Optimum Design of Machine Tool Structures Based on BP Neural Network and Genetic Algorithm

2013 ◽  
Vol 655-657 ◽  
pp. 1291-1295 ◽  
Author(s):  
Song Mei Yuan ◽  
Zhong Fei Zhan ◽  
Yao Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Machine Tool ◽  
Dynamic Characteristics ◽  
Bp Neural Network ◽  
Traditional Method ◽  
Fem Analysis ◽  
Fem Model ◽  
Static And Dynamic Characteristics ◽  
Machine Tool Structures

In order to estimate and optimize the static and dynamic characteristics of machine tool, the full parameterized FEM model of it is established and studied in the paper. After the FEM analysis of bed, this paper takes a machine bed as example, presents a method of combination of BP Neural-Network(NN) and Genetic-Algorithm(GA) to optimize dynamic characteristics and realizes the structural optimization of the bed. It proved that this method takes less time, and more precision compared to traditional method.

Design and analysis of epoxy granite vertical machining centre base for improved static and dynamic characteristics

2019 ◽  
Vol 234 (3) ◽  
pp. 481-495 ◽  
Author(s):  
Prabhu Raja Venugopal ◽  
P Dhanabal ◽  
PR Thyla ◽  
S Mohanraj ◽  
Mahendrakumar Nataraj ◽  
...  
Keyword(s):  
Cast Iron ◽  
Machine Tool ◽  
Dynamic Characteristics ◽  
Element Model ◽  
Numerical Approach ◽  
Structural Vibration ◽  
Polymer Concrete ◽  
Static Stiffness ◽  
Static And Dynamic Characteristics ◽  
Machine Tool Structures

The structural vibration in conventional machine tools which are generally made of cast iron may lead to poor surface finish of the machined components. This has led to the investigations on alternative materials for machine tool structures such as concrete, polymer concrete and epoxy granite which have higher damping properties but lesser Young's modulus. However, higher static stiffness with higher damping is essential for improving the static and dynamic characteristics of machine tool structures. Hence, this work focuses on replacing the vertical machining centre base made of cast iron with steel reinforced epoxy granite to improve the structural static stiffness. A finite element model of the above base is developed and validated against the experimental data obtained using modal analysis. The validated numerical approach is applied for investigating the seven progressive design configurations of base reinforced with steel. It is found that the epoxy granite base of Design configuration-7 with L-channels has significantly reduced the deformation by 56 and 36% considering milling and drilling operations, respectively, in comparison to cast iron base. Further, the natural frequencies of the above configuration are higher in all the modes (by more than 50%) under consideration than those of the existing cast iron structure. Therefore, the proposed configuration of base is a viable alternative for the existing base in order to achieve higher structural damping. The novelty of the present work is the design of epoxy granite vertical machining centre base using steel reinforcements to improve structural rigidity with ease of manufacturing.

Analysis and Optimization of the Static and Dynamic Characteristics of Head Frame

2011 ◽  
Vol 418-420 ◽  
pp. 2055-2059 ◽  
Author(s):  
Yu Lin Wang ◽  
Na Jin ◽  
Kai Liao ◽  
Rui Jin Guo ◽  
Hu Tian Feng
Keyword(s):  
Machine Tool ◽  
Dynamic Characteristics ◽  
Maximum Stress ◽  
Dynamic Performance ◽  
Mode Shapes ◽  
Cnc Machine ◽  
Static And Dynamic Characteristics ◽  
Maximum Deformation ◽  
Improvement Measures ◽  
Optimal Measure

The head frame is a key component which plays a supportive and accommodative role in the spindle system of CNC machine tool. Improving the static and dynamic characteristics has profound significance to the development of machine tool and product performance. The simplified finite element modal is established with ANSYS to carry out the static and modal analysis. The results showed that the maximum deformation of the head frame was 0.0066mm, the maximum stress was 3.94Mpa, the deformation of most region was no more than 0.0007mm, which all verified that the head frame had a good stiffness and deforming resistance; several improvement measures for dynamic performance were also proposed by analyzing the mode shapes, and the 1st order natural frequency increased 7.33% while the head frame mass only increased 1.58% applying the optimal measure, which improved the dynamic characteristics of the head frame effectively.

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Gas Analysis ◽  
Bp Network ◽  
Quantum Genetic Algorithm ◽  
Transformer Fault

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.

scholarly journals Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters

2021 ◽  
Vol 2083 (3) ◽  
pp. 032010
Author(s):  
Rong Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Network Parameters ◽  
The Neural Network ◽  
Target Effect ◽  
The Empirical Analysis ◽  
Better Than

Abstract The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.

Application of Blow-off Wind Tunnel Control Based on Genetic Algorithm Optimized BP-Neural Network PID Neural Network

2013 ◽  
Vol 310 ◽  
pp. 557-559 ◽  
Author(s):  
Li Ji ◽  
Xiao Fei Lian
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wind Tunnel ◽  
Bp Neural Network ◽  
Control Method ◽  
Network Control ◽  
Operating Conditions ◽  
Algorithm Optimization ◽  
Large Delay ◽  
Neural Network Pid

For a blow-off tunnel running, there is the large delay and lag issues. We build a mathematical model of the wind tunnel Mach number control by the test modeling method, then analyse the pros and cons of various control methods based on BP neural network control algorithm. Put forward genetic algorithm optimization neural network adaptive control method to solve the large inertia of the wind tunnel system, and large delay. A large number of simulation studies, run a variety of operating conditions for the wind tunnel simulation proved that the improved adaptive neural network PID control method is reasonable and effective.

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