Changchao Gu

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

Reliability assurance is a series of activities that ensure that the product reliability requirements be realised in a product life cycle. However, as reliability assurance activities are usually only implemented in design and usage, the integration of reliability assurance with quality control in production has not attracted the attention it deserved, thereby hindering production performance improvement to satisfy increasingly stringent customer requirements. To this end, this paper proposes a reliability-oriented quality control framework to integrate quality control and reliability assurance in production according to the principles of total quality management (TQM). Firstly, from the integrated quality and reliability assurance perspective, the RQR chain is proposed to represent the bidirectional relationships among the three basic management objects in a production process, namely, manufacturing system reliability (R), manufacturing process quality (Q), and the produced product reliability (R). Secondly, a reliability-oriented quality control framework for the production process based on the RQR chain is presented to provide the control clue for a manufacturing enterprise. Thirdly, the validity of the proposed approach is verified in a vehicle engine manufacturing enterprise of China. The proposed RQR chain as a decision-support model for TQM is eventually proven effective in promoting the integration of quality control and reliability assurance in production.

Reliability Modeling and Optimization Strategy for Manufacturing System Based on RQR Chain

Accurate and dynamic reliability modeling for the running manufacturing system is the prerequisite to implement preventive maintenance. However, existing studies could not output the reliability value in real time because their abandonment of the quality inspection data originated in the operation process of manufacturing system. Therefore, this paper presents an approach to model the manufacturing system reliability dynamically based on their operation data of process quality and output data of product reliability. Firstly, on the basis of importance explanation of the quality variations in manufacturing process as the linkage for the manufacturing system reliability and product inherent reliability, the RQR chain which could represent the relationships between them is put forward, and the product qualified probability is proposed to quantify the impacts of quality variation in manufacturing process on the reliability of manufacturing system further. Secondly, the impact of qualified probability on the product inherent reliability is expounded, and the modeling approach of manufacturing system reliability based on the qualified probability is presented. Thirdly, the preventive maintenance optimization strategy for manufacturing system driven by the loss of manufacturing quality variation is proposed. Finally, the validity of the proposed approach is verified by the reliability analysis and optimization example of engine cover manufacturing system.

Integrated predictive maintenance strategy for manufacturing systems by combining quality control and mission reliability analysis

Predictive maintenance (PdM) is an effective means to eliminate potential failures, ensure stable equipment operation and improve the mission reliability of manufacturing systems and the quality of products, which is the premise of intelligent manufacturing. Therefore, an integrated PdM strategy considering product quality level and mission reliability state is proposed regarding the intelligent manufacturing philosophy of ‘prediction and manufacturing’. First, the key process variables are identified and integrated into the evaluation of the equipment degradation state. Second, the quality deviation index is defined to describe the quality of the product quantitatively according to the co-effect of manufacturing system component reliability and product quality in the quality–reliability chain. Third, to achieve changeable production task demands, mission reliability is defined to characterise the equipment production states comprehensively. The optimal integrated PdM strategy, which combines quality control and mission reliability analysis, is obtained by minimising the total cost. Finally, a case study on decision-making with the integrated PdM strategy for a cylinder head manufacturing system is presented to validate the effectiveness of the proposed method. The final results shows that proposed method achieves approximately 26.02 and 20.54% cost improvement over periodic preventive maintenance and conventional condition-based maintenance respectively.

Reliability modeling of manufacturing systems based on the task network evolved by key quality characteristics

With the increasing requirements of product reliability and complexity of manufacturing system, the limitation of traditional reliability analysis of manufacturing systems neither considered product quality, nor the weight of key stations. Therefore, aiming at improving the product reliability, a reliability modeling of manufacturing systems based on the task network evolved by key quality characteristics is presented. Firstly, to identify the key task nodes of manufacturing system, the relationship between manufacturing system and product reliability was established in view of axiomatic design (AD), and the task network model evolved by product key quality characteristics was proposed based on axiomatic design theory successively. Then the reliability analysis model of manufacturing systems was put forward based on the capacity and failure rate of the key task nodes. Finally, a reliability analysis case of a piston ring machining system is given to verify the availability of the model.

Big data oriented root cause identification approach based on Axiomatic domain mapping and weighted association rule mining for product infant failure

Abstract Product infant failure formation mechanism is a maze that remains unclear to most manufacturers. Root cause analysis is an important and challenging task in exploring this mechanism in the era of quality and big data. Therefore, a novel big data oriented root cause identification approach based on weighted association rule mining (WARM) is proposed in this paper. First, the mechanism is expounded based on big data in the product lifecycle, and the requirements of root cause identification are determined simultaneously. Second, in view of domain mapping theory in Axiomatic design, the associated tree is proposed to provide a framework for the root cause search and identification. Then, the big data of root causes is defined based on the proposed associated tree. Third, a root cause mining technique using the WARM is presented, and the weight computation approach for the node on the associated tree based on the weight confidence is provided. Finally, the validity of the proposed method is verified by a case study on mining root causes for severe infant failure of an automatic washing machine. The final result shows that the proposed approach is conducive to heuristically identify the root causes of the complicated product infant failure from the big data of product lifecycle.

Health prognosis approach for manufacturing systems based on quality state task network

Previous studies on health prognosis are exceedingly dependence on the failure data and sensor data of a single component of manufacturing systems, and the holistic health prognosis techniques applicable to whole manufacturing systems still remain a challenge due to its increasingly physical and functional complexity. Therefore, a generalized health prognosis method is presented based on the deep fusion of quality-oriented big data of operational process of manufacturing systems. First, the generalized connotation of manufacturing system health is explained from the aspects of the physical composition and functional characteristics of manufacturing systems, and the quality state task network is proposed to organize quality-oriented big operational data, which improve the state transparency of the manufacturing system and lay the foundation of holistic health prognosis. Second, key characterization parameters in quality state task network are defined. Specifically, the performance state is analyzed based on multistate characteristics by considering the effects of stochastic degradation processes; the product quality state is quantified by using a process model that is established based on monitoring and inspection data; and the task execution state is quantitatively described by analyzing the evolution of task demand among machines. Third, an integrated model is built by integrating the three above-mentioned states as two key indicators, namely, qualified degree and mission reliability, for the comprehensive prognosis of the health of manufacturing systems. Finally, the effectiveness of the proposed approach is verified with a case study on the health prognosis of a cylinder head manufacturing system.

Cost-oriented predictive maintenance based on mission reliability state for cyber manufacturing systems:

With the advent of Industry 4.0, maintenance strategy faces new demands to avoid the hysteresis of the conventional passive maintenance mode and the non-feasibility of the periodic preventive maintenance model. In view of the inherent polymorphism of manufacturing systems and with the objective of maximizing benefits, a novel cost-oriented predictive maintenance based on mission reliability state for manufacturing systems is proposed. First, the cyber-physical system is adopted to organize and analyze big data in the operational process of manufacturing systems in terms of predictive analytics in cyber manufacturing environment. Second, a new connotation of mission reliability is defined based on the big operational data to comprehensively characterize the dynamic state of the equipment health states and the qualified degree of the production task. Third, the predictive maintenance mode based on mission reliability state is quantified by the comprehensive cost, and the relationship between mission reliability and cost is established. Thereafter, cost-oriented dynamic predictive maintenance strategy is proposed. Finally, a case study on the maintenance decision-making problem of a cylinder head manufacturing system is presented. The final result shows that the comprehensive cost can be further reduced by the proposed method relative to the traditional periodic preventive maintenance strategy.

Product quality oriented predictive maintenance strategy for manufacturing systems

Maintenance, production and quality are strongly linked to each other. Predictive maintenance is an effective way to eliminate the potential failures and ensure the stable operation of the manufacturing system, and further improve the reliability of the manufacturing system and the quality of manufactured products. Regarding the intelligent manufacturing philosophy “prediction and manufacturing#x201D;, a novel predictive maintenance strategy is proposed, in which the product quality control is integrated into predictive maintenance decision-making. First, the key process variables that characterize equipment wear are identified and integrated into the modeling of the equipment failure rate. Second, quality deviation that characterizes product quality level is defined based on co-effect between manufacturing system component reliability and product quality (i.e., Q-R chain). Third, the optimal maintenance strategy is obtained by optimizing the quality cost, maintenance cost, and interruption cost simultaneously. Finally, a case study on the predictive maintenance strategy decision-making for a cylinder head manufacturing system is presented to illustrate the effectiveness of the proposed method. The final result shows that the proposed method can achieve a superior economic performance relative to the conventional preventive maintenance mode.

Mission reliability modeling for multi-station manufacturing system based on Quality State Task Network

Multi-state-oriented mission reliability modeling is the premise of intelligent scheduling and predictive maintenance for the multi-station manufacturing system. Previous studies on reliability modeling for manufacturing system could only provide a static reliability model based on the basic reliability of the components of manufacturing systems, which cannot support reliability-oriented production scheduling and preventive maintenance effectively. To resolve this dilemma, a multi-state-oriented mission reliability modeling for multi-station manufacturing system is proposed. First, the mapping relationship between the produced product reliability and mission reliability of the manufacturing system is proposed as the basis for modeling, and the connotation of mission reliability is elaborated by analyzing the polymorphisms of the multi-station manufacturing system. Second, a graphical representation to improve the state transparency named as Quality State Task Network is proposed based on production data by integrating the variability of task-demands propagation as well as the multi-state in material quality and machine performance. Third, the mission reliability modeling method based on the Quality State Task Network is proposed. Finally, a case study of cylinder-head manufacturing system has been applied to validate the proposed model.

Comprehensive cost oriented predictive maintenance based on mission reliability for a manufacturing system

In this paper, a comprehensive cost oriented dynamic predictive maintenance policy based on mission reliability state is developed for a multi-state single-machine manufacturing system. In view of the inherent polymorphism of manufacturing systems (i.e., dynamic production scheduling and performance degradation), the connotation of mission reliability of equipment is defined and modeled based on the processing capacity distribution which integrates multiple fault data. Further, the relationship between mission reliability and performance of equipment is established by using the unavailability as the intermediary. The optimal predictive maintenance policy, the best mission reliability threshold for performing predictive maintenance action, is obtained by minimizing the comprehensive cost which includes processing capacity loss, corrective maintenance cost, predictive maintenance cost and indirect loss caused by failing to meet due dates over the planning period. This paper will also evaluate a manufacturing system of the cylinder head of an automotive engine as a case study to illustrate the effectiveness and advantages of the proposed method. The final result shows that a more significant economic benefit can be achieved by the proposed approach, which considers the mission reliability and comprehensive cost relative to the periodic preventive maintenance policy.