No abstract

The stability of process is certainly one of the most important aspects to fulfill the task. Now, it is still a challenge to put forward a model to deal with the stability of manufacturing process mathematically. Aiming at solving the problem, this paper discusses the issues by the notion of manufacturing process reliability and its sensitivity. Based on state space model, the process performance function which is used to describe the relations between the key product characters (KPC) of the machined part and the key control characters (KCC) of multistage process in machining system has been built. Furthermore, the measure index to the process reliability of multistage machining system is proposed, which facilitates the forming of the ways to model the process reliability mathematically. To determine the weakest stage during machining process, the sensitivity of process reliability to KCC of the process has been put forward and the corresponding ways how to calculate it. Because of the multistage process is highly non-linear process, chaos optimization algorithm and mutative scale chaos optimization algorithm are used to calculate this kind of robust reliability index．Finally, A simple 2-D case study has been used to validate the proposed model. It shows that the process reliability can be calculated effectively.

Fuel injector is the key part of a high-pressure common rail fuel injection system. Its manufacturing precision and assembly quality affect system's property and performance. According to the characteristics and demands of assembly of the fuel injector, an intelligent optimization algorithm is proposed to resolve the problem of assembly sequence planning. Based on geometric modeling, assembly dimension chain of the injector control chamber is established, and the relationship between assembly tolerance and volume change of control chamber is analyzed. The optimization model of the assembly is established. The impact of assembly tolerance on injector's performance is simulated according to the optimization algorithm. The simulation result shows that quantity of injection fuel changes correspondingly with the change of assembly tolerance, while injection rate and pressure do not change significantly, and the response rate of needle considerably slow. Similarly, the leakage rate of fuel in control chamber is calculated, indicating that the assembly tolerance has obvious impact on fuel leakage and its rate. The study illuminates that injector's assembly tolerance has prominent effect on injection.

The stability of process is certainly one of the most important aspects to fulfill the task. Now, it is still a challenge to put forward a model to deal with the stability of manufacturing process mathematically. Aiming at solving the problem, this paper discusses the issues by the notion of manufacturing process reliability and its sensitivity. Based on state space model, the process performance function which is used to describe the relations between the key product characters (KPC) of the machined part and the key control characters (KCC) of multistage process in machining system has been built. Furthermore, the measure index to the process reliability of multistage machining system is proposed, which facilitates the forming of the ways to model the process reliability mathematically. To determine the weakest stage during machining process, the sensitivity of process reliability to KCC of the process has been put forward and the corresponding ways how to calculate it. Because of the multistage process is highly non-linear process, chaos optimization algorithm and mutative scale chaos optimization algorithm are used to calculate this kind of robust reliability index．Finally, A simple 2-D case study has been used to validate the proposed model. It shows that the process reliability can be calculated effectively.

The quality performance of a multistage manufacturing systems (MMS) is jointly affected by incoming part quality, system condition unreliability due to batch-to-batch uncertainty, making it challenging to evaluate the quality performance of MMS. Previous research considered the incoming part quality and system conditions separately in systematic level. This paper aims to fill the gap by considering the joint effects of these two aspects to evaluate quality performance of a MMS from historical production data driven work. A system quality model was derived to predict the probability of producing good parts at each stage and entire MMS when the incoming good part quality rate and station conditions were given. To overcome the inconvenience of the quality model for its nonlinear transfer function, the concept of quality efficiency was developed to depict the joint effectiveness of incoming part quality and system conditions mathematically at each stage. Based on the quality model, on the paper also discusses how to maintain high good product quality rate. The results of this study suggested a possible approach of evaluating the impacts of system conditions on product quality. The results of the model will lead to guidelines of quality management in multistage manufacturing systems.