scholarly journals Performance of Five-Axis Machine Tool and Intelligent Machining Process

2021 ◽  
Author(s):  
Tzu-Chi Chan ◽  
Hsin-Hsien Lin
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Dynamic Characteristics ◽  
Processing Parameters ◽  
Quality Prediction ◽  
Processing Quality ◽  
Modal Frequency ◽  
Polynomial Neural Network ◽  
Five Axis ◽  
Modal Vibration

Abstract In this study, the processing performance of a five-axis machine tool was analyzed to identify processing weaknesses as the basis for subsequent structural improvements. Data were then integrated through the Abductory Induction Mechanism (AIM) polynomial neural network to predict intelligent processing quality, and an in-depth investigation was conducted by importing processing parameters to predict the surface quality of the finished product.The finite element analysis method was used to analyze the static and dynamic characteristics of the whole machine and to test the structural modal frequency and vibration shape. For modal testing, the experiment used various equipment, including impact hammers, accelerometers, and signal extractors. Subsequent planning of modal frequency band processing experiments was conducted to verify the influence of natural frequencies on the processing level. Finally, according to the machine processing characteristics, a processing experiment was planned. The measurement record was used as the training data of the AIM polynomial neural network to establish the processing quality prediction model.After analysis and an actual machine test comparison, the three-axis static rigidity values of the machine were X: 1.63 Kg/µm, Y: 1.93 Kg/µm, and Z: 3.95 Kg/µm. The modal vibration shape maximum error of the machine was within 6.2%. The processing quality prediction model established by the AIM polynomial neural network could input processing parameters to achieve the surface roughness prediction value, and the actual relative error of the Ra value was within 0.1 µm.Based on the results of cutting experiments, the influence of the dynamic characteristics of the machine on the processing quality was obtained, especially in the modal vibration environment, which had an adverse effect on the surface roughness. Hence, the surface roughness of the workpiece processed by the machine could be predicted.

Surface Roughness Prediction of Engineering Ceramics Electro-Spark Machining Based on Rough Set Neural Network

2010 ◽  
Vol 43 ◽  
pp. 269-273
Author(s):  
Xue Li ◽  
He Wang ◽  
Shu Fen Chen
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Processing Parameters ◽  
Relational Model ◽  
Engineering Ceramics ◽  
Roughness Prediction

To solve the problem of difficulty in establishing the mathematical model between process parameters and surface quality in the process of engineering ceramics electro-spark machining, a neural network relational model based on rough set theory is presented. By processing attribute reduction from data sample utilizing rough set theory, defects like bulkiness of neural network structure and difficult convergence etc are aovided when input dimensions is high. A prediction model that a surface roughness varies in accordance with processing parameters in application of well structured neural network rough set is established. Study result shows that utilizing this model can precisely predict surface roughness under the given conditions with little error which proves the reliability of this model.

Experimental Study on Micro-End Lapping Mechanism

2009 ◽  
Vol 16-19 ◽  
pp. 713-717 ◽  
Author(s):  
Suo Xian Yuan ◽  
Yun Xia Hao
Keyword(s):  
Experimental Study ◽  
Surface Roughness ◽  
High Precision ◽  
Processing Parameters ◽  
Product Performance ◽  
Processing Quality ◽  
Processing Accuracy

In recent years, the demands on product performance are improving, particularly in the aviation; electronics, precision instruments and other fields, the accuracy and quality have become the most important. So lapping is getting increasingly importance with its high precision, good processing quality. In this paper, the processing parameters, which have effect on processing accuracy and efficiency in the process of planar lapping, have been analyzed emphatically. The effects on surface roughness of various processing parameters have also been investigated.

scholarly journals Product Quality Prediction for Wire Electrical Discharge Machining with Markov Transition Fields and Convolutional Long Short-Term Memory Neural Networks

2021 ◽  
Vol 11 (13) ◽  
pp. 5922
Author(s):  
Jehn-Ruey Jiang ◽  
Cheng-Tai Yen
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Short Term Memory ◽  
Electrical Discharge Machining ◽  
Quality Prediction ◽  
Machining Parameters ◽  
Short Term ◽  
Workpiece Surface ◽  
Markov Transition ◽  
Long Short Term Memory

This paper proposes a wire electrical discharge machining (WEDM) product quality prediction method, called MTF-CLSTM, to integrate the Markov transition field (MTF) and the convolutional long short-term memory (CLSTM) neural network. The proposed MTF-CLSTM method can accurately predict WEDM workpiece surface roughness right after manufacturing by collecting and analyzing static machining parameters and dynamic manufacturing conditions. The highly accurate prediction is due to the following two reasons. First, MTF can transform data into images to extract data temporal information and state transition probability information. Second, the CLSTM neural network can extract image spacial features and temporal relationship of data that are separated far apart. In short, MTF-CLSTM predicts WEDM workpiece surface roughness with the MTF model and the CLSTM neural network using static machining parameters and dynamic manufacturing conditions. MTF-CLSTM is compared with 10 related research studies in many aspects. There is only one existing method that is like MTF-CLSTM to predict WEDM workpiece surface roughness by using static machining parameters and dynamic manufacturing conditions. Experiments are conducted to evaluate MTF-CLSTM performance to show that MTF-CLSTM significantly outperforms the existing method in terms of the prediction mean absolute percentage error.

An accurate full-dimensional potential energy surface for the reaction OH + SO → H + SO2

2021 ◽  
Vol 23 (1) ◽  
pp. 487-497
Author(s):  
Jie Qin ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Invariant Polynomial ◽  
Network Approach ◽  
Neural Network Approach ◽  
Polynomial Neural Network

An accurate full-dimensional PES for the OH + SO ↔ H + SO2 reaction is developed by the permutation invariant polynomial-neural network approach.

scholarly journals Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2998
Author(s):  
Xinyong Zhang ◽  
Liwei Sun
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Bayesian Regularization ◽  
Backpropagation Neural Network ◽  
Swarm Optimization ◽  
Modal Frequency ◽  
Supporting Structure ◽  
First Order ◽  
Machine Structure

Fit of the highly nonlinear functional relationship between input variables and output response is important and challenging for the optical machine structure optimization design process. The backpropagation neural network method based on particle swarm optimization and Bayesian regularization algorithms (called BMPB) is proposed to solve this problem. A prediction model of the mass and first-order modal frequency of the supporting structure is developed using the supporting structure as an example. The first-order modal frequency is used as the constraint condition to optimize the lightweight design of the supporting structure’s mass. Results show that the prediction model has more than 99% accuracy in predicting the mass and the first-order modal frequency of the supporting structure, and converges quickly in the supporting structure’s mass-optimization process. The supporting structure results demonstrate the advantages of the method proposed in the article in terms of high accuracy and efficiency. The study in this paper provides an effective method for the optimized design of optical machine structures.

Sign in / Sign up

Export Citation Format

Share Document