In-situ identification of material batches using machine learning for machining operations
Problem
Batches of the same specified material differ in machinability, and unidentified differences reduce product quality and raise manufacturing costs.
Why it matters
Ignoring batch variation degrades product quality and increases manufacturing costs.
What the AI did
A supervised machine learning model analyzes machine control signals, especially torque data, to classify the material batch in-situ during machining.
Results
Close to 90% prediction accuracy identifying five batches of the same material across various cutting conditions
Impact
Validated by cutting experiments; demonstrates feasibility of in-situ batch identification for process optimization.
Record
| Venue | Journal of Intelligent Manufacturing |
|---|---|
| Published | 2020 |
| Work type | article |
| Authors | Benjamin Lutz, Dominik Kißkalt, Andreas Mayr, Daniel Regulin, Matteo Pantano, Jörg E. Franke |
| Citations | 16 (OpenAlex, at import time) |
| References | 22 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1007/s10845-020-01718-3 |
| Open access | hybrid |
| Keywords | machine learning, batch identification, machining, torque data, classification |
Importance rating
real but narrower advance
Abstract states unidentified batch differences reduce product quality and raise manufacturing costs; a real but narrower manufacturing advance, validated only in cutting experiments.
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