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Statistical tolerance allocation design considering form errors based on rigid assembly simulation and deep Q-network

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Abstract

Consideration of form errors involves real machining features in tolerance modeling but increases uncertainties in functional requirement estimation, when tackling the trade-off between the cost and precision performance. In this paper, a statistical tolerance allocation method is presented to solve this problem. First of all, a top-down stepwise designing procedure is designed for complex products, and a combination of Jacobian matrix and Skin Model Shapes is applied in modeling the mechanical joints. Then, rigid assembly simulations of point-based surfaces are further advanced to provide an accurate estimation of the assembly state, through considering physical constraints and termination conditions. A mini-batch gradient descent method and a backtracking strategy are also proposed to promote computational efficiency. Finally, a deep Q-network is implemented in optimal computation after characterizing the systematic state, action domain, and reward function. The general tolerance scheme is then achieved using the trained Q-network. The results of 6 experiments each with 200 samples show the proposed method is capable of assessing tolerance schemes with 35.2% and 47.2% lower manufacturing costs and 16.7% and 28.3% higher precision maintenance on average than conventional particle swarm optimization and Monte Carlo method respectively.

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Funding

This research was supported by the National Key R&D Program of China (Grant No. 2018YFB1700700), and the National Natural Science Foundation of China (Grant No. 51875516).

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  1. State Key Laboratory of Fluid Power, Mechatronic Systems, Zhejiang University, Hangzhou, China

    Ci He, Shuyou Zhang, Lemiao Qiu, Zili Wang & Yang Wang

  2. Ningbo Research Institute, Zhejiang University, Ningbo, China

    Xiaojian Liu

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  1. Ci He
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He, C., Zhang, S., Qiu, L. et al. Statistical tolerance allocation design considering form errors based on rigid assembly simulation and deep Q-network. Int J Adv Manuf Technol 111, 3029–3045 (2020). https://doi.org/10.1007/s00170-020-06283-w

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