Decision support methods for closed-loop conceptual design

The dissertation deals with closed-loop product lifecycle management (PLM) approaches for the product conceptual design improvement using gathered data during the usage period of a product. In closed-loop PLM, the data generated in each lifecycle phase can be shared in different lifecycle phases and used for various objectives such as design, maintenance, recycle, and so on. Among them, we focus on the information flow from the middle of life (MOL) phase to the beginning of life (BOL) phase. From the MOL phase, the product usage data is gathered and transferred to the BOL phase. The transferred data should be transformed into suitable information (e.g. contextualized usage data such as failure rate, mean time between failure, and etc.) and knowledge (e.g. design modification suggestions for product improvement) for the conceptual design activity of the BOL phase. This dissertation provides a procedure and related methods for the transformation of data into information and knowledge. For this, we develop a product usage data transformation method consisting of three steps: 1) evaluation of a used component/part status, 2) correlation between a used component/part status and design parameters, and 3) estimation of product improvement cost investment for possible modifications of required design parameters. In the first step of our method, we define an index value which indicates the degree of performance degradation of used components/parts functions considering the historical change of the performance degradation over time. In this index, the historical change of the performance degradation is considered as three characteristics: criticality, abnormality, and severity. In the second step of our method, we correlate the evaluated performance degradation of component/part function and the field data which consists of the working environment and operational setting during product operation. To this end, we follow several sub-steps. In these sub-steps, we use various methods such as a function structure model, a degradation scenario, a clustering technique, and a relation matrix. To analyze product functions and define degradation scenarios, we build and use a function structure model. The clustering technique is used to classify the field data by product status. The relation matrix is used to calculate the importance rate of the product functions and the product design parameters. The importance rate helps engineers to recognize which functions are critical in performance degradation and which design parameters are important from the viewpoint of the performance degradation. In the last step of our method, the found design parameters and their importance rates are used to estimate the cost investment for product design modification during the conceptual design phase. To do this, an extended house of quality (HOQ) is developed and a mixed integer non-linear programming (MINLP) formulation is proposed. To solve the problem of the cost investment estimation in a reasonable time, we provide three heuristic algorithms to solve the MINLP problem in a reasonable time. During the explanation of our method, we use two cases (turbocharger and locomotive) to show the validity of our method. From the application of our method into the case studies, we recognize critical design parameters (compressor wheel shape and the number of compressor wheel blades for turbocharger, and the diameter of brake cylinder for the locomotive) to be modified for the product improvement. An estimation of the required cost investment to modify these critical design parameters is calculated by the solution of the MINLP.