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Prediction model of machining surface roughness for five-axis machine tool based on machine-tool structure performance

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Abstract

In this study, structural performance analysis and test verification of a machine tool were performed. This research is based on a five-axis machine tool for modeling and experimental verification. The mechanical-structure performance of the machine-tool cutting process directly affects the processing results. The processing performance of a five-axis machine tool was analyzed to identify processing weaknesses as the basis for subsequent structural improvements. Data were then integrated through the abductory induction mechanism (AIM) polynomial neural network to predict intelligent processing quality, and an in-depth investigation was conducted by importing processing parameters to predict the surface quality of the finished product. The finite-element analysis method was used to analyze the static and dynamic characteristics of the whole machine and to test the structural modal frequency and vibration shape. For modal testing, the experiment used various equipment, including impact hammers, accelerometers, and signal extractors. Subsequent planning of modal frequency band processing experiments was conducted to verify the influence of natural frequencies on the processing level. Finally, according to the machine processing characteristics, a processing experiment was planned. The measurement record was used as the training data of the AIM polynomial neural network to establish the processing quality prediction model. After analysis and an actual machine test comparison, the two-axis static rigidity values of the machine were X: 1.63 kg/µm and Y: 1.93 kg/µm. The modal vibration shape maximum error of the machine was within 6.2%. The processing quality prediction model established by the AIM polynomial neural network could input processing parameters to achieve the surface roughness prediction value, and the actual relative error of the Ra value was within 0.1 µm. Based on the results of cutting experiments, the influence of the dynamic characteristics of the machine on the processing quality was obtained, especially in the modal vibration environment, which had an adverse effect on the surface roughness. Hence, the surface roughness of the workpiece processed by the machine could be predicted.

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Abbreviations

CPM:

Complexity penalty factor

FSE:

Fitting squared error

KP:

Complexity penalty

PSE:

Predicted squared error

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Funding

The authors are greatly indebted to the Ministry of Science and Technology of the R.O.C. for supporting this research (Grant No. MOST 107–2218-E-150–005-MY3 and MOST 109–2622-E-150–014).

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Authors and Affiliations

  1. Department of Mechanical and Computer-Aided Engineering, National Formosa University, Yunlin, Taiwan

    Tzu-Chi Chan, Hsin-Hsien Lin & Sabbella Veera Venkata Satyanarayana Reddy

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  1. Tzu-Chi Chan
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  2. Hsin-Hsien Lin
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  3. Sabbella Veera Venkata Satyanarayana Reddy
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Contributions

Formal analysis, writing, and funding acquisition, T-CC; data curation, and software H-HL and SVVSR. All authors read and agreed to the published version of the manuscript.

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Correspondence to Tzu-Chi Chan.

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The authors declare no competing interests.

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Chan, TC., Lin, HH. & Reddy, S.V.V.S. Prediction model of machining surface roughness for five-axis machine tool based on machine-tool structure performance. Int J Adv Manuf Technol 120, 237–249 (2022). https://doi.org/10.1007/s00170-021-08634-7

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  • DOI: https://doi.org/10.1007/s00170-021-08634-7

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