Multi-Objective Optimal Design of Obstacle-Avoiding Two-Dimensional Steiner Trees With Application to Ascent Assembly Engineering

Abstract

We present an effective optimization strategy that is capable of discovering high-quality cost-optimal solution for two-dimensional (2D) path network layouts (i.e., groups of obstacle-avoiding Euclidean Steiner trees) that, among other applications, can serve as templates for complete ascent assembly structures (CAA-structures). The main innovative aspect of our approach is that our aim is not restricted to simply synthesizing optimal assembly designs with regard to a given goal, but we also strive to discover the best tradeoffs between geometric and domain-dependent optimal designs. As such, the proposed approach is centered on a variably constrained multi-objective formulation of the optimal design task and on an efficient coevolutionary solver. The results we obtained on both artificial problems and realistic design scenarios based on an industrial test case empirically support the value of our contribution to the fields of optimal obstacle-avoiding path generation in particular and design automation in general.

Potential identification and industrial evaluation of an integrated design automation workflow

“…Their value will be evaluated by RQ3 as listed in Section 5.1. First solutions were recently prototypically realized, as elaborated by Zăvoianu et al (2019), Hellwig et al (2019), Zăvoianu et al (2018) and summarized in Section 4.3.2. Besides improving the adaptive design process through this optimization, integrating the sketching functionality with ACC-Design bears the potential to further automate both the adaptive and original design processes (as elaborated in Section 4.4).…”

“…In this section, attempts to automate and optimize this task are presented. The implementation of these algorithms is on a prototypical level; details can be found in Zăvoianu et al (2019), Hellwig et al (2019), Zăvoianu et al (2018).…”

“…For brevity, we focus on the algorithm presented by Zăvoianu et al (2019, 2018). The cost function of the optimization is to minimize the Euclidean distance of the path with optional penalties for violating certain angle restrictions (e.g.…”

“…Two example cranes with ascent assemblies are shown in Figure 1. The DA tools used in the case study are a prototypical design space exploration tool generating a number of pre-optimized layout designs (Zăvoianu et al , 2018), and a product configurator, which is in operational use at LWN, to perform assembly configuration tasks mostly occurring during the detailed design phase (Frank et al , 2014). We approach the case study by developing an automation roadmap, i.e.…”