Abstract
Assembly quality is the barometer of assembly system health, and a healthy assembly system is an important physical guarantee for producing reliable products. Therefore, for ensuring the high reliability of products, the operational data of the assembly system should be analyzed to manage health states. Therefore, based on the operational data of the assembly system collected by intelligent sensors, from the perspective of quality control based on risk thinking, a risk-oriented health assessment method and predictive maintenance strategy for managing assembly system health are proposed. First, considering the loss of product reliability, the concept of assembly system health risk is proposed, and the risk formation mechanism is expounded. Second, the process variation data of key reliability characteristics (KRCs) collected by different sensors are used to measure and assess the health risk of the running assembly system to evaluate the health state. Third, the assembly system health risk is used as the maintenance threshold, the predictive maintenance decision model is established, and the optimal maintenance strategy is determined through stepwise optimization. Finally, the case study verifies the effectiveness and superiority of the proposed method. Results show that the proposed method saves 37.40% in costs compared with the traditional method.
Highlights
The assembly process is a classic and critical step in manufacturing, whose quality considerably affects product reliability [1,2]
As this study focuses on the related influence dimensional variationsreliability of the assembly process on product manufacturing reliability, the concept of KRCs is proposed as follows: The product characteristics which are closely related to the product manufacturing reliability are called the key reliability characteristics (KRCs), that is, KRCs are sensitive to product manufacturing reliability
The smart sensors located on the assembly system variations of KRCs on product manufacturing reliability is quantified to assesskey the indicator assemblytypes system collect a large amount of process data, which can be filtered by the identified to health risk
Summary
The assembly process is a classic and critical step in manufacturing, whose quality considerably affects product reliability [1,2]. Park and Shrivastava [15] emphasized that the traditional assembly ideas that only pay attention to the assembly quality state and disregard the impact of assembly quality decline on the stage of use of the finished product are immature, and these ideas neither propose the concept of risk management quality analysis nor use risk-based thinking to explain the quality of product assembly On this basis, He et al [16] targeted the general manufacturing process by considering the cumulative effect of process variation; they focused on the qualification rate of the key manufacturing end to characterize the batch quality risk. Shi and Zeng [26] established a predictive maintenance model by considering the real-time residual life and maintenance cost of the system for dynamic opportunistic multi-station systems He et al [27] proposed an integrated predictive maintenance strategy that combines product quality control and mission reliability constraints regarding the intelligent manufacturing philosophy of ‘prediction and manufacturing’.
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