Diagnosis of machining outcomes based on machine learning with Logical Analysis of Data

Force is considered to be one of the indicators that best describe the machining process.
Measured force can be used to evaluate the quality and geometric profile of the machined part.
In this paper, a combinatorial optimization approach is used to characterize the effect of force on the quality of a machined part made of Carbon Fiber Reinforced Polymers (CFRP) material.
The approach is called Logical Analysis of Data (LAD) and is based on machine learning and pattern recognition.
LAD is used in order to map the machining conditions, in terms of force and torque that lead to conforming products and those which lead to nonconforming products.
In this paper, the LAD technique is applied to the drilling of CFRP plates, and the results, based on data obtained experimentally, are reported.
A discussion of the potential use of LAD in manufacturing concludes the paper.
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