Evolutionary Computing and Genetic Algorithms: Paradigm Applications in 3D Printing Process Optimization

Canellidis, V.; Giannatsis, John; Dedoussis, V.

doi:10.1007/978-3-662-49179-9_13

Cited by 9 publications

(7 citation statements)

References 31 publications

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“…This problem is also referred to in the literature as optimal packing (see e.g. Canellidis et al, 2016). Nesting problems can be considered a type of cutting and packing problem and have been intensively dealt with in the literature.…”

Section: Background and Definitionmentioning

confidence: 99%

“…Since then, the optimal build orientation problem has been intensively dealt with in the relevant literature, and different papers reviewing the techniques employed for optimal build orientation-apart from an early overview by Kulkarni et al (2000)-are Pandey et al (2004), Taufik and Jain (2013), Canellidis et al (2016), Di Angelo et al (2020b and recently Qin et al (2021). More than 70 contributions can be identified, therefore it is not feasible to provide a summary of references as in other sections, and instead the interested readers are referred to the aforementioned reviews.…”

Section: Evolution and Problem Variantsmentioning

confidence: 99%

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An overview on the use of operations research in additive manufacturing

Framiñán

Fernández-Viagas

2022

Ann Oper Res

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Additive Manufacturing (AM) is a disruptive production technology that challenges many well-established, conventional practices in Operations and Supply Chain Management decisions. Consequently, new context-specific decision problems have appeared in this area, while existing decision problems must be adapted or extended to this context. In this overview, these decision problems in AM are highlighted and classified, describing the different decision in an unified manner and citing the underlying OR techniques that have been applied to solve them. The aim of the paper is that, by presenting an overview of the AM-related problems in a systematic, OR- or Decision-Making-centred (rather than technology-centred) manner, the OR community can become more aware of this stream of research and thus be more active and contribute with some high-quality work. Open research challenges, as well as avenues for future research are also discussed.

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Section: Background and Definitionmentioning

confidence: 99%

Section: Evolution and Problem Variantsmentioning

confidence: 99%

An overview on the use of operations research in additive manufacturing

Framiñán

Fernández-Viagas

2022

Ann Oper Res

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“…One of the preferred ways to tackle such problems is using evolution-based meta-heuristics, a practice that has gained popularity in the last years (Dumas et al, 2014;Eiben and Smith, 2015;Canellidis et al, 2016). Algorithms in this category provide a good balance between exploration and exploitation of the search space, abstracting themselves from problemspecific traits, and performing well over problems that classic methods would have trouble solving, if at all (Talbi, 2009).…”

Section: Evolutionary Algorithms and Differential Evolution (De)mentioning

confidence: 99%

Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process

et al. 2021

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Multiobjective optimization approaches have allowed the improvement of technical features in industrial processes, focusing on more accurate approaches for solving complex engineering problems and support decision-making. This paper proposes a hybrid approach to optimize the 3D printing technology parameters, integrating the design of experiments and multiobjective optimization methods, as an alternative to classical parametrization design used in machining processes. Alongside the approach, a multiobjective differential evolution with uniform spherical pruning (usp-MODE) algorithm is proposed to serve as an optimization tool. The parametrization design problem considered in this research has the following three objectives: to minimize both surface roughness and dimensional accuracy while maximizing the mechanical resistance of the prototype. A benchmark with nondominated sorting genetic algorithm II (NSGA-II) and with the classical sp-MODE is used to evaluate the performance of the proposed algorithm. With the increasing complexity of engineering problems and advances in 3D printing technology, this study demonstrates the applicability of the proposed hybrid approach, finding optimal combinations for the machining process among conflicting objectives regardless of the number of decision variables and goals involved. To measure the performance and to compare the results of metaheuristics used in this study, three Pareto comparison metrics have been utilized to evaluate both the convergence and diversity of the obtained Pareto approximations for each algorithm: hyper-volume (H), g-Indicator (G), and inverted generational distance (IGD). To all of them, ups-MODE outperformed, with significant figures, the results reached by NSGA-II and sp-MODE algorithms.

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“…Recently, there have been many studies on AM part-grouping and scheduling problems. Some studies dealt with the detail of part-grouping or nesting [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], while others focused on scheduling rather than the detail of part-grouping or nesting. In this section, a literature review of studies of the latter is conducted.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraints ( 9), (10), and (11) ensure that there is an order for each build in each machine. Constraint (12) calculates the completion time of each build by considering the sequence depend on setup time. Constraint (13) ensures that the completion time of the first build of each machine is the same as the processing time of that build.…”

Section: Milp Modelmentioning

confidence: 99%

Part-grouping and build-scheduling with sequence-dependent setup time to minimize the makespan for non-identical parallel additive manufacturing machines

Kim

2021

Int J Adv Manuf Technol

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This paper addresses a part-grouping and build-scheduling problem for an additive manufacturing process with non-identical parallel additive manufacturing machines. Parts with the same material can be simultaneously manufactured as a build in the machine. The total areas of parts in the build cannot exceed the area capacity of the machine. Each machine has different heights in a build area. The maximum height of parts in the build is less than or equal to the height of each machine in the build area. The processing time of each build is the maximum processing time of parts belonging to the build. When the material is changed in each machine, the setup time occurs. The objective function is minimizing the makespan. To find optimal and near-optimal solutions for the problem, a mixed integer linear programming model and three meta-heuristic algorithms are presented. For small problem instances, the performance of three meta-heuristic algorithms is compared with the optimal value obtained mixed integer linear programming model. For large problem instances, the performance of three meta-heuristic algorithms is compared to each other.

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Evolutionary Computing and Genetic Algorithms: Paradigm Applications in 3D Printing Process Optimization

Cited by 9 publications

References 31 publications

An overview on the use of operations research in additive manufacturing

An overview on the use of operations research in additive manufacturing

Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process

Part-grouping and build-scheduling with sequence-dependent setup time to minimize the makespan for non-identical parallel additive manufacturing machines

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