This paper presents a new systematic approach to the optimization of both design and manufacturing variables across a multi-step production process. The approach assumes a generic manufacturing process in which an initial near net shape (NNS) process is followed by a limited number of finishing operations. In this context the optimisation problem becomes a multi-variable problem in which the aim is to optimize by minimizing cost (or time) and improving technological performances (e.g. turning force). To enable such computation a methodology, named conditional design optimization (CoDeO) is proposed which allows the modelling and simultaneous optimization of process parameters and product design (geometric variables), using single or multi-criteria optimization strategies. After investigation of CoDeO’s requirements, evolutionary algorithms, in particular Genetic Algorithms, are identified as the most suitable for overall NNS manufacturing chain optimization The CoDeO methodology is tested using an industrial case study that details a process chain composed of casting and machining processes. For the specific case study presented the optimized process resulted in cost savings of 22% (corresponding to equivalent machining time savings) and a 10% component weight reduction.

本文提出了一种新的系统方法，用于跨多步生产过程优化设计和制造变量。该方法假设使用通用制造工艺，其中最初的近似最终形状（NNS）工艺之后是有限的精加工工序。在这种情况下，优化问题变成了多变量问题，其目的是通过最小化成本（或时间）和改善技术性能（例如转向力）来进行优化。为了实现这种计算，提出了一种名为条件设计优化（CoDeO）的方法，该方法允许使用单准则或多准则优化策略对过程参数和产品设计（几何变量）进行建模和同时优化。在调查了CoDeO的需求，进化算法之后，特别是遗传算法，被认为是最适合整体NNS制造链优化的方法。CoDeO方法论是通过工业案例研究进行测试的，该案例研究详细说明了由铸造和机加工过程组成的过程链。对于特定的案例研究，提出的优化工艺可节省22％的成本（相当于等效的加工时间的节省），并将部件重量减少10％。