Fast, lean-and-agile, multi-parameter multi-trending robust quality screening in a 3D-printed product

Additive manufacturing (AM) has revolutionized the local production realization of highly customizable items. However, high process complexity - inherent to AM operations renders uncertain quality performance final products. Consequently, there is often a need assess unique fabrication capabilities against reoccurring issues instability and end-product inconsistency. Improvement opportunities may be identified by empirically exploring complex phenomena that regulate Thus, focused quality-screening optimization studies should additionally take into account special for speedy, practical economical experimentation. Robust multi-factorial solvers predict effect strength relying on small samples while possibly dealing with non-linear non-normal trends. We propose nonparametric modification classical Taguchi method in order enable generation rapid robust screening/optimization predictions an arbitrary 3D-printing process. The new methodology elucidated recently published dataset involves difficult application fused deposition compare differences predicted effect-strength magnitudes between two approaches. comment advantages technique might offer over traditional Taguchi-based improvement analysis. emphasis placed ‘assumption-free’ aspect, which embodied solver. It shown proposed tool agile. could also reliably support customized offering faster predictions.