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The International Journal of Advanced Manufacturing Technology

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05-01-2023 | ORIGINAL ARTICLE

The development of an ANN surface roughness prediction system of multiple materials in CNC turning

Authors: PoTsang B. Huang, Maria Magdalena Wahyuni Inderawati, Rohmat Rohmat, Ronald Sukwadi

Published in: The International Journal of Advanced Manufacturing Technology | Issue 3-4/2023

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Abstract

As one of the critical output parameters in the machining process, surface roughness quality must be constantly monitored, including predictions. Many scholars have researched sensing technology to monitor surface roughness. However, most of the research applied in a single-material model; this research intended to explore the intelligent combination prediction system between two materials, namely stainless steel (SUS304) and aluminum (Al6061). The machining process used computer numerical control (CNC) turning and applied sensing technology to collect signals as input factors in the prediction model. The prediction model used an artificial neural network (ANN) with the learning curve to find a good fitting of root mean square error (\(RMSE\)) in training and validation. This research obtains the best accuracy prediction in each material and multi-material model by developing a backpropagation neural network prediction model, in which the surface roughness (R_a) was output, and the signal factors were inputs. The precise prediction of the multi-material model was higher (96.74%) than the accurate predictions of the SUS304 model (93.75%) and Al6061 model (89.81%). An appropriate t-test was used to compare the error prediction results of every single-material and multi-material models. From the t-test result of the error model, there were significant differences between the single and multi-material. This result was highly recommended to be practically applied in the manufacturing industry with various materials. Further research was proposed for improvement.

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Title: The development of an ANN surface roughness prediction system of multiple materials in CNC turning
Authors: PoTsang B. Huang
Maria Magdalena Wahyuni Inderawati
Rohmat Rohmat
Ronald Sukwadi
Publication date: 05-01-2023
Publisher: Springer London
Published in: The International Journal of Advanced Manufacturing Technology / Issue 3-4/2023
Print ISSN: 0268-3768
Electronic ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-022-10709-y

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