Microstructure prediction of multi-directional forging for 30Cr2Ni4MoV steel by the secondary development of Deform software and BP neural network

Luo, Junting; Zhao, Jingqi; Yang, Zheyi; Jin, Yongbo; Zhang, Chunxiang

doi:10.1007/s00170-021-08615-w

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…As an algorithm for mining historical data and learning case experience, BP neural network has been applied in many machinery fields, such as microstructure prediction of multi-directional forging 17 and mold breakout prediction in slab. 18 Combining the shortcomings of the above researches and the BP neural network effect in data mining, an intelligent decision-making method of CAM NC programming parameters based on BP neural network algorithm is proposed.…”

Section: Introductionmentioning

confidence: 99%

Research on intelligent decision method of computer-aided manufacturing numerical control parameters based on model-based definition and back propagation neural networks

Wang

Zhang

Wang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Aiming at the low efficiency and intelligence degree in the parameter setting of computer-aided manufacturing (CAM) numerical control (NC) programming caused by manual work, an intelligent decision-making method of CAM NC programming parameters based on model-based definition (MBD) is proposed. The hierarchical process model uses MBD technology to express the process flow and manage the process information. The tightness between process and NC machining is enhanced by sorting out NC machining steps and extracting their process information automatically. Then a back propagation (BP) neural network is used to mine the experience of NC programming in historical cases. Sparrow search algorithm (SSA) and other methods are used to optimize BP neural network’s initial weight and threshold, learning rate, and feedback process. A CAM parameter decision model based on BP neural network is constructed. Taking the connecting rod of a certain type of diesel engine as the experimental object, the method increases programming efficiency by 47%, with an accuracy rate of 80%. It is proved that the intelligent decision-making method proposed in this paper has practicability and feasibility.

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Section: Introductionmentioning

confidence: 99%

Research on intelligent decision method of computer-aided manufacturing numerical control parameters based on model-based definition and back propagation neural networks

Wang

Zhang

Wang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Application of neural network in micromechanical deformation behaviors of Inconel 740H alloy

Wang

Liu

Song

et al. 2023

Int J Adv Manuf Technol

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Multi-Objective Optimization of the Forming Process Parameters of Disc Forgings Based on Grey Correlation Analysis and the Response Surface Method

Wei,

Liang,

et al. 2024

Applied Sciences

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This paper introduces a multi-objective optimization problem (MPO) for the forming process parameters of disc forgings using grey relational analysis (GRA) and the response surface methodology (RSM). Firstly, an experimental design based on the Box–Behnken design (BBD) principle was established, and simulations were performed in Deform to obtain response data. Secondly, GRA was used to transform the MPO into a grey relational degree (GRD) problem, and the entropic weight method was integrated to ascertain the influence weights of each variable on GRD. Then, a quadratic polynomial prediction model based on the RSM was constructed, and its accuracy was ensured through model validation. Finally, the optimal process parameter combination was determined through the particle swarm optimization algorithm, which included a friction coefficient of 0.3, an initial temperature of 1250 °C, and a downward pressing speed of 7.5 mm/s. The results of the experimental investigation indicate that optimized process parameters significantly reduce the forming load, equivalent stress, and damage value, effectively enhancing the overall quality of forged parts.

show abstract

Microstructure prediction of multi-directional forging for 30Cr2Ni4MoV steel by the secondary development of Deform software and BP neural network

Cited by 3 publications

References 19 publications

Research on intelligent decision method of computer-aided manufacturing numerical control parameters based on model-based definition and back propagation neural networks

Research on intelligent decision method of computer-aided manufacturing numerical control parameters based on model-based definition and back propagation neural networks

Application of neural network in micromechanical deformation behaviors of Inconel 740H alloy

Multi-Objective Optimization of the Forming Process Parameters of Disc Forgings Based on Grey Correlation Analysis and the Response Surface Method

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