Design of Experiments Procedure for Evaluating the Formability of Sheet Metals Components in Forming Processes

Sarema, Blessed; Matope, Stephen; Sterzing, Andreas

doi:10.52202/066390-0004

Cited by 1 publication

(1 citation statement)

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“…The aim is to choose the optimal value for each input parameter to achieve the desired response despite the potential variation in manufacturing conditions, loads or part tolerances. In general, DOE is a well-researched field, and its fundamental ideas are widely accepted among researchers (Sarema, et al, 2022;Joseph et al, 2019;Oudjene and Ben-Ayed, 2008;Lehman, Santner, and Notz, 2004;Jin, Chen, and Sudjianto, 2003). When paired with commercially available Finite Element Analysis (FEA) software, a virtual DOE can be undertaken that can achieve a higher level of verification and eliminate experimental effort and cost (Kim, 2010;Al-Momani and Rawabdeh, 2008).…”

Section: Introductionmentioning

confidence: 99%

Exploring the Potential for a Fea-Based Design of Experiments to Develop Design Tools for Bulk-Metal Joining Processes

Hatherell,

Marmier,

Dennis

et al. 2023

Proc. Des. Soc.

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Over the last 20 years, finite element analysis (FEA) has become a standard analysis tool for metal joining processes. When FEA tools are combined with design of experiments (DOE) methodologies, academic research has shown the potential for virtual DOE to allow for the rapid analysis of manufacturing parameters and their influence on final formed products. However, within the domain of bulk-metal joining, FEA tools are rarely used in industrial applications and limit DOE trails to physical testing which are therefore constrained by financial costs and time.This research explores the suitability of an FEA-based DOE to predict the complex behaviour during bulk-metal joining processes through a case study on the staking of spherical bearings. For the two DOE outputs of pushout strength and post-stake torque, the FEA-based DOE error did not exceed ±1.2% and ± 1.5 Nm respectively which far surpasses what was previously capable from analytically derived closed-form solutions. The outcomes of this case study demonstration the potential for FEA-based DOE to provide an inexpensive, methodical, and scalable solution for modelling bulk-metal joining process

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