Predictive analytics for smart manufacturing : use and impact from a systems thinking perspective
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, System Design and Management Program, Engineering and Management Program, 2016.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2017
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| Online Access: | http://hdl.handle.net/1721.1/106252 |
| _version_ | 1811079228354985984 |
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| author | Jeong, Hyunsoo |
| author2 | Roy E. Welsch and Natasha Markuzon. |
| author_facet | Roy E. Welsch and Natasha Markuzon. Jeong, Hyunsoo |
| author_sort | Jeong, Hyunsoo |
| collection | MIT |
| description | Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, System Design and Management Program, Engineering and Management Program, 2016. |
| first_indexed | 2024-09-23T11:11:53Z |
| format | Thesis |
| id | mit-1721.1/106252 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:11:53Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1062522022-01-12T20:00:40Z Predictive analytics for smart manufacturing : use and impact from a systems thinking perspective Jeong, Hyunsoo Roy E. Welsch and Natasha Markuzon. Massachusetts Institute of Technology. Engineering Systems Division. Massachusetts Institute of Technology. Engineering and Management Program System Design and Management Program. Massachusetts Institute of Technology. Engineering Systems Division. System Design and Management Program Engineering and Management Program. System Design and Management Program. Engineering Systems Division. Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, System Design and Management Program, Engineering and Management Program, 2016. Cataloged from PDF version of thesis. Includes bibliographical references (pages 115-122). The manufacturing industry has, recently, been facing tremendous challenges, including cost efficiency, system safety, and process automation, and manufacturing companies are required to adopt new technologies to keep themselves sustainable in the fast-changing world of technology. This research focuses, in particular, on how to prevent cutting tool failures and catastrophic accidents in Computerized Numerically Controlled (CNC) machining processes by using a predictive model based on the cutting sound data. With advances in machine learning algorithms and predictive analytics techniques, it becomes possible to create a noise-robust predictive model from an unstructured dataset of sound data. It is an obviously desirable decision to make use of every technology as required and benefit from it. The predictive model introduced in this research uses cutting sound data rather than acoustic emission or force/torque sensor data, which have been widely used for machine failure detection but have shown some limitations. The model is an important stepping stone for realizing an unmanned and fully automated manufacturing system, the so-called "smart factory," and it would be a meaningful movement for the government side as well, taking into account government's responsibility to keep people safe in the workplace. In this research, several experiments were carried out to collect sound data in the CNC machining center in Korea, and particular features were extracted from the analog waveform signals, using the unstructured data to make the predictive model using various advanced data analytics techniques and cutting-edge machine learning algorithms. Then, several analysis methods with systems thinking were used to explore potential impacts of the predictive model on the manufacturing system because the systems thinking approach is the most effective way to analyze a wide range of potential impacts from a holistic perspective. Specifically, the impact analysis was successfully conducted by using a "Causal Analysis based on STAMP (CAST)," which is a system safety analysis method. Also used was "system dynamics modeling," which is generally employed to identify dynamic behaviors in a complex system. Finally, a "complete value template" was constructed to portray how the new system delivers value to its stakeholders from a system architecture perspective. by Hyunsoo Jeong. S.M. in Engineering and Management 2017-01-06T16:13:55Z 2017-01-06T16:13:55Z 2016 2016 Thesis http://hdl.handle.net/1721.1/106252 961940194 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 122 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Engineering and Management Program. System Design and Management Program. Engineering Systems Division. Jeong, Hyunsoo Predictive analytics for smart manufacturing : use and impact from a systems thinking perspective |
| title | Predictive analytics for smart manufacturing : use and impact from a systems thinking perspective |
| title_full | Predictive analytics for smart manufacturing : use and impact from a systems thinking perspective |
| title_fullStr | Predictive analytics for smart manufacturing : use and impact from a systems thinking perspective |
| title_full_unstemmed | Predictive analytics for smart manufacturing : use and impact from a systems thinking perspective |
| title_short | Predictive analytics for smart manufacturing : use and impact from a systems thinking perspective |
| title_sort | predictive analytics for smart manufacturing use and impact from a systems thinking perspective |
| topic | Engineering and Management Program. System Design and Management Program. Engineering Systems Division. |
| url | http://hdl.handle.net/1721.1/106252 |
| work_keys_str_mv | AT jeonghyunsoo predictiveanalyticsforsmartmanufacturinguseandimpactfromasystemsthinkingperspective |