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Keywords: machine learning
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Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. August 2024, 146(8): 081006.
Paper No: MANU-23-1550
Published Online: May 7, 2024
... spindle vibration machine learning recurrent neural network gated recurrent unit unbalance bearing fault detection machine tool dynamics modeling and simulation sensing monitoring and diagnostics Natural Sciences and Engineering Research Council of Canada 10.13039/501100000038 IRCPJ...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. August 2024, 146(8): 081002.
Paper No: MANU-23-1431
Published Online: April 24, 2024
... and the underlying system parameters, including the frequency response function (FRF) and cutting force model. These machine learning approaches begin with a probabilistic guess about the shape of the stability map and iteratively update it based on the results of cutting tests. This method was first implemented...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. April 2024, 146(4): 040902.
Paper No: MANU-23-1463
Published Online: February 28, 2024
... is empowered by an embedded inductive inference-based machine learning module which learns a welded object's structural properties in the form of geometrical properties. In particular, the system tries to recognize line segments, using a spatial (three-dimensional) visual sensor in order to autonomously...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. February 2024, 146(2): 020901.
Paper No: MANU-23-1413
Published Online: November 1, 2023
... the efficiency of assessing product repairability, this study introduces two artificial intelligence (AI) based approaches. The first approach is a supervised learning framework that utilizes object detection on product teardown images to measure repairability. Transfer learning is employed with machine learning...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. January 2024, 146(1): 011009.
Paper No: MANU-23-1290
Published Online: October 19, 2023
... components: (1) the ISBJSSP simulator, which manages the dynamic graph generation and machine availability complexity, which are existent in the ISBJSSP setting but absent in the JSSP one, and (2) GNN-RL, a machine learning technique employed to learn the scheduling policy by observing the dynamic graphs...
Includes: Supplementary data
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. November 2023, 145(11): 111006.
Paper No: MANU-23-1381
Published Online: September 11, 2023
...] Samie Tootooni , M. , Dsouza , A. , Donovan , R. , Rao , P. K. , Kong , Z. , and Borgesen , P. , 2017 , “ Classifying the Dimensional Variation in Additive Manufactured Parts From Laser-Scanned Three-Dimensional Point Cloud Data Using Machine Learning Approaches ,” ASME J. Manuf...
Journal Articles
Journal Articles
Journal Articles
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. January 2023, 145(1): 011006.
Paper No: MANU-22-1333
Published Online: October 13, 2022
...Alexandra Schueller; Christopher Saldaña Tool condition monitoring (TCM) has become a research area of interest due to its potential to significantly reduce manufacturing costs while increasing process visibility and efficiency. Machine learning (ML) is one analysis technique which has demonstrated...
Journal Articles
Publisher: ASME
Article Type: Technical Briefs
J. Manuf. Sci. Eng. September 2022, 144(9): 094504.
Paper No: MANU-22-1097
Published Online: July 29, 2022
... training data. For further research on activity recognition in manual manufacturing, we propose the explicit consideration and evaluation of disturbance variables and diversity in data collection for the training of machine learning models. 1 Corresponding author. Email: matthias.doerr@kit.edu...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. September 2022, 144(9): 091011.
Paper No: MANU-21-1388
Published Online: July 29, 2022
... the printability of a nickel-rich NiTi powder, where printability refers to the ability to fabricate macro-defect-free parts. Specifically, single track experiments were first conducted to select key processing parameter settings for cubic specimen fabrication. Machine learning classification techniques were...
Journal Articles
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. June 2022, 144(6): 061008.
Paper No: MANU-21-1317
Published Online: December 3, 2021
...Joseph Cohen; Jun Ni Machine learning and other data-driven methods have developed at a prolific rate for industrial applications due to the advent of industrial big data. However, industrial datasets may not be especially well-suited to supervised learning approaches that require extensive domain...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. March 2022, 144(3): 031005.
Paper No: MANU-21-1308
Published Online: August 16, 2021
.... The purpose of this study is to use machine learning tools to analyze several parameters crucial for achieving a robust repair service system, including the number of repairs, the time of the next repair ticket or product failure, and the time to repair. A large data set of over 530,000 repairs...
Journal Articles
Journal Articles
Publisher: ASME
Article Type: Technical Briefs
J. Manuf. Sci. Eng. January 2022, 144(1): 014501.
Paper No: MANU-19-1700
Published Online: July 6, 2021
... from multiple identical production lines are collected and analyzed to learn the “best” feasible action on critical machines, which offers a new way to optimize the management of product lines. Machine learning and system model are used to find the relationships between the performance index...
Journal Articles
Publisher: ASME
Article Type: Technical Briefs
J. Manuf. Sci. Eng. May 2021, 143(5): 054501.
Paper No: MANU-20-1137
Published Online: November 11, 2020
... that the average tool diameter reduces by 32 μm, 67 μm and 108 μm, and the average resultant cutting force were 2.45 N, 4.17 N, and 4.93 N in stage 1, 2, and 3, respectively. To avoid catastrophic breakage of the tool, the tool life stages are predicted from the force data using machine learning models. Among...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. January 2021, 143(1): 011008.
Paper No: MANU-20-1188
Published Online: October 5, 2020
... detection long short-term memory neural networks machine learning soft-computing techniques milling operation ball screw drive advanced materials and processing computer-integrated manufacturing machining processes sensing monitoring and diagnostics Flexibility of cutting tools is a major...
Journal Articles
Publisher: ASME
Article Type: Review Articles
J. Manuf. Sci. Eng. November 2020, 142(11): 110816.
Paper No: MANU-20-1083
Published Online: September 29, 2020
... and FSW; and (9) efforts in advanced sensing, data fusion/sensor fusion, and process control using machine learning/deep learning, model predictive control (MPC), and adaptive control. Email: yuming.zhang@uky.edu Email: yyang@ewi.org Email: zhang.3978@osu.edu Email: sjoona@kaist.ac.kr...