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

• Main Author: George Besseris
• Format: Article
• Language: English
• Published: Elsevier 2021-11-01
• Series: Advances in Industrial and Manufacturing Engineering
• Subjects: 3D-product improvement; Robust multifactorial screening/optimization; Fractional factorial designs; Small sample; Data messiness/complexity
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666912921000210

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