Build orientation optimization problem in additive manufacturing

Additive manufacturing (AM) is an emerging type of production technology to create three-dimensional objects layer-by-layer directly from a 3D CAD model. AM is being extensively used by engineers and designers. Build orientation is a critical issue in AM since it is associated with the object accuracy, the number of supports required and the processing time to produce the object. Finding the best build orientation in the AM will reduced significantly the building costs and will improve the object accuracy. This paper presents an optimization approach to solve the part build orientation problem considering the staircase effect, support area characteristics and the build time. Two global optimization methods, the Electromagnetism-like and the Stretched Simulated Annealing algorithms, are used to study the optimal orientation of four models. Preliminary experiments show that both optimization methods can effectively solve the build orientation problem in AM, finding several global solutions.This work has been supported and developed under the FIBR3D project - Hybrid processes based on additive manufacturing of composites with long or short fibers reinforced thermoplastic matrix (POCI-01-0145-FEDER-016414), supported by the Lisbon Regional Operational Programme 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Improving additive manufacturing performance by build orientation optimization

Additive manufacturing (AM) is an emerging type of production technology to create three-dimensional objects layer-by-layer directly from a 3D CAD model. AM is being extensively used in several areas by engineers and designers. Build orientation is a critical issue in AM since it is associated with the part accuracy, the number of supports required and the processing time to produce the object. This paper presents an optimization approach to solve the part build orientation problem taking into account some characteristics or measures that can affect the accuracy of the part, namely the volumetric error, the support area, the staircase effect, the build time, the surface roughness and the surface quality. A global optimization method, the Electromagnetism-like algorithm, is used to solve the part build orientation problem.The authors are grateful to the anonymous referees for their fruitfulcomments and suggestions. This work has been supported and developed under the FIBR3Dproject - Hybrid processes based on additive manufacturing of composites with long or shortfibers reinforced thermoplastic matrix (POCI-01-0145-FEDER-016414), supported by theLisbon Regional Operational Programme 2020, under the PORTUGAL 2020 PartnershipAgreement, through the European Regional Development Fund (ERDF). This work hasbeen also supported by national funds through FCT - Funda ̧c ̃ao para a Ciˆencia e Tecnologiawithin the Project Scope: UID/CEC/00319/201

A multi-objective approach to solve the build orientation problem in additive manufacturing

Additive manufacturing (AM) has been increasingly used in the creation of three-dimensional objects, layer-by-layer, from three-dimensional (3D) computer-aided design (CAD) models. The problem of determining the 3D model printing orientation can lead to reduced amount of supporting material, build time, costs associated with the deposited material, labor costs, and other factors. This problem has been formulated and studied as a single-objective optimization problem. More recently, due to the existence and relevance of considering multiple criteria, multi-objective approaches have been developed. In this paper, a multi-objective optimization approach is proposed to solve the part build orientation problem taking into account the support area characteristics and the build time. Therefore, the weighted Tchebycheff scalarization method embedded in the Electromagnetism-like Algorithm will be used to solve the part build orientation bi-objective problem of four 3D CAD models. The preliminary results seem promising when analyzing the Pareto fronts obtained for the 3D CAD models considered. Concluding, the multi-objective approach effectively solved the build orientation problem in AM, finding several compromise solutions.This work has been supported and developed under the FIBR3D project - Hybrid processes based on additive manufacturing of composites with long or short fibers reinforced thermoplastic matrix (POCI-01-0145-FEDER-016414), supported by the Lisbon Regional Operational Programme 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019

Multi-objective optimization of ultrasonic-assisted magnetic abrasive finishing process

This paper is closed access until 23 November 2019.Ultrasonic-assisted magnetic abrasive finishing (UAMAF) is an advanced abrasive finishing process that finishes a workpiece surface effectually when compared to a traditional magnetic abrasive finishing process in the order of nanometer. A change of surface roughness and material removal rate are two important factors determining the efficacy of the process. These two factors affect the surface quality and production time and, thereby, a total production cost. The finishing performed at higher material removal rates leads to a loss in shape/form accuracy of the surface. At the same time, increasing the rate of change of surface roughness increases loss of material. For an optimized finishing process, a compromise has to be made between the change of surface roughness and the material removal (loss). In this work, a multi-objective optimization technique based on genetic algorithm is used to optimize the finishing parameters in the UAMAF processes. A fuzzy-set-based strategy for a higher level decision is also discussed. The results of the optimization based on a mathematical model of the process are validated with the experimental results and are found to be in compliance