Machine learning in intelligent manufacturing system for optimization of production costs and overall effectiveness of equipment in fabrication models

The study proposes optimize the production costs with the implementation of an intelligent autonomous system applied to adaptive control and supervision to in computer-integrated manufacturing. For the validation, a horizontal band saw was used with 3 axes of displacement implementing 2 cameras with stereoscopic vision and finding an estimation of depth in the cut. With the dimensional deviations (x, y, z) of the cut, the shape and dimension of the cuts in the pipe, are defined to be manipulated and classified in correct and in-correct cuts by means of a separator coupling. For this purpose, algorithms were developed on two computer platforms: LabVIEW, which obtains the images, controls the automatic separator and the material feeder; Matlab, which processes dimensional deviations by recognizing patterns with the "Principal component analysis" (PCA) technique, in turn compares with an ideal pattern and optimizes the cutting parameters: Cut speed, cutting index, through a derivative Integral Proportional PID algorithm with the interaction of machine learning (ML) based on SVM theory. Autonomously corrects errors without human supervision, obtains the lengths and depths with the optimum cut-offs and result of adaptive supervision, increases production, product quality and reduces operating costs for each cutting cycle by com-plying with Overall Equipment Effectiveness parameters (OEE) and integrating into intelligent manufacturing systems.