Tool Selection Method Based on Transfer Learning for CNC Machines

Jingtao Zhou,Han Zhao,Mingwei Wang, Bingbo Shi

Mechanical Sciences（2018）SCI 4区

Northwestern Polytech Univ | China Acad Space Technol

Cited 4|Views15

Abstract

Owing to the changes in product requirements and development of new tool technology, traditional tool selection approach based on the human experience is leading to time-consuming and low efficiency. Under the cooperation of historical data resource accumulated by manufacturing enterprises, with human expert resource, a new tool selection mechanism can be established. In this paper, we apply transfer learning to tool selection issue. Starting from the foundation of migration, we showed a unified expression of expert experience and process case in a multi-source heterogeneous environment. Then, we propose a transfer learning algorithm (TLrAdaBoost) based on AdaBoost, which uses a small amount of target domain data (expert experience sample) and a large number of source domain low-quality data (process case sample), to build a high-quality classification model. Experimental results show the effectiveness of the proposed algorithm.

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