Lai, Jing YuJing YuLaiPEI-CHUN LIN2023-07-142023-07-142022-01-019781665413084https://scholars.lib.ntu.edu.tw/handle/123456789/633622Surface roughness plays an important role in grinding; it can represent the grinding quality of machined parts. In previous research, analytical models and empirical models have been used to predict surface roughness. This research presented surface roughness prediction models based on linear regression and artificial neural networks of several types of model structures, then applied different features as model inputs, including force data directly get from the force sensor and those collected force data after statistical processing to reduce dimension. After conducting the prediction model, a self-developed grinding machine was used to collect the force data for model training and testing, and the mean absolute percentage error was used to evaluate the prediction performance. In the end, a neural network of three hidden layers was marked as the best model, which was useful for surface roughness prediction during grinding.contact force | Grinding | linear regression | neural network | surface roughness prediction[SDGs]SDG12conference paper10.1109/AIM52237.2022.98634022-s2.0-85137704309https://api.elsevier.com/content/abstract/scopus_id/85137704309