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) 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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Aeronautics and Astronautics

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Lunny, Michael

Advisors dc:contributor.advisor

• Freund, Daniel
• Lozano, Paulo

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/146859

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/146859