considering the real part of the frequency response function (FRF) which enables the absolute stability limit in a single degree-of-freedom (SDOF) to be maximised for a milling operation. Chatter performance is experimentally validated through milling trials using the prototype IDVA and a flexible workpiece. The experimental results show that the IDVA provides more than 15% improvement in the absolute stability limit compared to a classical TMD.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44177303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Zare, M. Tunesi, T. Harriman, John R. Troutman, M. Davies, D. Lucca
Single crystal Ge is a semiconductor that has broad applications, especially in manipulation of infra-red (IR) light. Diamond machining enables the efficient production of surfaces with tolerances required by the optical industry. During machining of anisotropic single crystals, the cutting direction with respect to the in-plane lattice orientation plays a fundamental role in the final quality of the surface and subsurface. In this study, on-axis face turning experiments were performed on an undoped (111)Ge wafer to investigate the effects of crystal anisotropy and feedrate on the surface and subsurface condition. Atomic force microscopy and scanning white light interferometry were used to characterize the presence of brittle fracture on the machined surfaces and to evaluate the resultant surface roughness. Raman spectroscopy was performed to evaluate the residual stresses and lattice disorder induced by the tool during machining. Nanoindentation with Berkovich and cube corner indenter tips was performed to evaluate elastic modulus, hardness, and fracture toughness of the machined surfaces and to study their variations with feedrate and cutting direction. Post-indentation studies of selected indentations were also performed to characterize the corresponding quasi-plasticity mechanisms. It was found that an increase of feedrate produced a rotation of the resultant force imparted by the tool indication a shift from indentation-dominant to cutting-dominant behavior. Fracture increased with the feedrate and showed a higher propensity when the cutting direction belonged to the <112¯> family.
{"title":"Face Turning of Single Crystal (111)Ge: Cutting Mechanics and Surface/Subsurface Characteristics","authors":"A. Zare, M. Tunesi, T. Harriman, John R. Troutman, M. Davies, D. Lucca","doi":"10.1115/1.4057054","DOIUrl":"https://doi.org/10.1115/1.4057054","url":null,"abstract":"\u0000 Single crystal Ge is a semiconductor that has broad applications, especially in manipulation of infra-red (IR) light. Diamond machining enables the efficient production of surfaces with tolerances required by the optical industry. During machining of anisotropic single crystals, the cutting direction with respect to the in-plane lattice orientation plays a fundamental role in the final quality of the surface and subsurface. In this study, on-axis face turning experiments were performed on an undoped (111)Ge wafer to investigate the effects of crystal anisotropy and feedrate on the surface and subsurface condition. Atomic force microscopy and scanning white light interferometry were used to characterize the presence of brittle fracture on the machined surfaces and to evaluate the resultant surface roughness. Raman spectroscopy was performed to evaluate the residual stresses and lattice disorder induced by the tool during machining. Nanoindentation with Berkovich and cube corner indenter tips was performed to evaluate elastic modulus, hardness, and fracture toughness of the machined surfaces and to study their variations with feedrate and cutting direction. Post-indentation studies of selected indentations were also performed to characterize the corresponding quasi-plasticity mechanisms. It was found that an increase of feedrate produced a rotation of the resultant force imparted by the tool indication a shift from indentation-dominant to cutting-dominant behavior. Fracture increased with the feedrate and showed a higher propensity when the cutting direction belonged to the <112¯> family.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42249345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bo Pan, R. Kang, Xu Zhu, Zhe Yang, Juntao Zhang, Jiang Guo
Double-sided lapping (DSL) is a precision process widely used for machining flat workpieces, such as optical windows, wafers, and brake pads owing to its high efficiency and parallelism. However, the mechanism of parallelism error reduced by the DSL process was rarely investigated. Furthermore, the relationship between parallelism and the flatness was not clearly illustrated. To explain why the parallelism of workpieces becomes convergent by the DSL, a theoretical model has been developed in this paper by calculating the parallelism evolution with the consideration of variation contact situations between workpieces and lapping plates for the first time. Moreover, several workpieces, including a slanted one rendering the model close to the actual process, are taken to calculate the parallelism evolution, and the mechanism of the parallelism error reduced by the DSL process is clarified. The calculation result has indicated that the parallelism error was reduced from 100.0 μm to 25.6 μm based on the parallelism evolution model. The experimental results showed that the parallelism improved from 108.6 μm to 28.2 μm, which agreed with the theoretical results well.
{"title":"Why parallelism of workpieces becomes convergent during double-sided lapping?","authors":"Bo Pan, R. Kang, Xu Zhu, Zhe Yang, Juntao Zhang, Jiang Guo","doi":"10.1115/1.4057053","DOIUrl":"https://doi.org/10.1115/1.4057053","url":null,"abstract":"\u0000 Double-sided lapping (DSL) is a precision process widely used for machining flat workpieces, such as optical windows, wafers, and brake pads owing to its high efficiency and parallelism. However, the mechanism of parallelism error reduced by the DSL process was rarely investigated. Furthermore, the relationship between parallelism and the flatness was not clearly illustrated. To explain why the parallelism of workpieces becomes convergent by the DSL, a theoretical model has been developed in this paper by calculating the parallelism evolution with the consideration of variation contact situations between workpieces and lapping plates for the first time. Moreover, several workpieces, including a slanted one rendering the model close to the actual process, are taken to calculate the parallelism evolution, and the mechanism of the parallelism error reduced by the DSL process is clarified. The calculation result has indicated that the parallelism error was reduced from 100.0 μm to 25.6 μm based on the parallelism evolution model. The experimental results showed that the parallelism improved from 108.6 μm to 28.2 μm, which agreed with the theoretical results well.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42053092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Editorial","authors":"Albert Shih, Ajay P. Malshe","doi":"10.1115/1.4057045","DOIUrl":"https://doi.org/10.1115/1.4057045","url":null,"abstract":"\u0000 May 2023 Editorial","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42825804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Industrial robots have become a suitable alternative to machine tools due to their great flexibility, low cost, and large working space. However, the deformation and vibration caused by the cutting forces during machining result in poor machining accuracy and surface quality. In order to improve the machining performance of the robot, this paper proposes a posture optimization method for robotic milling with the redundant degree of freedom of the industrial robot. First, modal tests are conducted in the robotic workspace to obtain the parameters of the structural dynamics of the robotic milling system. Then, considering the dynamics model of the system, the optimization model based on surface location error (SLE) is proposed to obtain the optimal robotic posture. Finally, a series of experiments illustrate that pose optimization based on SLE can improve the machining accuracy and surface machining quality.
{"title":"Pose optimization in robotic milling based on surface location error","authors":"Teng-fei Hou, Yang Lei, Ye Ding","doi":"10.1115/1.4057055","DOIUrl":"https://doi.org/10.1115/1.4057055","url":null,"abstract":"\u0000 Industrial robots have become a suitable alternative to machine tools due to their great flexibility, low cost, and large working space. However, the deformation and vibration caused by the cutting forces during machining result in poor machining accuracy and surface quality. In order to improve the machining performance of the robot, this paper proposes a posture optimization method for robotic milling with the redundant degree of freedom of the industrial robot. First, modal tests are conducted in the robotic workspace to obtain the parameters of the structural dynamics of the robotic milling system. Then, considering the dynamics model of the system, the optimization model based on surface location error (SLE) is proposed to obtain the optimal robotic posture. Finally, a series of experiments illustrate that pose optimization based on SLE can improve the machining accuracy and surface machining quality.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44879669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ayantha Senanayaka, Wenmeng Tian, T. Falls, L. Bian
This study aims to develop an intelligent, rapid porosity prediction methodology for varying process conditions based on knowledge transfer from the existing process conditions. Conventional machine learning algorithms are extensively used in porosity prediction. However, these approaches assume that the training (source) and testing (target) data follow the same probability distribution, and the labeled data are available in both source and target domains. The source and target do not follow the same distribution in real-world manufacturing environments. The diversity of industrialization processes leads to heterogeneous data collection in different production conditions, and labeling is costly. Transfer learning is one of the robust techniques that enables transferring learned knowledge between source and target to establish a relationship while the target has less data. Therefore, this paper presents similarity-based multi-source transfer learning(SiMuS-TL) method to develop a relationship between a source and an unknown target. The similarities between sources and targets are learned by forming a new domain called the mixed domain, which organizes data into identity groups. Then, a group-based learning process is designated to transfer knowledge to make target predictions. The effectiveness of the SiMuS-TL is explored with the application of porosity prediction in additively manufactured parts in realistic situations, i.e., single-source and multi-sources transfer to unknown target porosity prediction. The porosity prediction accuracies are approximately 90% for both scenarios with the SiMuS-TL method, but conventional SVM and CNN classifiers barely perform well in predicting porosity while process condition varies.
{"title":"Understanding the Effects of Process Conditions on Thermal-Defect Relationship: A Transfer Machine Learning Approach","authors":"Ayantha Senanayaka, Wenmeng Tian, T. Falls, L. Bian","doi":"10.1115/1.4057052","DOIUrl":"https://doi.org/10.1115/1.4057052","url":null,"abstract":"\u0000 This study aims to develop an intelligent, rapid porosity prediction methodology for varying process conditions based on knowledge transfer from the existing process conditions. Conventional machine learning algorithms are extensively used in porosity prediction. However, these approaches assume that the training (source) and testing (target) data follow the same probability distribution, and the labeled data are available in both source and target domains. The source and target do not follow the same distribution in real-world manufacturing environments. The diversity of industrialization processes leads to heterogeneous data collection in different production conditions, and labeling is costly. Transfer learning is one of the robust techniques that enables transferring learned knowledge between source and target to establish a relationship while the target has less data. Therefore, this paper presents similarity-based multi-source transfer learning(SiMuS-TL) method to develop a relationship between a source and an unknown target. The similarities between sources and targets are learned by forming a new domain called the mixed domain, which organizes data into identity groups. Then, a group-based learning process is designated to transfer knowledge to make target predictions. The effectiveness of the SiMuS-TL is explored with the application of porosity prediction in additively manufactured parts in realistic situations, i.e., single-source and multi-sources transfer to unknown target porosity prediction. The porosity prediction accuracies are approximately 90% for both scenarios with the SiMuS-TL method, but conventional SVM and CNN classifiers barely perform well in predicting porosity while process condition varies.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41297803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In profile bending, the geometrical defect that reduces dimensional quality is mainly due to springback. While predicting dimensions after bending is important for quality control, many factors during bending cause difficulty in springback prediction. Furthermore, complex three-dimensional (3D) shapes in bending can make springback prediction significantly more difficult. This work presents a springback prediction method for varying curvature 3D profile/tube bending. An advanced 5-axis bending machine, with rotary semi-dies opposing each other, has been developed. As the geometry of the bend die constrains the workpiece in the bending region, a model for 3D stretch bending is established from the rotational motion of the bend die. The discretized curvature of a bent profile geometry is described by using the Frenet-Serret frames, and a model for springback prediction is further developed based on the kinematics analysis of the bending process. This generalized analytical approach and numerical simulation are applied to evaluate springback in both 2D and 3D stretch bending of a thin-walled aluminum profile with a rectangular cross-section. The analytical and numerical results for springback prediction are validated with experiments, showing good agreement. The developed model is able to evaluate 3D springback of a profile with arbitrary cross-section efficiently for product design and development.
{"title":"On kinematics in sequential three-dimensional (3D) stretch bending: Analytical springback model","authors":"T. Ha, T. Welo, G. Ringen, Jyhwen Wang","doi":"10.1115/1.4057027","DOIUrl":"https://doi.org/10.1115/1.4057027","url":null,"abstract":"In profile bending, the geometrical defect that reduces dimensional quality is mainly due to springback. While predicting dimensions after bending is important for quality control, many factors during bending cause difficulty in springback prediction. Furthermore, complex three-dimensional (3D) shapes in bending can make springback prediction significantly more difficult. This work presents a springback prediction method for varying curvature 3D profile/tube bending. An advanced 5-axis bending machine, with rotary semi-dies opposing each other, has been developed. As the geometry of the bend die constrains the workpiece in the bending region, a model for 3D stretch bending is established from the rotational motion of the bend die. The discretized curvature of a bent profile geometry is described by using the Frenet-Serret frames, and a model for springback prediction is further developed based on the kinematics analysis of the bending process. This generalized analytical approach and numerical simulation are applied to evaluate springback in both 2D and 3D stretch bending of a thin-walled aluminum profile with a rectangular cross-section. The analytical and numerical results for springback prediction are validated with experiments, showing good agreement. The developed model is able to evaluate 3D springback of a profile with arbitrary cross-section efficiently for product design and development.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49402260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Radial forging of metallic materials requires both high temperatures and large plastic deformation. During this process, non-metallic inclusions (NMIs) can debond from the metallic matrix and break apart, resulting in a linear array of smaller inclusions, known as stringers. The evolution of NMIs into stringers can result in matrix load shedding, localized plasticity, and stress concentrations near the matrix-NMI interface. Due to these factors, stringers can be detrimental to the fatigue life of the final forged component, especially when present near a free surface. By performing a finite element model of the forging process with cohesive zones to simulate material debonding, we contribute to the understanding of processing induced deformation and damage sequences on the onset of stringer formation for both Type 1 and Type 2 alumina NMIs in a Ni-200 matrix. Through a parametric study, the interactions of forging temperature, strain rate, strain per pass, and interfacial decohesion on the NMI damage evolution metrics are studied, specifically NMI particle separation, rotation, and cavity formation. For Type 2 alumina NMIs, embedded in a Ni-200 matrix, the simulations indicate that at temperatures below 800 °C, particle separation dominates the NMI damage sequences, whereas at temperatures between 900 °C - 1000 °C, below an interfacial bond strength of 178 MPa, cavity formation is the dominate damage evolution mechanism, resulting in matrix load shedding and stress concentrations around the NMI.
{"title":"Simulating the evolution of non-metallic inclusions during the forging process","authors":"Brandon T. Mackey, T. Siegmund, M. Sangid","doi":"10.1115/1.4057026","DOIUrl":"https://doi.org/10.1115/1.4057026","url":null,"abstract":"\u0000 Radial forging of metallic materials requires both high temperatures and large plastic deformation. During this process, non-metallic inclusions (NMIs) can debond from the metallic matrix and break apart, resulting in a linear array of smaller inclusions, known as stringers. The evolution of NMIs into stringers can result in matrix load shedding, localized plasticity, and stress concentrations near the matrix-NMI interface. Due to these factors, stringers can be detrimental to the fatigue life of the final forged component, especially when present near a free surface. By performing a finite element model of the forging process with cohesive zones to simulate material debonding, we contribute to the understanding of processing induced deformation and damage sequences on the onset of stringer formation for both Type 1 and Type 2 alumina NMIs in a Ni-200 matrix. Through a parametric study, the interactions of forging temperature, strain rate, strain per pass, and interfacial decohesion on the NMI damage evolution metrics are studied, specifically NMI particle separation, rotation, and cavity formation. For Type 2 alumina NMIs, embedded in a Ni-200 matrix, the simulations indicate that at temperatures below 800 °C, particle separation dominates the NMI damage sequences, whereas at temperatures between 900 °C - 1000 °C, below an interfacial bond strength of 178 MPa, cavity formation is the dominate damage evolution mechanism, resulting in matrix load shedding and stress concentrations around the NMI.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45507545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automated optical inspection (AOI) is increasingly advocated for in-situ quality monitoring of additive manufacturing (AM) processes. The availability of layerwise imaging data improves the information visibility during fabrication processes and is thus conducive to performing online certification. However, layerwise images show complex patterns and often contain hidden information that cannot be revealed in a single scale. A new and alternative approach will be to analyze these intrinsic patterns with multi-scale lenses. This paper aims to design and develop an AOI system with contact image sensors for multi-resolution quality inspection of layerwise builds in additive manufacturing. We design the experiments to fabricate nine parts under a variety of factor levels (e.g., gas flow blockage, recoater damage, laser power changes). In each layer, the AOI system collects imaging data of both recoating powder beds before the laser fusion and surface finishes after the laser fusion. Then, we leverage the wavelet transformation to analyze ROI images in multiple scales and further extract salient features that are sensitive to process variations, instead of extraneous noises. The proposed framework of multi-resolution quality inspection is evaluated and validated using real-world AM imaging data. Experimental results demonstrated the effectiveness of the proposed AOI system with contact image sensors for online quality inspection of layerwise builds in AM processes.
{"title":"Multi-resolution Quality Inspection of Layerwise Builds for Metal 3D Printer-and-Scanner","authors":"Hui Yang, Joni Reijonen, A. Revuelta","doi":"10.1115/1.4057013","DOIUrl":"https://doi.org/10.1115/1.4057013","url":null,"abstract":"\u0000 Automated optical inspection (AOI) is increasingly advocated for in-situ quality monitoring of additive manufacturing (AM) processes. The availability of layerwise imaging data improves the information visibility during fabrication processes and is thus conducive to performing online certification. However, layerwise images show complex patterns and often contain hidden information that cannot be revealed in a single scale. A new and alternative approach will be to analyze these intrinsic patterns with multi-scale lenses. This paper aims to design and develop an AOI system with contact image sensors for multi-resolution quality inspection of layerwise builds in additive manufacturing. We design the experiments to fabricate nine parts under a variety of factor levels (e.g., gas flow blockage, recoater damage, laser power changes). In each layer, the AOI system collects imaging data of both recoating powder beds before the laser fusion and surface finishes after the laser fusion. Then, we leverage the wavelet transformation to analyze ROI images in multiple scales and further extract salient features that are sensitive to process variations, instead of extraneous noises. The proposed framework of multi-resolution quality inspection is evaluated and validated using real-world AM imaging data. Experimental results demonstrated the effectiveness of the proposed AOI system with contact image sensors for online quality inspection of layerwise builds in AM processes.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48129773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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