Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 17, Issue 9, Pages 6023-6033Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.3040285
Keywords
Tools; Surface roughness; Rough surfaces; Task analysis; Surface treatment; Predictive models; Machining; Deep belief network (DBN); deep learning; multitask learning; surface quality prediction; tool wear condition
Categories
Funding
- National Natural Science Foundation of China [51775085, U1608251]
- Liaoning Revitalization Talents Program [XLYC1807081]
- Open Project of State Key Lab of Digital Manufacturing Equipment and Technology [DMETKF2019014]
- Changjiang Scholar Program of Chinese Ministry of Education [T2017030]
- Top and Leading Talents of Dalian [2018RD05]
- Youth Science and Technology Star of Dalian [2018RQ14]
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The article introduces a multitask learning method based on deep belief networks for predicting tool wear and part surface quality. Experimental results show that the proposed method can improve prediction accuracy and reduce computing time.
Deep learning has been gradually used in the field of machining condition monitoring. However, at present only single-task prediction can be performed, which results in increased experimental costs, wasted datasets, and repetitive work. In this article, a new multitask learning method based on a deep belief network (DBN) is proposed, which can be used to predict the tool wear condition and part surface quality. The single-task data transmission of the last few hidden layers of the DBN network is improved to multitask parallel data transmission so that the improved DBN can realize multitask learning. The loss function of the multitask learning model is defined as the weighted sum of all single-task loss functions. According to the loss of different tasks in the iteration process, the weight of corresponding tasks can be adjusted automatically. Furthermore, the multitask deep learning method can realize information sharing, suppress overfitting, improve prediction accuracy, and require less computing time. Combined with the abovementioned improvements, a multitask model for tool wear and part surface quality was developed. Experimental verification was performed on a KVC850M three-axis vertical machining center. The results show that the accuracy of the proposed multitask prediction model is 99% for the tool wear prediction and 92.86% for part surface quality prediction.
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