An accurate prediction method of multiple deterioration forms of tool based on multitask learning with low rank tensor constraint

Tool deterioration is a common issue in Numerical Control (NC) machining, which directly affects part quality, production efficiency and manufacturing cost. Due to the complexity of machining, multiple deterioration forms of tool are involved during the tool deterioration process, which imposes a significant challenge for tool condition prediction because of the coupling effects among different deterioration forms. In order to address this issue, an accurate prediction method of multiple deterioration forms of tool based on multitask learning with low rank tensor constraint is proposed in this paper. A base model for prediction of each deterioration form is firstly constructed by using neural network, and then a 3-order tensor is constructed by stacking the weight matrices of the hidden layers of the base neural networks. The hidden relationship among the multiple related tasks is expected to be learned by means of a mathematical approach, i.e., constraining the 3-order tensor with low rank, which is realized by joint training the base prediction models. The experimental results show that the proposed method can reduce the prediction error by about 13 % for wear and 25 % for chipping respectively compared with the corresponding single task models.

Automated summary

This paper proposes an accurate prediction method of multiple deterioration forms of tool based on multitask learning with low rank tensor constraint, which aims to reduce the prediction error for wear and chipping through joint training the base prediction models.