Concurrent optimization of process parameters and product design variables for near net shape manufacturing processes

Daniele Marini, Jonathan R. Corney

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
34 Downloads (Pure)


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.
Original languageEnglish
Number of pages21
JournalJournal of Intelligent Manufacturing
Early online date12 Jun 2020
Publication statusE-pub ahead of print - 12 Jun 2020


  • manufacturing optimization
  • process optimization
  • design optimization
  • near net shape
  • genetic algorithm
  • machining parameters optimization


Dive into the research topics of 'Concurrent optimization of process parameters and product design variables for near net shape manufacturing processes'. Together they form a unique fingerprint.

Cite this