Multi-Objective Optimization of Additive Manufacturing Process

Asadollahi-Yazdi, Elnaz; Gardan, Julien; Lafon, Pascal

doi:10.1016/j.ifacol.2018.08.250

Cited by 27 publications

(11 citation statements)

References 12 publications

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“…In the multiobjective one, it is desirable to produce a function comprised of performance parameters to be treated as variables that can address variations of all the parameters at the same time. New function will be treated as objective function for the job and the performance parameters act as function variables (Asadollahi-Yazdi, Gardan, and Lafon, 2018). After that, the process for the optimization remains the same (Guohua et al, 2013).…”

Section: Particle Swarm Optimization 54mentioning

confidence: 99%

Optimal design of tricept parallel manipulator with particle swarm optimization using performance parameters

Farooq

2021

JER is an international, peer-reviewed journal that publishes f

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The parallel manipulators are skilled for their precision manufacturing but need optimized design to get maximum dexterity that will lead towards better industrial production rates. The 3-DOF tricept is chosen to utilize its maximum capabilities for its functionality. Three performance parameters conditioning index, workspace volume, and global conditioning index are used to obtain optimum design variables of tricept mechanism. With a view to compare them in terms of processing effort, particle swarm optimization (PSO) is applied here. Finally, multiobjective optimization with two strategies weighted and epsilon constraint is performed to control the different parameters simultaneously and also to give validation of previously obtained GA based optimum design values of tricept mechanism.

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Section: Particle Swarm Optimization 54mentioning

confidence: 99%

Optimal design of tricept parallel manipulator with particle swarm optimization using performance parameters

Farooq

2021

JER is an international, peer-reviewed journal that publishes f

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“…Most optimization methods aim to maximize the quality of the surface but, as observed by Asadollahi-Yazdi et al [37], the user should obtain the surface quality in accordance to design specifications. In other words, at the end of the optimization process, the manufactured object should show that the surface quality requirements are satisfied.…”

Section: Build Orientationmentioning

confidence: 99%

“…where: With the aim to implement more reliable methods, Asadollahi-Yazdi et al [37] suggested evaluating build time and material directly from the set of command instructions given to the AM machine to realize the part. This approach, whose computation cost is enormous, cannot be based on simply reading the G-Code, but must take into account all that information, such as the jerk and acceleration laws, related to the specific machine [5].…”

Section: Costmentioning

confidence: 99%

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Search for the Optimal Build Direction in Additive Manufacturing Technologies: A Review

Angelo

Stefano

Guardiani

2020

JMMP

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By additive manufacturing technologies, an object is produced deposing material layer by layer. The piece grows along the build direction, which is one of the main manufacturing parameters of Additive Manufacturing (AM) technologies to be set-up. This process parameter affects the cost, quality, and other important properties of the manufactured object. In this paper, the Objective Functions (OFs), presented in the literature for the search of the optimal build direction, are considered and reviewed. The following OFs are discussed: part quality, surface quality, support structure, build time, manufacturing cost, and mechanical properties. All of them are distinguished factors that are affected by build direction. In the first part of the paper, a collection of the most significant published methods for the estimation of the factors that most influence the build direction is presented. In the second part, a summary of the optimization techniques adopted from the reviewed papers is presented. Finally, the advantages and disadvantages are briefly discussed and some possible new fields of exploration are proposed.

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“…In a competitive market of AM services, manufacturing costs must be estimated in a reliable way [6]- [8], which is why the accurate estimation of build times is crucial. A reliable quantification of build time also aids ascertainment of the deposition direction, which minimises the manufacturing costs [9]- [23].…”

Section: Introductionmentioning

confidence: 99%

An advanced GCode analyser for predicting the build time for additive manufacturing components

2020

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<p class="Abstract">Additive manufacturing is a technology for quickly fabricating physical models, functional prototypes, and small batches of parts by stacking two-dimensional layered features directly from computer-aided design data. One of the most important challenges in this sector relates to the capability to predict the build time in advance, since this is crucial to evaluating the production costs. In this paper, an accurate method for obtaining build-time is proposed. This method is based on an advanced GCode analyzer written in Python following an object-oriented paradigm for scalability and maintainability. Various examples are used to demonstrate the reliability of the algorithm, while its potential applications are also illustrated.</p>

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Multi-Objective Optimization of Additive Manufacturing Process

Cited by 27 publications

References 12 publications

Optimal design of tricept parallel manipulator with particle swarm optimization using performance parameters

Optimal design of tricept parallel manipulator with particle swarm optimization using performance parameters

Search for the Optimal Build Direction in Additive Manufacturing Technologies: A Review

An advanced GCode analyser for predicting the build time for additive manufacturing components

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