Personalized design of part orientation in additive manufacturing

Abstract

Purpose Orientation determination is an essential planning task in additive manufacturing (AM) because it directly affects the part quality, build time, geometric tolerance, fabrication cost, etc. This paper aims to propose a negative feedback decision-making (NFDM) model to realize the personalized design of part orientation in AM process. Design/methodology/approach NFDM model is constructed by integrating two sub-models: proportional–integral–derivative (PID) negative feedback control model and technique for order preference by similarity to an ideal solution (TOPSIS) decision-making model. With NFDM model, a desired target is first specified by the user. Then, the TOPSIS decision model calculates the “score” for the current part orientation. TOPSIS decision model is modified for ease of control. Finally, the PID controller automatically rotates the part based on the error between the user-specified target and the calculated “score”. Part orientation adjustment is completed when the error is eliminated. Five factors are considered in NFDM model, namely, surface roughness, support structure volume, geometric tolerance, build time and fabrication cost. Findings The case studies of turbine fan and dragon head indicate that the TOPSIS model can be perfectly integrated with the PID controller. This work extends the proposed model to different AM processes and investigates the feasibility of combining different decision-making models with PID controller and the effects of including various evaluation criteria in the integrated model. Originality/value The proposed model innovatively takes the TOPSIS decision-making model and the PID control model as a whole. In this way, the uncontrollable TOPSIS model becomes controllable, so the proposed model can control the TOPSIS model to achieve the user-specified targets.

Status, issues, and future of computer-aided part orientation for additive manufacturing

“…In the method of [107], the features of 3D models were divided into hole, revolution surface, round surface, thin AM processes Convex hull generation MMFs [111] AM processes Feature recognition WSM [112] AM processes Feature recognition WSM [113] LPBF Feature recognition WSM [114] SLA Quaternion rotation OWAO [115] AM processes Feature recognition FAOs [116] SLA, FDM Quaternion rotation WSM [117] AM processes Facet clustering WSM [118,119] LPBF Facet clustering WSM OOF stands for overall objective function; PP stands for part property; PA stands for part accuracy; SQ stands for surface quality; SS stands for support structure; BT stands for build time; BC stands for build cost; PT stands for post-processing time; PC stands for post-processing cost; MMFs stands for min-max functions; WSM stands for weighted sum model; DF stands for deviation function; 4 processes include SLA, SLS, FDM, LOM; OWAO stands for ordered weighted averaging operator; FAOs stands for fuzzy aggregation operators structure, inter/external surface, plane, freeform surface, inclined plane, and overhang. On the basis of these features, a set of rules were designed to generate the ABOs for an SLA part with an objective of improving the surface quality of the part.…”

“…In the method of [116], personalised design of the build orientation of an SLA/FDM part was studied. A certain number of ABOs of the part were also determined via quaternion rotation.…”

Description Logic Ontology-Supported Part Orientation for Fused Deposition Modelling

“…Unlike one-step approaches, a two-step approach first shrinks the solution space by generating a certain number of alternative orientations and then selects an orientation that best meets certain requirements from the generated alternatives. The techniques applied to generate alternative orientations mainly include shape feature recognition [51][52][53][54], convex hull generation [55], quaternion rotation [56,57], and triangular facet clustering [58,59]. The techniques used to select an optimal orientation mainly include deviation model [51], weighted sum model [53][54][55]57,58,60], deviation-similarity model [61], ordered weighted averaging operator [56], fuzzy aggregation operators [62,63], and double-layer priority aggregation multi-criterion decision-making [64].…”

Nanomaterials for 3D Printing of Polymers via Stereolithography: Concept, Technologies, and Applications

“…[ 161 ] The reinforcement materials derived from the cellulose are environmentally friendly, relatively cheap to produce and they can be combined with other materials. [ 11–16 ]…”

“…The user has to consider that the position of the object in the printer affects its mechanical properties, the printing time and the quantity of support material needed. [ 14,15 ] Second, the STL file is sliced into vertical layers and converted to geometric code (G‐code), containing a series of 2D horizontal cross‐sections commands. Usually, the slicing software is provided by the printer manufacturers, but there are free software's available, such as Ultimaker Cura, that can be used for different processes.…”