ArticleMaterials (Basel, Switzerland)2020
Improving Prediction of Springback in Sheet Metal Forming Using Multilayer Perceptron-Based Genetic Algorithm.
Article in Materials (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Simulation and Prediction of Springback in Sheet Metal Bending Process Based on Embedded Control System.Sensors (Basel, Switzerland) · 2024Article
- Optimisation of Flexible Forming Processes Using Multilayer Perceptron Artificial Neural Networks and Genetic Algorithms: A Generalised Approach for Advanced High-Strength Steels.Materials (Basel, Switzerland) · 2024Article
- MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing.Nature communications · 2024Article
- Current Trends in Metallic Materials for Body Panels and Structural Members Used in the Automotive Industry.Materials (Basel, Switzerland) · 2024Review
- Analysis of the Frictional Performance of AW-5251 Aluminium Alloy Sheets Using the Random Forest Machine Learning Algorithm and Multilayer Perceptron.Materials (Basel, Switzerland) · 2023Article
- On the Springback and Load in Three-Point Air Bending of the AW-2024 Aluminium Alloy Sheet with AW-1050A Aluminium Cladding.Materials (Basel, Switzerland) · 2023Article
- Forecasting the Fatigue Strength of DC01 Cold-Formed Angles Using the Anisotropic Barlat Model.Materials (Basel, Switzerland) · 2022Article
- Finite Element Simplifications and Simulation Reliability in Single Point Incremental Forming.Materials (Basel, Switzerland) · 2022Article
- Modelling of Friction Phenomena Existed in Drawbead in Sheet Metal Forming.Materials (Basel, Switzerland) · 2021Article
- A Versatile Punch Stroke Correction Model for Trial V-Bending of Sheet Metals Based on Data-Driven Method.Materials (Basel, Switzerland) · 2021Article
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2 authors.
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Abstract
This paper presents the results of predictions of springback of cold-rolled anisotropic steel sheets using an approach based on a multilayer perceptron-based artificial neural network (ANN) coupled with a genetic algorithm (GA). A GA was used to optimise the number of input parameters of the multilayer perceptron that was trained using different algorithms. In the investigations, the mechanical parameters of sheet material determined in uniaxial tensile tests were used as input parameters to train the ANN. The springback coefficient, determined experimentally in the V-die air bending test, was used as an output variable. It was found that specimens cut along the rolling direction exhibit higher values of springback coefficient than specimens cut transverse to the rolling direction. An increase in the bending angle leads to an increase in the springback coefficient. A GA-based analysis has shown that Young's modulus and ultimate tensile stress are variables having no significant effect on the coefficient of springback. Multilayer perceptrons trained by back propagation, conjugate gradients and Lavenberg-Marquardt algorithms definitely favour punch bend depth under load as the most important variables affecting the springback coefficient.
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