Automation of NC Programming with Artificial Intelligence
Name
lunny-mlunny-mba-mgt-2022-thesis.pdf
Description
Thesis PDF
Size
1.98 MB
Format
Adobe PDF
Checksum (MD5)
f4934db6fa6ae269f3f1cb7fda1dc0db
Author(s)
Lunny, Michael
Advisor(s)
Freund, Daniel
Lozano, Paulo
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Sloan School of Management
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