Abstract
The accuracy of computer numerical control machine tools can be improved by identifying error sources affecting the overall position error and orientation errors. Because of their inevitable nature, the position errors cannot be entirely eliminated from the machinery, but they can be identified, measured and compensated during the manufacturing process of the components by developing and using a mathematical model. In this present work, different mathematical models have been developed for the errors measured by laser interferometer at different nominal positions of X, Y and Z axes both in forward and reverse direction movement as per VDI 3441 Germany standard. Using Akaike information criterion, the best model is selected for each axis and later the best model’s coefficients have been optimized by considering both minimizing sum square errors and maximizing R2 values using teaching–learning-based optimization algorithm. Technique for Order Preference by Similarity to the Ideal Solution method has been adopted to convert the dual objectives into a single objective. An improvement of 1%–71% in R2 values was reported to prove the effectiveness of the proposed optimization algorithm, Teaching–Learning-Based Optimization algorithm, with the same sum square error values.
Highlights
Globalization causes international competition among the manufacturers in selling their quality product with competitive prices
The coefficients of the best model have been optimized by considering dual objectives of minimizing sum square error (SSE) and maximizing R2 values using teaching–learning-based optimization (TLBO) algorithm where Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method has been adopted to convert the dual objectives into single objective
The positional errors have been measured in an equal interval by varying the nominal position of X, Y and Z axes using laser interferometer in computer numerical control (CNC) Vertical Machining Center (VMC) both in forward and reverse direction
Summary
Globalization causes international competition among the manufacturers in selling their quality product with competitive prices. Uncontrolled errors present in machines are the main reason in producing components with under or oversized condition which is not accepted by the customer because of their unacceptable dimension. Sometimes, this error causes secondary operation or rework to make it a customer-desired component. Quality and productivity of VMC have been greatly improved by accuracy assessment of machine tools and condition monitoring.[5] Neural network system was developed to perform positioning error compensation in the lathe by laser interferometer.[6] Laser autocollimator had been used to measure angular error, and the neural network has been used to predict error and compensation.[7] By incorporating four steps of measurement of error components by using a laser interferometer system, artificial neural network (ANN) has been used for online error measurement using sensors.
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