Bober, P.; Zgodavová, K.; Čička, M.; Mihaliková, M.; Brindza, J. Predictive Quality Analytics of Surface Roughness in Turning Operation Using Polynomial and Artificial Neural Network Models. Processes2024, 12, 206.
Bober, P.; Zgodavová, K.; Čička, M.; Mihaliková, M.; Brindza, J. Predictive Quality Analytics of Surface Roughness in Turning Operation Using Polynomial and Artificial Neural Network Models. Processes 2024, 12, 206.
Bober, P.; Zgodavová, K.; Čička, M.; Mihaliková, M.; Brindza, J. Predictive Quality Analytics of Surface Roughness in Turning Operation Using Polynomial and Artificial Neural Network Models. Processes2024, 12, 206.
Bober, P.; Zgodavová, K.; Čička, M.; Mihaliková, M.; Brindza, J. Predictive Quality Analytics of Surface Roughness in Turning Operation Using Polynomial and Artificial Neural Network Models. Processes 2024, 12, 206.
Abstract
The variability of the material properties of steel from different suppliers causes problems in achieving the required surface quality after turning. Therefore, the manufacturer needs to be able to estimate the resulting quality before starting production, especially if it is an expensive, small-batch production from stainless steel. Predictive models will make it possible to estimate the surface roughness from the mechanical properties of steel in the Mill Test Certificate (MTC) and thus support decision-making about the supplier selection or the acceptance of a material supply. Multivariate second-order polynomial model and feedforward back-propagation Artificial Neural Network (ANN) model were used to enhance the methodological robustness in formulating the decision if the predicted surface roughness is outside the required range, even before accepting the delivery. Both models can accurately predict surface roughness, while the ANN model is more accurate than the polynomial model; however, the predictive model is sensitive to the accuracy of the input data, and the model’s prediction is valid only under precisely defined conditions. The prediction model is used in a step-by-step decision-making procedure, which enables the trained staff to make a quick decision.
Keywords
fine turning, AISI 304; round bar; roughness; food processing equipment; machine learning; predictive quality; small batch; artificial neural network
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
