A statistical method for build orientation determination in additive manufacturing

Abstract

Purpose For part models with complex shape features or freeform shapes, the existing build orientation determination methods may have issues, such as difficulty in defining features and costly computation. To deal with these issues, this paper aims to introduce a new statistical method to develop fast automatic decision support tools for additive manufacturing build orientation determination. Design/methodology/approach The proposed method applies a non-supervised machine learning method, K-Means Clustering with Davies–Bouldin Criterion cluster measuring, to rapidly decompose a surface model into facet clusters and efficiently generate a set of meaningful alternative build orientations. To evaluate alternative build orientations at a generic level, a statistical approach is defined. Findings A group of illustrative examples and comparative case studies are presented in the paper for method validation. The proposed method can help production engineers solve decision problems related to identifying an optimal build orientation for complex and freeform CAD models, especially models from the medical and aerospace application domains with much efficiency. Originality/value The proposed method avoids the limitations of traditional feature-based methods and pure computation-based methods. It provides engineers a new efficient decision-making tool to rapidly determine the optimal build orientation for complex and freeform CAD models.

Determination of optimal build orientation for additive manufacturing using Muirhead mean and prioritised average operators

“…However, considering more number of objectives is likely to generate large number of Pareto-optimal solutions (most of them are not the real optimal solutions) (Ancău and Caizar 2010), and will greatly extend the convergence time of the optimisation algorithm. These can hardly be accepted for the actual OBO determination (Zhang et al 2018).…”

“…The basic principle of this category of methods is to use specific MADM techniques to estimate the overall score of each ABO in the case of comprehensively considering multiple attributes, and to sort all ABOs according to their overall scores (from high to low), and to select the ABO ranked first as the OBO. Representative methods based on this principle are the methods proposed by Pham et al (1999), West et al (2001), Byun and Lee (2006), Chen et al (2008), Zhang et al (2016Zhang et al ( , 2018, Ransikarbum and Kim (2017), and Qie et al (2018). A brief summarisation of these methods is listed in Table 2.…”

“…The different attributes used for OBO determination are usually not independent of each other, but are contradictory, interrelated, or prioritised. It can be seen from Table 2 that most of the methods [except the method presented by Zhang et al (2016)] assume that the attributes are independent of each other when aggregating their values (Zhang et al 2016(Zhang et al , 2018. 2Negative influence from the deviation of attribute values has never been reduced.…”

“…MOO algorithms may encounter difficulty in forming the Pareto front when dealing with some optimisation cases with more than three objectives, which are common in most OBO determination problems. In addition, large number of optimal solutions on the Pareto front would bring difficulty to later determination (Zhang et al 2018). MADM methods avoid these issues to some extent.…”

“…At present, build orientations are manually specified by AM machine operators according to their expertise. Under the same circumstances, different operators may be in favour of different build orientations, which may greatly increase the uncertainty of process planning and directly affect the build cost, build time, accuracy, and quality of the parts (Zhang et al 2018).…”