Author(s)

Chaoran Tong ¹, Lei Que ¹, Siyuan Yang ²

Affiliation(s)

¹ School of Fine Arts and Design, Guangzhou University, Guangzhou, Guangdong, 510006, China
² School of Information Science and Technology, Xiamen University Tan Kah Kee College, Zhangzhou, Fujian, 363123, China

Corresponding Author

Lei Que

ABSTRACT

The process design of ceramic products has a great impact on the quality and performance of the final product. Traditional methods are not only inefficient, but also consume a lot of resources. In recent years, new technologies such as big data and machine learning have provided new ways to optimize the process of ceramic materials. The machine learning method used in this article is the GA-BP (Genetic Algorithm - Back Propagation) model, which uses big data technology to obtain processing parameters. This parameter includes ceramic material characteristics, process parameter settings, production process monitoring, finished product performance evaluation, etc., while ensuring the accuracy and completeness of the data. This article uses the GA-BP model to optimize the processing parameters. There is a good linear relationship between cutting speed and cutting temperature, but there is a certain gradient change in the local range. Therefore, this article establishes a one-dimensional model based on a one-dimensional linear function, and uses an exponential function to correct it, plus a constant to increase the degree of fit. Compared with the model output result of 134.98MPa, the actual measured surface residual stress is 135.98 MPa. By comparing the model output results with actual measurements, it is proved that the prediction error of this method is far less than 5%. This article helps optimize the processing parameters of engineering ceramic materials.

KEYWORDS

Ceramic Process Design, Big Data, Machine Learning, Surface Residual Stress, Processing Parameters

CITE THIS PAPER

Chaoran Tong, Lei Que, Siyuan Yang, Methods of Optimizing Ceramic Process Design Using Big Data and Machine Learning. Journal of Materials, Processing and Design (2024) Vol. 8: 6-14. DOI: http://dx.doi.org/10.23977/jmpd.2024.080102.

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