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Keywords: machine learning
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Proceedings Papers

Proc. ASME. MSEC2023, Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering, V001T01A033, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-106095
... system based on machine learning (ML) algorithms is developed. The system is found effective in predicting eDIW printing geometry under varied process parameter settings. Image processing approaches to collect experiment data are developed. Accuracies of different machine learning algorithms...
Proceedings Papers

Proc. ASME. MSEC2023, Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering, V001T01A012, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-104387
...) and the other based on classic machine learning (ML), and compares them with statistical image thresholding approaches (i.e., K-means, Bernsen’s, Otsu’s Thresholding). A discussion about the method-level difference among these methods is provided, revealing the merits of the proposed DL and ML methods in fast...
Proceedings Papers

Proc. ASME. MSEC2023, Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering, V001T01A022, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-105015
..., a common classification metric in literature, an F1 measure of 0.96 is obtained for an object printed using compensation compared to 0.76 when compensation is not used, demonstrating our proposed method’s effectiveness. machine learning 3D CNN additive manufacturing error compensation...
Proceedings Papers

Proc. ASME. MSEC2023, Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering, V001T01A013, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-104463
... the desired microstructure and eliminate the trial-and-error process optimization methodologies. In this paper, a surrogate machine learning model is implemented to design the additive manufacturing (AM) process parameters, including scan speed, laser power, and scanning strategies, to achieve desired...
Proceedings Papers

Proc. ASME. MSEC2023, Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering, V001T01A004, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-101325
... the shape change due to a microstructure phase transformation. Given the high number of fabrication factors, machine learning (ML) approaches provide a promising approach to the design of SMA to control the TTs. The main obstacle to using ML methods is the need for an established correlation between...
Proceedings Papers

Proc. ASME. MSEC2023, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T08A007, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-106557
... are not only computationally expensive, but also must be integrated into an iterative control framework to optimize the digital mask used to selectively control laser power, making it critical to enable quick temperature predictions. Therefore, this paper proposes a regression-based Machine Learning model...
Proceedings Papers

Proc. ASME. MSEC2023, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T06A013, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-104529
... to the boundaries (< 23.6 %). convolutional neural network (CNN) linear time invariant (LTI) system finite element analysis (FEA) real-time temperature prediction moving heat source heat map machine learning Proceedings of the ASME 2023 18th International Manufacturing Science and Engineering...
Proceedings Papers

Proc. ASME. MSEC2023, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T06A031, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-105178
... physics-based modeling has long been the path to such predictions, machine learning models are of increasing interest recently. But the data-hungry nature of machine learning entails prohibitive experimental cost, is hindered by large oversimplifications of computationally efficiency analytical models...
Proceedings Papers

Proc. ASME. MSEC2023, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T06A030, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-105175
...Proceedings of the ASME 2023 18th International Manufacturing Science and Engineering Conference MSEC2023 June 12-16, 2023, New Brunswick, NJ, USA MSEC2023-105175 Physics-Informed Machine Learning Model for In-process Estimation of Cutter Runout Parameters in End Milling Shubham Vaishnav Department...
Proceedings Papers

Proc. ASME. MSEC2023, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A004, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-104452
... on the search for high-quality solutions in manufacturing systems, which is envisioned to drive future research on RL-supported manufacturing systems. Keywords: machine learning; reinforcement learning; discrete manufacturing systems; production control. NOMENCLATURE AMI Advanced Metering Infrastructures A3C...
Proceedings Papers

Proc. ASME. MSEC2023, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T09A003, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-101213
... an effective inspection method for reducing cost and improving yield rate, this paper introduces a data-driven, integrated, and intelligent framework that enables an inspection of particle defects on a semiconductor wafer surface and the location of the particles by machine learning-based algorithms...
Proceedings Papers

Proc. ASME. IAM2022, 2022 International Additive Manufacturing Conference, V001T02A012, October 19–20, 2022
Publisher: American Society of Mechanical Engineers
Paper No: IAM2022-96740
..., the fused decision of the 324 test samples achieved 100% detection accuracy. powder bed fusion additive manufacturing decision fusion data fusion machine learning classification Proceedings of the 2022 International Additive Manufacturing Conference IAM2022 October 19-20, 2022, Lisbon...
Proceedings Papers

Proc. ASME. MSEC2022, Volume 1: Additive Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation; Nano/Micro/Meso Manufacturing, V001T01A018, June 27–July 1, 2022
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2022-85382
... show that the SVM model was able to predict the standoff distance classification with an accuracy of 97 percent and a speed of 0.122 s per image, making it a viable solution for real-time control of standoff distance. additive manufacturing machine learning support vector machines melt pool...
Proceedings Papers

Proc. ASME. MSEC2022, Volume 2: Manufacturing Processes; Manufacturing Systems, V002T06A017, June 27–July 1, 2022
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2022-85459
... and maintenance cost. The recent development in machine learning and artificial intelligence enables data-driven Predictive Maintenance (PdM) by means of perceiving the dynamics of manufacturing systems and abstracting them into learnable features to provide a better interpretation of machine failures...
Proceedings Papers

Proc. ASME. MSEC2022, Volume 2: Manufacturing Processes; Manufacturing Systems, V002T05A048, June 27–July 1, 2022
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2022-85587
... the goodness of fit. Machine learning models can effectively deal with such inherent uncertainties and serve as an alternative to the statistical curve-fitting. The present work proposes to improve the empirical relationship between instantaneous uncut chip thickness and cutting coefficients by employing...
Proceedings Papers

Proc. ASME. MSEC2022, Volume 1: Additive Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation; Nano/Micro/Meso Manufacturing, V001T01A020, June 27–July 1, 2022
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2022-85404
... performance especially for high granularity CTWD predictions when compared to current sensing approaches. The implications for these results in rapid implementation for high-level process monitoring of process condition are brie y discussed. Keywords Wire Arc, Additive Manufacturing, Machine Learning...
Proceedings Papers

Proc. ASME. MSEC2022, Volume 1: Additive Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation; Nano/Micro/Meso Manufacturing, V001T01A012, June 27–July 1, 2022
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2022-85325
...-informed machine learning (ML) framework for predicting and compensating for the geometric deformation of WAAM fabricated products based on a few sample parts. The proposed ML algorithm efficiently separates geometric shape deviation into deformation and surface roughness. Then, the predicted shape...
Proceedings Papers

Proc. ASME. MSEC2022, Volume 1: Additive Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation; Nano/Micro/Meso Manufacturing, V001T01A030, June 27–July 1, 2022
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2022-85691
... by the curing image pattern in each layer, and vice versa. Machine learning algorithms were leveraged to infer the printing status from the measured temperatures of these thermistors. Specifically, we proposed a simple and robust Failure Index to detect if the printing was active or terminated. Gaussian process...
Proceedings Papers

Proc. ASME. MSEC2021, Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation, V001T04A008, June 21–25, 2021
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2021-63966
... with safety standards. The purpose of this study Number of the probability distribution is to use machine learning tools to analyze several parameters crucial for achieving a robust repair service parameters system, including the number of repairs, the time of the next repair ticket or product failure...
Proceedings Papers

Proc. ASME. MSEC2021, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A007, June 21–25, 2021
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2021-63670
... obstacles. maximum obstacle radius to consider minimum clearance between obstacles for robot Keywords: human-robot collaboration, adaptive control, passage augmented intelligence, machine learning, smart factory, function that saturates such that industry 4.0 maximum duration to consider for calculating...
Topics: Risk, Robots