Abstract
With the increase in the number and advances of additive manufacturing technologies (AM), coupled with the focus of certifying AM parts with new guidelines, companies have been looking to shift production of spare parts to AM. Therefore, a framework for assisting companies to assess parts for additive manufacturing has become more relevant in recent years. Even though there are 2 main frameworks that aimed to do so, both do not examine relationships between company goals and classification factors. There have also been attempts to introduce machine learning and artificial intelligence software to aid companies in doing so. However, this would often require proprietary software that may not be easily accessible. Therefore, this research is aimed at studying the current climate surrounding Additive Manufacturing, conducting a literature review to determine what are the current concerns and perceptions of using AM for spare part production. In the end, a 2-stage framework is proposed to assist companies, especially those new to AM, in determining the suitability of obsolete parts for AM. The first stage aims to identify potential obsolete parts that are feasible for AM, using potential inventory savings as the main ranking matrix, as well as incorporating criticality and complexity of the part. The second stage introduces the companies to a list of common concerns of AM, thereafter, using a user-dependent weighted matrix to align the companies’ goals. Two case studies are then presented to showcase the usage of the framework and its effectiveness, the first with regards to obsolete parts and the second focusing on non-obsolete parts. Both studies are compared, possible limitations and future works are then highlighted and discussed in the end.