Development of a manufacturability predictor for periodic cellular structures in a selective laser melting process via experiment and ANN modelling

Design for Manufacture and Assembly (DfMA) of Digital Fabrication (Dfab) and Additive Manufacturing (AM) in Construction: A Review

“…The main applications highly adopted machine learning were such as aerospace, automobile, and defense. These include multi-stage Bayesian surrogate models [135][136][137], artificial neuron network (ANN) [138][139][140], inductive design exploration method [141][142][143], support vector machine [69,[144][145], graph convolutional networks [146], surfel convolutional neural network [147], multi-task Gaussian process learning algorithm [148], computational fluid dynamics model [149,150], back propagation neural network [151], and particle swarm optimization method [152,153]. Regarding the AEC industry, machine learning can be implemented effectively in any activities including conceptual design phase, design optimization, cost prediction, transportation, and fabrication time.…”

Design for Manufacture and Assembly (DfMA) of Digital Fabrication (Dfab) and Additive Manufacturing (AM) in Construction: A Review

“…The main applications highly adopt machine learning are such as aerospace, automobile, and defense. These include multi-stage Bayesian surrogate models [129][130][131], artificial neuron network (ANN) [132][133][134][135], inductive design exploration method [136][137][138], support vector machine [69,139,140], graph convolutional networks [141,142], surfel convolutional neural network [143], multi-task Gaussian process learning algorithm [144], computational fluid dynamics model [145,146], back propagation neural network [147], and particle swarm optimization method [148,149]. In terms of geometric flexibility and highly interconnected structures, AM has enabled novel designs and performance improvements in product development [150].…”