The Experts below are selected from a list of 2742 Experts worldwide ranked by ideXlab platform
Rob N. Candler - One of the best experts on this subject based on the ideXlab platform.
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improving precision of material extrusion 3d printing by in situ monitoring predicting 3d Geometric Deviation using conditional adversarial networks
Additive manufacturing, 2021Co-Authors: Ryan Mcguan, Robert Isaac, Pirouz Kavehpour, Rob N. CandlerAbstract:Abstract Material extrusion 3D printing has long been established for rapid prototyping and functional testing in many research and industry fields. However, its inconsistency and intrinsic defects (surface roughness and Geometric inaccuracies) hinder its application in several areas, most notably “certify-as-you-build” small-batch prototyping and large-batch production. In this study, we present an approach to reduce both inconsistency and the 3D Geometric inaccuracies of products fabricated by material extrusion. To achieve these improvements in print quality, we developed an in situ metrology system, which scans each layer at the time of printing, providing a 3D model of the as-printed part. We then trained machine learning algorithms with data from this scanning system and predicted 3D Geometric inaccuracies in new designs. Eight conditional adversarial network (CAN) machine learning models were trained on a limited number of scanned profile images of different layers, consisting of less than 50 actual images and 50 generated images, to predict the 3D Geometric Deviations of freeform shapes. The generated images were produced by randomly combining and cropping the actual images without any distortion. These CAN models produced predictions where at least 44.4%, 87.6%, 99.2% of data were within ± 0.05 mm, ± 0.10 mm, ± 0.15 mm of the actual measured value, respectively. A laser sensor was integrated into a material extrusion 3D printer to achieve in situ monitoring of dimensional inaccuracies during printing, which leaves the door open to implement a closed-loop feedback system to compensate Geometric errors during printing in the future and fabricate “certify-as-you-build” products.
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Improving precision of material extrusion 3D printing by in-situ monitoring & predicting 3D Geometric Deviation using conditional adversarial networks
Additive Manufacturing, 2021Co-Authors: Ryan Mcguan, Robert Isaac, Pirouz Kavehpour, Rob N. CandlerAbstract:Author(s): Li, Ling | Advisor(s): Candler, Robert N; Kavehpour, Pirouz H | Abstract: The field of additive manufacturing, especially 3D printing, has gained growing attention in the research and commercial sectors in recent years. Notwithstanding that the capabilities of 3D printing have moved on to enhanced resolution, higher deposition rate, and a wide variety of materials, the crucial challenge of verifying that the component manufactured is within the dimensional tolerance as designed continues to exist. Material extrusion 3D printing has long been established for rapid prototyping and functional testing in many research and industry fields. However, its inconsistency and intrinsic defects (surface roughness and Geometric inaccuracies) hinder its application in several areas, most notably “certify-as-you- build” small-batch prototyping and large-batch production.In this study, we present an approach to reduce both inconsistency and the 3D Geometric inaccuracies of products fabricated by material extrusion.1. This work developed and demonstrated an approach for layer-by-layer mapping of 3D printed parts, which can be used for validation of printed models and in situ adjustment of print parameters. This in situ metrology system scans each layer at the time of printing, providing a 3D model of the as-printed part. A high-speed optical scanning system was integrated with a Material Extrusion type 3D printer to achieve in situ monitoring of dimensional inaccuracies during printing, which leaves the door open to implement a closed-loop feedback system to compensate Geometric errors during printing in the future and fabricate “certify-as-you-build” products.2. This work trained machine learning algorithms with data from this scanning system and predicted 3D Geometric inaccuracies in new designs. Eight Conditional Adversarial Networks (CAN) machine learning models were trained on a limited number of scanned profile images of different layers, consisting of less than 50 actual images and 50 generated images, to predict the 3D Geometric Deviations of freeform shapes. The generated images were produced by randomly combining and cropping the actual images without any distortion. These CAN models produced predictions where at least 44.4%, 87.6%, 99.2% of data were within �0.05 mm, �0.10 mm, �0.15 mm of the actual measured value, respectively.3. This work developed an Iterative Forward approach to redesign the Computer-Aided- Design model by reverse engineering using the trained machine learning models, allowing for compensation of print imperfection at the design stage, in advance of the first printing. The compensation algorithms with eight different sets of different parameters were evaluated. It has been proven that the Iterative Forward approach improved the Geometric Deviation of the predicted profiles by making compensation to the CAD model.
Qiang Huang - One of the best experts on this subject based on the ideXlab platform.
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Predictive modeling of in-plane Geometric Deviation for 3D printed freeform products
Automation Science and Engineering (CASE), 2015 IEEE International Conference on, 2015Co-Authors: He Luan, Qiang HuangAbstract:Although 3D printing or additive manufacturing (AM) holds great promise as a direct manufacturing technology, the dimensional Geometric accuracy remains a critical issue, especially for freeform products with complex Geometric shapes. Efforts have long been attempted to improve the accuracy of AM built freeform products. But there is a lack of generic methodology transparent to specific AM processes. This study fills the gap by establishing a general model predicting the in-plane (x-y plane) Geometric Deviations of AM built freeform products. Built upon our previous predictive model and optimal compensation study for cylinder and polyhedron shapes, this work makes a breakthrough by directly predicting arbitrary freeform shape Deviations from CAD design. Experimental investigation using stereolithography process successfully validates the proposed methodology, which indicates the prospect of optimal compensation for freeform products built by a varieties of AM processes.
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CASE - Predictive modeling of in-plane Geometric Deviation for 3D printed freeform products
2015 IEEE International Conference on Automation Science and Engineering (CASE), 2015Co-Authors: He Luan, Qiang HuangAbstract:Although 3D printing or additive manufacturing (AM) holds great promise as a direct manufacturing technology, the dimensional Geometric accuracy remains a critical issue, especially for freeform products with complex Geometric shapes. Efforts have long been attempted to improve the accuracy of AM built freeform products. But there is a lack of generic methodology transparent to specific AM processes. This study fills the gap by establishing a general model predicting the in-plane (x-y plane) Geometric Deviations of AM built freeform products. Built upon our previous predictive model and optimal compensation study for cylinder and polyhedron shapes, this work makes a breakthrough by directly predicting arbitrary freeform shape Deviations from CAD design. Experimental investigation using stereolithography process successfully validates the proposed methodology, which indicates the prospect of optimal compensation for freeform products built by a varieties of AM processes.
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state space modeling of dimensional variation propagation in multistage machining process using differential motion vectors
International Conference on Robotics and Automation, 2003Co-Authors: Shiyu Zhou, Qiang HuangAbstract:In this paper, a state space model is developed to describe the dimensional variation propagation of multistage machining processes. A complicated machining system usually contains multiple stages. When the workpiece passes through multiple stages, machining errors at each stage will be accumulated and transformed onto the workpiece. Differential motion vector, a concept from the robotics field, is used in this model as the state vector to represent the Geometric Deviation of the workpiece. The Deviation accumulation and transformation are quantitatively described by the state transition in the state space model. A systematic procedure that builds the model is presented and an experimental validation is also conducted. The validation result is satisfactory. This model has great potential to be applied to fault diagnosis and process design evaluation for complicated machining processes.
Ryan Mcguan - One of the best experts on this subject based on the ideXlab platform.
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improving precision of material extrusion 3d printing by in situ monitoring predicting 3d Geometric Deviation using conditional adversarial networks
Additive manufacturing, 2021Co-Authors: Ryan Mcguan, Robert Isaac, Pirouz Kavehpour, Rob N. CandlerAbstract:Abstract Material extrusion 3D printing has long been established for rapid prototyping and functional testing in many research and industry fields. However, its inconsistency and intrinsic defects (surface roughness and Geometric inaccuracies) hinder its application in several areas, most notably “certify-as-you-build” small-batch prototyping and large-batch production. In this study, we present an approach to reduce both inconsistency and the 3D Geometric inaccuracies of products fabricated by material extrusion. To achieve these improvements in print quality, we developed an in situ metrology system, which scans each layer at the time of printing, providing a 3D model of the as-printed part. We then trained machine learning algorithms with data from this scanning system and predicted 3D Geometric inaccuracies in new designs. Eight conditional adversarial network (CAN) machine learning models were trained on a limited number of scanned profile images of different layers, consisting of less than 50 actual images and 50 generated images, to predict the 3D Geometric Deviations of freeform shapes. The generated images were produced by randomly combining and cropping the actual images without any distortion. These CAN models produced predictions where at least 44.4%, 87.6%, 99.2% of data were within ± 0.05 mm, ± 0.10 mm, ± 0.15 mm of the actual measured value, respectively. A laser sensor was integrated into a material extrusion 3D printer to achieve in situ monitoring of dimensional inaccuracies during printing, which leaves the door open to implement a closed-loop feedback system to compensate Geometric errors during printing in the future and fabricate “certify-as-you-build” products.
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Improving precision of material extrusion 3D printing by in-situ monitoring & predicting 3D Geometric Deviation using conditional adversarial networks
Additive Manufacturing, 2021Co-Authors: Ryan Mcguan, Robert Isaac, Pirouz Kavehpour, Rob N. CandlerAbstract:Author(s): Li, Ling | Advisor(s): Candler, Robert N; Kavehpour, Pirouz H | Abstract: The field of additive manufacturing, especially 3D printing, has gained growing attention in the research and commercial sectors in recent years. Notwithstanding that the capabilities of 3D printing have moved on to enhanced resolution, higher deposition rate, and a wide variety of materials, the crucial challenge of verifying that the component manufactured is within the dimensional tolerance as designed continues to exist. Material extrusion 3D printing has long been established for rapid prototyping and functional testing in many research and industry fields. However, its inconsistency and intrinsic defects (surface roughness and Geometric inaccuracies) hinder its application in several areas, most notably “certify-as-you- build” small-batch prototyping and large-batch production.In this study, we present an approach to reduce both inconsistency and the 3D Geometric inaccuracies of products fabricated by material extrusion.1. This work developed and demonstrated an approach for layer-by-layer mapping of 3D printed parts, which can be used for validation of printed models and in situ adjustment of print parameters. This in situ metrology system scans each layer at the time of printing, providing a 3D model of the as-printed part. A high-speed optical scanning system was integrated with a Material Extrusion type 3D printer to achieve in situ monitoring of dimensional inaccuracies during printing, which leaves the door open to implement a closed-loop feedback system to compensate Geometric errors during printing in the future and fabricate “certify-as-you-build” products.2. This work trained machine learning algorithms with data from this scanning system and predicted 3D Geometric inaccuracies in new designs. Eight Conditional Adversarial Networks (CAN) machine learning models were trained on a limited number of scanned profile images of different layers, consisting of less than 50 actual images and 50 generated images, to predict the 3D Geometric Deviations of freeform shapes. The generated images were produced by randomly combining and cropping the actual images without any distortion. These CAN models produced predictions where at least 44.4%, 87.6%, 99.2% of data were within �0.05 mm, �0.10 mm, �0.15 mm of the actual measured value, respectively.3. This work developed an Iterative Forward approach to redesign the Computer-Aided- Design model by reverse engineering using the trained machine learning models, allowing for compensation of print imperfection at the design stage, in advance of the first printing. The compensation algorithms with eight different sets of different parameters were evaluated. It has been proven that the Iterative Forward approach improved the Geometric Deviation of the predicted profiles by making compensation to the CAD model.
Pirouz Kavehpour - One of the best experts on this subject based on the ideXlab platform.
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improving precision of material extrusion 3d printing by in situ monitoring predicting 3d Geometric Deviation using conditional adversarial networks
Additive manufacturing, 2021Co-Authors: Ryan Mcguan, Robert Isaac, Pirouz Kavehpour, Rob N. CandlerAbstract:Abstract Material extrusion 3D printing has long been established for rapid prototyping and functional testing in many research and industry fields. However, its inconsistency and intrinsic defects (surface roughness and Geometric inaccuracies) hinder its application in several areas, most notably “certify-as-you-build” small-batch prototyping and large-batch production. In this study, we present an approach to reduce both inconsistency and the 3D Geometric inaccuracies of products fabricated by material extrusion. To achieve these improvements in print quality, we developed an in situ metrology system, which scans each layer at the time of printing, providing a 3D model of the as-printed part. We then trained machine learning algorithms with data from this scanning system and predicted 3D Geometric inaccuracies in new designs. Eight conditional adversarial network (CAN) machine learning models were trained on a limited number of scanned profile images of different layers, consisting of less than 50 actual images and 50 generated images, to predict the 3D Geometric Deviations of freeform shapes. The generated images were produced by randomly combining and cropping the actual images without any distortion. These CAN models produced predictions where at least 44.4%, 87.6%, 99.2% of data were within ± 0.05 mm, ± 0.10 mm, ± 0.15 mm of the actual measured value, respectively. A laser sensor was integrated into a material extrusion 3D printer to achieve in situ monitoring of dimensional inaccuracies during printing, which leaves the door open to implement a closed-loop feedback system to compensate Geometric errors during printing in the future and fabricate “certify-as-you-build” products.
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Improving precision of material extrusion 3D printing by in-situ monitoring & predicting 3D Geometric Deviation using conditional adversarial networks
Additive Manufacturing, 2021Co-Authors: Ryan Mcguan, Robert Isaac, Pirouz Kavehpour, Rob N. CandlerAbstract:Author(s): Li, Ling | Advisor(s): Candler, Robert N; Kavehpour, Pirouz H | Abstract: The field of additive manufacturing, especially 3D printing, has gained growing attention in the research and commercial sectors in recent years. Notwithstanding that the capabilities of 3D printing have moved on to enhanced resolution, higher deposition rate, and a wide variety of materials, the crucial challenge of verifying that the component manufactured is within the dimensional tolerance as designed continues to exist. Material extrusion 3D printing has long been established for rapid prototyping and functional testing in many research and industry fields. However, its inconsistency and intrinsic defects (surface roughness and Geometric inaccuracies) hinder its application in several areas, most notably “certify-as-you- build” small-batch prototyping and large-batch production.In this study, we present an approach to reduce both inconsistency and the 3D Geometric inaccuracies of products fabricated by material extrusion.1. This work developed and demonstrated an approach for layer-by-layer mapping of 3D printed parts, which can be used for validation of printed models and in situ adjustment of print parameters. This in situ metrology system scans each layer at the time of printing, providing a 3D model of the as-printed part. A high-speed optical scanning system was integrated with a Material Extrusion type 3D printer to achieve in situ monitoring of dimensional inaccuracies during printing, which leaves the door open to implement a closed-loop feedback system to compensate Geometric errors during printing in the future and fabricate “certify-as-you-build” products.2. This work trained machine learning algorithms with data from this scanning system and predicted 3D Geometric inaccuracies in new designs. Eight Conditional Adversarial Networks (CAN) machine learning models were trained on a limited number of scanned profile images of different layers, consisting of less than 50 actual images and 50 generated images, to predict the 3D Geometric Deviations of freeform shapes. The generated images were produced by randomly combining and cropping the actual images without any distortion. These CAN models produced predictions where at least 44.4%, 87.6%, 99.2% of data were within �0.05 mm, �0.10 mm, �0.15 mm of the actual measured value, respectively.3. This work developed an Iterative Forward approach to redesign the Computer-Aided- Design model by reverse engineering using the trained machine learning models, allowing for compensation of print imperfection at the design stage, in advance of the first printing. The compensation algorithms with eight different sets of different parameters were evaluated. It has been proven that the Iterative Forward approach improved the Geometric Deviation of the predicted profiles by making compensation to the CAD model.
Robert Isaac - One of the best experts on this subject based on the ideXlab platform.
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improving precision of material extrusion 3d printing by in situ monitoring predicting 3d Geometric Deviation using conditional adversarial networks
Additive manufacturing, 2021Co-Authors: Ryan Mcguan, Robert Isaac, Pirouz Kavehpour, Rob N. CandlerAbstract:Abstract Material extrusion 3D printing has long been established for rapid prototyping and functional testing in many research and industry fields. However, its inconsistency and intrinsic defects (surface roughness and Geometric inaccuracies) hinder its application in several areas, most notably “certify-as-you-build” small-batch prototyping and large-batch production. In this study, we present an approach to reduce both inconsistency and the 3D Geometric inaccuracies of products fabricated by material extrusion. To achieve these improvements in print quality, we developed an in situ metrology system, which scans each layer at the time of printing, providing a 3D model of the as-printed part. We then trained machine learning algorithms with data from this scanning system and predicted 3D Geometric inaccuracies in new designs. Eight conditional adversarial network (CAN) machine learning models were trained on a limited number of scanned profile images of different layers, consisting of less than 50 actual images and 50 generated images, to predict the 3D Geometric Deviations of freeform shapes. The generated images were produced by randomly combining and cropping the actual images without any distortion. These CAN models produced predictions where at least 44.4%, 87.6%, 99.2% of data were within ± 0.05 mm, ± 0.10 mm, ± 0.15 mm of the actual measured value, respectively. A laser sensor was integrated into a material extrusion 3D printer to achieve in situ monitoring of dimensional inaccuracies during printing, which leaves the door open to implement a closed-loop feedback system to compensate Geometric errors during printing in the future and fabricate “certify-as-you-build” products.
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Improving precision of material extrusion 3D printing by in-situ monitoring & predicting 3D Geometric Deviation using conditional adversarial networks
Additive Manufacturing, 2021Co-Authors: Ryan Mcguan, Robert Isaac, Pirouz Kavehpour, Rob N. CandlerAbstract:Author(s): Li, Ling | Advisor(s): Candler, Robert N; Kavehpour, Pirouz H | Abstract: The field of additive manufacturing, especially 3D printing, has gained growing attention in the research and commercial sectors in recent years. Notwithstanding that the capabilities of 3D printing have moved on to enhanced resolution, higher deposition rate, and a wide variety of materials, the crucial challenge of verifying that the component manufactured is within the dimensional tolerance as designed continues to exist. Material extrusion 3D printing has long been established for rapid prototyping and functional testing in many research and industry fields. However, its inconsistency and intrinsic defects (surface roughness and Geometric inaccuracies) hinder its application in several areas, most notably “certify-as-you- build” small-batch prototyping and large-batch production.In this study, we present an approach to reduce both inconsistency and the 3D Geometric inaccuracies of products fabricated by material extrusion.1. This work developed and demonstrated an approach for layer-by-layer mapping of 3D printed parts, which can be used for validation of printed models and in situ adjustment of print parameters. This in situ metrology system scans each layer at the time of printing, providing a 3D model of the as-printed part. A high-speed optical scanning system was integrated with a Material Extrusion type 3D printer to achieve in situ monitoring of dimensional inaccuracies during printing, which leaves the door open to implement a closed-loop feedback system to compensate Geometric errors during printing in the future and fabricate “certify-as-you-build” products.2. This work trained machine learning algorithms with data from this scanning system and predicted 3D Geometric inaccuracies in new designs. Eight Conditional Adversarial Networks (CAN) machine learning models were trained on a limited number of scanned profile images of different layers, consisting of less than 50 actual images and 50 generated images, to predict the 3D Geometric Deviations of freeform shapes. The generated images were produced by randomly combining and cropping the actual images without any distortion. These CAN models produced predictions where at least 44.4%, 87.6%, 99.2% of data were within �0.05 mm, �0.10 mm, �0.15 mm of the actual measured value, respectively.3. This work developed an Iterative Forward approach to redesign the Computer-Aided- Design model by reverse engineering using the trained machine learning models, allowing for compensation of print imperfection at the design stage, in advance of the first printing. The compensation algorithms with eight different sets of different parameters were evaluated. It has been proven that the Iterative Forward approach improved the Geometric Deviation of the predicted profiles by making compensation to the CAD model.