Optimum utilization of on-demand manufacturing and laser polishing in existence of supply disruption risk. (September 2022)

• Record Type: Journal Article
• Authors: Ulutan, Durul; İşler, Zülal; Erkan Kaya, Burak; Hekimoğlu, Mustafa
• Is Part Of: Manufacturing letters. Volume 33(2022)Supplement
• Page Start: 17
• Page End: 28
• Publication Date: 2022-09
• Subjects: Additive manufacturing -- Laser polishing -- Optimization -- Supply disruption -- Demand uncertainty; Manufacturing industries -- Periodicals; Production engineering -- Periodicals; Manufacturing industries; Periodicals; 670
• DOI: 10.1016/j.mfglet.2022.07.011
• Languages: English
• ISSNs: 2213-8463

Abstract

Abstract: 3D printing has moved from being a rapid prototyping tool to an additive manufacturing method within the last decade. Additive manufacturing can satisfy the need in dire situations where spare parts distribution is an issue but access to a 3D printer is much more likely and rapid than access to original parts. Managing inventories of spare parts can be tackled with more ease thanks to the reduced part types with additive manufacturing. While quality (in terms of reliability) of additively manufactured spare parts in terms of mechanical properties seem to be lower than original parts (particularly due to the inherent staircase appearance and the corresponding stress concentration zones that can lead to premature fatigue failure), use of post-processing subtractive techniques to correct such surface irregularities are found to improve reliability. While each process adds another layer of complexity to the cost minimization problem, demand uncertainty and risk of supply disruption represent the modern global problems faced recently. The problem tackled in this study is the joint optimization of the supply reliability considering the effect of laser polishing parameters and the demand uncertainty. In this problem, a condition of random breakdowns of identical products is considered. Also, the original supplier of machine components is subject to exogenous disruptions, such as strikes, raw material scarcity, or the COVID-19 pandemic. As a result, the optimum control policy with the right cost parameters was shown via numerical experiments originated from mathematical analyses. This optimality can be critical in managing the system in the best possible way, particularly during times of unforeseen circumstances such as pandemics.