An automatic and optimal selection of parts orientation in additive manufacturing

Abstract

Purpose In additive manufacturing processes such as stereolithography and fused deposition modeling, optimal part orientation is pivotal in improving the quality of the part. This paper aims to propose an automatic and optimal part orientation system to improve part quality/accuracy in additive manufacturing, which minimizes the production time and hence reduces the cost of product. Design/methodology/approach The developed system reads STEP AP 203 E2 file from CATIA V5 and generates data extraction output file by extracting the relevant geometrical and topological data using an object-oriented approach. Afterwards, the algorithms and rules are developed to extract and recognize feature faces along with their geometric properties such as face type, face area, parallelism and perpendicularity. The feature data obtained that are used to develop feasible part orientations depend on the maximization of G&DT for all part faces. The automatic slicing is then achieved by creating slicing file using CATVBA editor inside CATIA V5. Findings After slicing, output data are exported in Excel data sheet to calculate the total additive volume of the part. The building time of the part is then calculated on the basis of machine parameters, part geometry, part height, layer thickness and amount of support volume needed to build the part. The optimal orientation of the part is achieved by maximization of G&DT value and minimization of production time. The proposed methodology is tested using an illustrative example. Originality/value Although lot of approaches have been discussed in the literature, automation of setup planning/orientation of the part in additive manufacturing is not fully attained. Therefore, the article focuses on the automation of setup planning by adding automatic feature extraction and recognition module along with the automatic slicing during setup planning. Moreover, the significance of adding feature extraction and recognition module is to achieve best accuracy for form feature faces and hence reduction in post processing machining/finishing operations.