Effect and control of path parameters on thickness distribution of cylindrical cups formed via multi-pass conventional spinning

Gondo, Shiori; Arai, Hirohiko

doi:10.1007/s10845-021-01886-w

Cited by 5 publications

(5 citation statements)

References 57 publications

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“…Drum raising mechanism is an important part of shearer. In the process of working, the shearer adjusts the drum height up and down within the height range of the coal seam to adapt to the changes of the coal seam through the drum raising mechanism, so as to achieve the best coal cutting effect and obtain the maximum recovery rate [13]. At present, the automatic drum elevation control based on memory cutting control technology refers to that the controller automatically controls the shearer elevation mechanism to adjust the drum height within the height range of coal seam according to the set change law of the top and bottom floor.…”

Section: High Adjustment Control Mechanism Of Shearer Drum Based On D...mentioning

confidence: 99%

Retracted: Research on intelligent elevation control method of shearer drum based on deep learning

Zhang

2023

IET Software

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Research on deep learning and shearer drum elevation control is a hot topic in recent years. The shearer drum elevation control based on deep learning has been paid attention to by the majority of researchers and a series of studies have been carried out. In order to solve the problem that the traditional memory cutting method of shearer is not accurate, the research on intelligent elevation control method of shearer drum based on deep learning is proposed. In this essay, the intelligent control system of drum elevation of shearer is modelled mathematically, and the improved deep-learning firefly algorithm is used to optimise the PID parameters of the drum control system, and the Simulink simulation model of the intelligent control system of drum elevation is built. The response of the system using the improved and basic firefly algorithm is analysed. The experimental results show that the overshoot of the optimised system decreases by 21.6% under the step signal. Under the ramp signal, the overshoot of the optimised system decreased by 38.4%. Under the pulse interference signal, the overshoot of the optimised system is reduced by 21.9%. Under the step interference signal, the overshoot of the optimised system is reduced by 33.2%. Under the random interference signal, the wave range of the optimised response curve is reduced by 40.1%. Conclusion: After adding the PID controller optimised by the improved deep learning firefly algorithm to the drum elevation control system, the response performance and anti-interference performance of the system are greatly improved and enhanced. The research of this article provides important guidance for deep learning and shearer drum elevation control. K E Y W O R D S biomedical electronics, combinatorial mathematics | INTRODUCTIONThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: High Adjustment Control Mechanism Of Shearer Drum Based On D...mentioning

confidence: 99%

Retracted: Research on intelligent elevation control method of shearer drum based on deep learning

Zhang

2023

IET Software

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“…The experimental results by Gondo and Arai (2022a) were reused for the training data of the ANN. These were 54 sets of input/output data, which included the following three types of results: I: 18 sets of data derived from the partly randomized input based on an orthogonal array by the Taguchi method; II: 17 sets of data derived from the input of uniform random numbers; III: 19 sets of data used for the verification of thickness control.…”

Section: Methodsmentioning

confidence: 99%

“…Next, the convergence speed of the proposed algorithm using the pseudoinverse Jacobian was compared with that of the previous algorithm (Gondo and Arai, 2022a) © The Japan Society of Mechanical Engineers ℎ=75mm, 𝑡 50% =0.75mm, 𝑙 𝑠 =0mm, and 𝑇 form =120s. The subtasks in the task-priority control were not used, and 𝜹𝒑 𝒔 = 𝟎 in Eq.…”

Section: Convergence Of Iterative Solutionmentioning

confidence: 99%

Roller path solver system for multi-objective task-priority control of multipass conventional spinning

GONDO,

ARAI

2023

Mechanical Engineering Journal

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A general design method for roller paths for multipass conventional spinning has not been established yet. The roller paths in a production site are often decided by experienced spinners by trial and error. In this paper, we propose a roller path solver system, which can automatically generate a roller path for spinning a cylindrical cup and simultaneously realize multiple target values of the spun product and forming process. On the basis of an artificial neural network trained with parameters of the roller paths and property values of the forming results, the path parameters that satisfy the target output are solved by an iterative algorithm using a pseudoinverse Jacobian matrix. In addition, the null space of the Jacobian matrix is used to handle the main and subtasks according to their priority. The experiments show that the target cup height, wall thickness, forming time, and surplus path length can be simultaneously achieved as the main task while moderately controlling the number of passes and the roller feed ratio as the subtask.

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“…S.C. Ma analyzed the results of V-bending tests by using artificial neural networks and finite element simulations, respectively, in order to improve the accuracy of the springback prediction of rigid strength steels under various conditions, confirming that artificial neural networks and numerical simulations are effective tools for springback prediction [16]. Gondo used artificial neural networks in multi-pass conventional spinning artificial neural networks to form the distribution of the raceway parameters of cylindrical cups, with respect to the thickness distribution of the entire height of the workpiece; artificial neural networks were used to achieve the path parameter solutions required for constant thickness [17]. Yang proposed a genetic-algorithm-based back-propagation neural network optimization model for predicting and optimizing the wall angles of variable-wall-angle circular tables [18].…”

Section: Introductionmentioning

confidence: 90%

Prediction of Spherical Sheet Springback Based on a Sparrow-Search-Algorithm-Optimized BP Neural Network

Zhang²,

Xu³

2022

Metals

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Springback is an unavoidable problem in cold-forming processes and affects the efficiency and quality of the processing of outer sheets for ships. Therefore, effective control and prediction of sheet-forming springback is particularly important in the field of cold-bending processes. To this end, this paper presents research on cold-bending springback prediction based on a study of the multipoint cold-bending process combined with intelligent algorithms, as well as research on the multipoint cold-bending production of ship-hull plates. The forming process of spherical sheets was simulated by a finite element simulation. The amount of springback under different processes was studied, and the forming state and springback state were briefly analyzed. Then an in-depth study of machine learning was carried out, and the sparrow search algorithm (SSA) was introduced based on a back-propagation neural network (BPNN). The purpose of this integration was to prevent the BP neural network model from falling into local optimal solution problems. Then simulation data were obtained with the help of a simulation to build a backpropagation neural network prediction model, which was optimized based on the sparrow search algorithm, and training tests were conducted. Then the prediction results of the model were compared with the simulation data to verify that the prediction accuracy performance of the sparrow-search-algorithm-optimized BPNN model was improved. Finally, the prediction model based on the SSA–BPNN algorithm was compared with the prediction models of different algorithms, and the prediction results showed that SSA–BPNN outperformed other algorithms in prediction accuracy and speed; its prediction error was within 4%, which meets on-site processing requirements. The sparrow-search-algorithm-based optimization of BPNN was confirmed to have strong applicability in springback prediction.

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Effect and control of path parameters on thickness distribution of cylindrical cups formed via multi-pass conventional spinning

Cited by 5 publications

References 57 publications

Retracted: Research on intelligent elevation control method of shearer drum based on deep learning

Retracted: Research on intelligent elevation control method of shearer drum based on deep learning

Roller path solver system for multi-objective task-priority control of multipass conventional spinning

Prediction of Spherical Sheet Springback Based on a Sparrow-Search-Algorithm-Optimized BP Neural Network

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