Exploring barriers for the use of FEA-based variation simulation in industrial development practice. (11th October 2021)

• Record Type: Journal Article
• Authors: Nerenst, Tim Brix; Ebro, Martin; Nielsen, Morten; Eifler, Tobias; Nielsen, Kim Lau
• Is Part Of: Design science. Volume 7(2021)
• Publication Date: 2021-10-11
• Subjects: FEA variation simulation -- DOE -- Meta-model -- Monte-Carlo -- Industrial barriers; Design -- Research -- Periodicals; New products -- Management -- Periodicals; Design; Design -- Research; Electronic journals; Periodicals; 658.5752
• DOI: 10.1017/dsj.2021.21
• Languages: English
• ISSNs: 2053-4701

Abstract

Abstract: Over the last decades, finite element analysis (FEA) has become a standard tool in industrial product development, allowing for virtual analysis of designs, quick turnaround times and prompt implementation of results. Although academic research also provides numerous approaches for evaluating a product's robustness towards geometrical, material and load variations based on FEA analyses, this, however, stands in striking contrast to the limited use of these FEA-based variation simulations in industry. In order to bridge the existing gap between academic research and industrial application, this paper explores the barriers that limit the adoption of FEA-based variation simulation. The investigation is based on interviews with five lead engineers, followed by a case study that details the underlying technical challenges and allows for some initial suggestions for future solutions. The case study involves a sterile canister with seven geometrical variables. The design objective is to ensure sufficient sealing within the range of expected probabilistic variation. The combined study details the identified main barriers for a wider application, that is, the lack of robust CAD, practical guidelines to select an efficient design of experiments for design purposes, and the complexity of the automated processes. From a technical perspective, the case study results in estimations for main and interaction effects, an accurate metamodel and Monte Carlo simulations of 100, 000 samples providing the design engineer with more detailed and actionable insights on the performance of the product compared with the traditional nominal or best/worst case simulations.