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Prediction models for energy consumption and surface quality in stainless steel milling

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Abstract

Stainless steel is a kind of difficult-to-machine material, and the work hardening in milling easily leads to high energy consumption and poor surface quality. Thus, the influence of machined surface hardness on energy consumption and surface quality cannot be ignored. To solve this problem, the prediction models for machine tool specific energy consumption and surface roughness are developed with tool wear and machined surface hardness considered firstly. Then, the validity of the models is verified through AISI 304 stainless steel milling experiments. The results show that the prediction accuracy of the machine tool specific energy consumption model can reach 98.7%, and the roughness model can reach 96.8%. Later, according to the developed prediction models, the influence of milling parameters, surface hardness, and tool wear on the machine specific energy consumption and surface roughness is studied. Results show that in stainless steel milling, the most significant parameter for surface roughness is the machined surface hardness, while that for energy consumption is the feed per tooth. The machine specific energy consumption increases linearly with the increase of the tool wear and the machined surface hardness gradually. The proposed models are helpful to optimize the process parameters for energy-saving and high-quality machining of stainless steel.

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Funding

This work was supported by the key projects of Shandong Province Natural Science Foundation of China [ZR2020KE060].

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Authors and Affiliations

  1. Institute for Advanced Manufacturing, Shandong University of Technology, Zibo, 255000, China

    Shuo Yu, Guoyong Zhao, Chunxiao Li, Shuang Xu & Zhifu Zheng

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  1. Shuo Yu
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  2. Guoyong Zhao
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  3. Chunxiao Li
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  4. Shuang Xu
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  5. Zhifu Zheng
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Correspondence to Guoyong Zhao.

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Yu, S., Zhao, G., Li, C. et al. Prediction models for energy consumption and surface quality in stainless steel milling. Int J Adv Manuf Technol 117, 3777–3792 (2021). https://doi.org/10.1007/s00170-021-07971-x

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  • DOI: https://doi.org/10.1007/s00170-021-07971-x

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