Reliability-oriented quality control approach for production process based on RQR chain

Proceedings of the Institution of Mechanical Engineers, Part B:

Liu

et al. 2020

Self Cite

Assembling variations caused by assembling system reliability degradation are the root causes of poor-assembled product reliability, and the reliability loss is the barometer of assembling quality risks. However, few studies have integrated reliability loss with assembling quality risk analysis. Therefore, a modeling approach of assembling quality risk regarding product reliability loss is expounded in this work. First, the assembling quality risk is divided into systematic, exterior, and interior risks, and a formation mechanism of assembling quality risk is presented. Second, the fusion framework of big operational data in assembling is described, an assembling reliability–quality–reliability chain is established, and the parameters of the chain constitute assembling system reliability ( R), assembling process quality ( Q), and assembled product reliability ( R). Third, on the basis of the reliability–quality–reliability chain, the risk priority number is adopted to quantify the assembling quality risk, which is extended by quantifying the undetectable rate of the assembling system, the occurrence possibility of process variations, and the product reliability loss. Finally, an assembling quality risk of a circuit maintenance cover of a car is conducted to validate the effectiveness and advancement of the presented approach. The result shows that the proposed method can systematically quantify the assembling quality risk.

Section: Introductionmentioning

confidence: 99%

Reliability loss–oriented product assembling quality risk modeling approach based on reliability–quality–reliability chain

Proceedings of the Institution of Mechanical Engineers, Part B:

Liu

et al. 2020

Self Cite

“…With the operation of processing, these factors are coupled to affect the quality of a produced product [ 2 ]. As the output of the manufacturing process, the quality of Work In Process (WIP) is directly related to the operational status of the manufacturing system [ 3 ]. The quality variations of WIP are transferred between processes, leading to the degradation of the reliability of the finished product.…”

Section: Introductionmentioning

confidence: 99%

Modeling Product Manufacturing Reliability with Quality Variations Centered on the Multilayered Coupling Operational Characteristics of Intelligent Manufacturing Systems

Han

et al. 2020

Sensors

Self Cite

For intelligent manufacturing systems, there are many deviations in operational characteristics, and the coupling effect of harmful operational characteristics leads to the variations in quality of the work-in-process (WIP) and the degradation of the reliability of the finished product, which is reflected as a loss of product manufacturing reliability. However, few studies on the modeling of product manufacturing reliability and mechanism analysis consider the operating mechanism and the coupling of characteristics. Thus, a novel modeling approach based on quality variations centered on the coupling of operational characteristics is proposed to analyze the formation mechanism of product manufacturing reliability. First, the PQR chain containing the co-effects among the manufacturing system performance (P), the manufacturing process quality (Q), and the product manufacturing reliability (R) is elaborated. The connotation of product manufacturing reliability is defined, multilayered operational characteristics are determined, and operational data are collected by smart sensors. Second, on the basis of the coupling effect in the PQR chain, a multilayered product quality variation model is proposed by mining operational characteristic data obtained from sensors. Third, an integrated product manufacturing reliability model is presented on the basis of the variation propagation mechanism of the multilayered product quality variation model. Finally, a camshaft manufacturing reliability analysis is conducted to verify the validity of the proposed method. The method proposed in this paper proved to be effective for evaluating and predicting the product reliability in the smart manufacturing process.

“…As individualized and small batch production modes become popular, manufacturing systems are gradually and increasingly required to be flexible, and the manufacturing process continuously incorporates the characteristics of multi-threading and multi-task flow. The RQR (i.e., Reliability-Quality-Reliability) chain proposed by He et al [1] reveals that the reliability of a machine is the root cause of manufacturing quality deviations and product quality defects. The healthy operation of a machine is the premise of process quality control and the output of highreliability products.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling Method of Mission Reliability of a Multi-State Manufacturing System With Multiple Production Lines

Wang

et al. 2020

IEEE Access

Self Cite

Mission reliability could systematically characterize the running state of a manufacturing system and the rationality of production task setting in advance. However, few studies can realize the mission reliability modeling, which integrates production scheduling based on fully considering the complexity, dynamics, and multi-state characteristics of a manufacturing system. Thus, a dynamic method for modeling the mission reliability of a multi-state manufacturing system with multiple production lines is proposed in this paper. First, the multi-state characteristic of manufacturing systems operation is analyzed. On this basis, considering the composition of manufacturing systems, the connotation and modeling mechanism of the mission reliability of manufacturing systems are proposed. Second, combining the discretization of machine performance states and dynamic continuity of the performance degradation process, the dynamic modeling method of multi-state machine performance and buffer usage are respectively discussed. The transfer evolution of task demands in the production line is analyzed, and the decomposition method of task scheduling and load mapping in multiple production lines is provided. Third, considering the characteristics of multiple production lines and the buffer of a manufacturing system, taking production tasks as the core and integrating production scheduling, a modeling method for the mission reliability of manufacturing systems is proposed. Finally, the effectiveness of the proposed approach is verified using a case study on the modeling of the mission reliability of a manufacturing system of aerospace parts.