Genetic Algorithm-Based Data-Driven Process Selection System for Additive Manufacturing in Industry 4.0

Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection...

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Vydané v:Materials Ročník 17; číslo 18; s. 4544
Hlavní autori: Aljabali, Bader Alwomi, Shelton, Joseph, Desai, Salil
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 16.09.2024
MDPI
Predmet:
3D printing
Additive manufacturing
Algorithms
Automation
Classification
Datasets
Decision making
Design
Designers
Dimensional analysis
Genetic algorithms
Industrial applications
Industrial development
Industry 4.0
Knowledge
Knowledge bases (artificial intelligence)
Manufacturability
Mathematical optimization
Neural networks
Optimization
Particle swarm optimization
Process planning
Process selection
Rapid prototyping
Technology application
Three dimensional printing
Taiwan
genetic algorithm
expert system
design for additive manufacturing
Industry 4.0
ISSN:1996-1944, 1996-1944
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Popis
Shrnutí:Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes.
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SourceType-Scholarly Journals-1
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ISSN:1996-1944
1996-1944
DOI:10.3390/ma17184544