Development of predictive models for wire electrical discharge machining of aluminium metal matrix composites by using regression analysis and neural network

Surendranatha, G. M.; Naidu, B. Vishnu Vardhana; Gangaraju, Moopuri; Sarapure, Sadanand; Hemanth, KR.

doi:10.1016/j.matpr.2022.07.258

Cited by 4 publications

(1 citation statement)

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“…The neural networks, providing convenience for people to reduce the time for calculation of topology optimization, have been proven to be powerful functions in data learning and generalization, which can be used in classification, clustering, and regression [21][22][23]. Many researchers have aimed to reduce the time of structural design by combining neural networks with the topology optimization method.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Structure Generation Based on Data-Driven Using Machine Learning

2023

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Topology optimization results are highly dependent on the given design constraints and boundary conditions. Moreover, small changes in initial design conditions can result in different topological configurations, which makes topology optimization time-consuming in a given design constraint domain and inefficient in structural design. To address this problem, a data-driven real-time topology optimization framework and method coupled with machine learning by using a principal component analysis algorithm combined with a feedforward neural network are developed in this paper. Meanwhile, through the offline training, the mapping relationship between initial design conditions and topology optimization results is obtained. From this mapping, we estimate the optimal topologies for novel loading configurations. Numerical examples display that the online prediction results are consistent with the results of the topology optimization method. Furthermore, the network parameters are calibrated, and accurate structure prediction is achieved based on the algorithm. In addition, this method ensures the accuracy of high-resolution structural prediction on the premise of small samples.

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Section: Introductionmentioning

confidence: 99%