Towards a Framework for Self-Evolving Products in Additive Manufacturing

In critical industrial applications, enhancing existing custom-designed products throughout their life cycles is crucial for improving user value while managing the costs of continuous design iterations. Additive manufacturing has demonstrated significant improvements in product performance through optimized designs, but implementing these improvements in scale requires excessive design efforts. In this paper, we address this gap by presenting a digital twin (DT)-based product design framework for additively manufactured parts that makes the design of the physical parts independently to adapt into their industrial application environments. The methodology is based on efficiently utilizing data in the intersection of application-specific DT model and the CAD (Computer-Aided Design). It is proposed that with the smart utilization of Evolutionary Algorithms (EA), most of the manual labor involved in drafting performance-improving revisions of design of part geometry could be eliminated. The proposition is evaluated from the business model point of view highlighting the potential for novel, mutually beneficial supplier-customer relationships in advanced industrial equipment solutions.