Automation of NC Programming with Artificial Intelligence
With the advent of artificial intelligence (AI) in business operations of various industries in recent decades, manufacturing firms are embracing intelligent, data-driven methods of making their processes more efficient. In particular, AI-driven automation of computer numerically controlled (CNC) pr...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/146859 https://orcid.org/0000-0003-0939-5769 |
| _version_ | 1826212194568634368 |
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| author | Lunny, Michael |
| author2 | Freund, Daniel |
| author_facet | Freund, Daniel Lunny, Michael |
| author_sort | Lunny, Michael |
| collection | MIT |
| description | With the advent of artificial intelligence (AI) in business operations of various industries in recent decades, manufacturing firms are embracing intelligent, data-driven methods of making their processes more efficient. In particular, AI-driven automation of computer numerically controlled (CNC) programming, the process by which cutting tool and operation parameters governing CNC machines are determined, has potential to yield dramatic benefits to machining companies. Within the context of Midwest-based machining firm Orizon, two approaches to programming automation were developed. Geometry Rule-based Automation of Programming (GRAP) is a rule based system with the ability to recognize hole and pocket features and automatically create an associated program, albeit suboptimal. Deep Learning for Automated Tool Selection (DLATS) is a machine learning algorithm with the ability to select the appropriate cutting tool for a hole drilling process with 32% accuracy, which is over 300 times better than random selection. Motivation, results, and implementation findings for both GRAP and DLATS are presented. |
| first_indexed | 2024-09-23T15:17:51Z |
| format | Thesis |
| id | mit-1721.1/146859 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:17:51Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1468592022-12-14T03:06:47Z Automation of NC Programming with Artificial Intelligence Lunny, Michael Freund, Daniel Lozano, Paulo Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Sloan School of Management With the advent of artificial intelligence (AI) in business operations of various industries in recent decades, manufacturing firms are embracing intelligent, data-driven methods of making their processes more efficient. In particular, AI-driven automation of computer numerically controlled (CNC) programming, the process by which cutting tool and operation parameters governing CNC machines are determined, has potential to yield dramatic benefits to machining companies. Within the context of Midwest-based machining firm Orizon, two approaches to programming automation were developed. Geometry Rule-based Automation of Programming (GRAP) is a rule based system with the ability to recognize hole and pocket features and automatically create an associated program, albeit suboptimal. Deep Learning for Automated Tool Selection (DLATS) is a machine learning algorithm with the ability to select the appropriate cutting tool for a hole drilling process with 32% accuracy, which is over 300 times better than random selection. Motivation, results, and implementation findings for both GRAP and DLATS are presented. M.B.A. S.M. 2022-12-13T16:57:27Z 2022-12-13T16:57:27Z 2022-05 2022-12-07T17:07:33.812Z Thesis https://hdl.handle.net/1721.1/146859 https://orcid.org/0000-0003-0939-5769 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Lunny, Michael Automation of NC Programming with Artificial Intelligence |
| title | Automation of NC Programming with Artificial Intelligence |
| title_full | Automation of NC Programming with Artificial Intelligence |
| title_fullStr | Automation of NC Programming with Artificial Intelligence |
| title_full_unstemmed | Automation of NC Programming with Artificial Intelligence |
| title_short | Automation of NC Programming with Artificial Intelligence |
| title_sort | automation of nc programming with artificial intelligence |
| url | https://hdl.handle.net/1721.1/146859 https://orcid.org/0000-0003-0939-5769 |
| work_keys_str_mv | AT lunnymichael automationofncprogrammingwithartificialintelligence |