Neural Network and Fuzzy Logic based prediction of Surface Roughness and MRR in Cylindrical Grinding Process

2017 ◽  
Vol 4 (8) ◽  
pp. 8134-8141 ◽  
Author(s):  
N. Sudheer Kumar Varma ◽  
S. Rajesh ◽  
K. Sita Rama Raju ◽  
V.V. Murali Krishnam Raju
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Fuzzy Logic ◽  
Grinding Process ◽  
Cylindrical Grinding

A New Dynamic State Space Model for the Cylindrical Grinding Process

2005 ◽  
Author(s):  
Cheol W. Lee
Keyword(s):  
Surface Roughness ◽  
State Space ◽  
State Space Model ◽  
Dynamic State ◽  
Grinding Process ◽  
Cylindrical Grinding ◽  
Space Model ◽  
Proposed Model ◽  
Grinding Power ◽  
Actual Part

A new dynamic state space model is proposed for the in-process estimation and prediction of part qualities in the plunge cylindrical grinding process. A through review on various grinding models in literature reveals a hidden dynamic relationship among the grinding conditions, the grinding power, the surface roughness, and the part size due to the machine dynamics and the wheel wear, based on which a nonlinear state space equation is derived. After the model parameters are determined according to the reported values in literature, several simulations are run to verify that the model makes good physical sense. Since some of the output variables, such as the actual part size, may or may not be measured in industry applications, the observability is tested for different sets of outputs in order to see how each set of on-line sensors affects the observability of the model. The proposed model opens a new way of estimating the part qualities such as the surface roughness and the actual part size based on application of the state estimation algorithm to the measured outputs such as the grinding power. In addition, a long term prediction of the part qualities in batch grinding processes would be realized by simulation of the proposed model. Possible applications to monitoring and control of grinding processes are discussed along with several technical challenges lying ahead.

Adaptive Fuzzy Neural Network Control System in Cylindrical Grinding Process

2010 ◽  
Vol 426-427 ◽  
pp. 220-224
Author(s):  
X.M. Li ◽  
Ning Ding
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Network Control ◽  
Grinding Process ◽  
Neural Network Control ◽  
Network Control System ◽  
Cylindrical Grinding ◽  
Adaptive Fuzzy Neural Network ◽  
Fuzzy Neural

An adaptive fuzzy neural network control system in cylindrical grinding process was proposed. In this system, the initial cylindrical grinding parameters were decided by the expert system based on fuzzy neural network. Multi-feed and setting overshoot optimization methods were also adopted during the grinding process, and a human machine cooperation system (composed of human and two fuzzy – neural networks) could revise the process parameters in real-time. The experiment of the cylindrical grinding was implemented. The results showed that this control system was valid, and could greatly improve the cylindrical grinding quality and machining efficiency.

scholarly journals Producing Optimum Quality Grinding Spindle using Hardened AISI 4340 Steel through a Cylindrical Grinding Process

2019 ◽  
Vol 9 (2) ◽  
pp. 447-454
Keyword(s):  
Surface Roughness ◽  
Grinding Process ◽  
Rough Machining ◽  
Aisi 4340 Steel ◽  
Cylindrical Grinding ◽  
4340 Steel ◽  
Grinding Machine ◽  
Experimental Runs ◽  
The Ideal ◽  
Aisi 4340

The intent of this study is to produce optimum quality grinding spindle using hardened AISI 4340 steel through the cylindrical grinding process. Primarily the AISI 4340 steel specimens are cut according to the product specification and subjected to rough machining. Then the steel specimens are subjected to a heat-treatment process to enhance the mechanical property hardness so that the specimen becomes wear-resistant. The experimental runs are planned depending on Taguchi’s L27(37) array and conducted in a cylindrical grinding machine (Toyoda G32 cylindrical grinding machine). The surface roughness of the machined specimens is measured using a calibrated surface roughness tester. A prediction model is created through regression analysis for the outcome. The significance of the selected grinding factors and their levels on surface roughness is found by analysis of variance (ANOVA) and F-test and finally. An affirmation test is directed to produce the ideal components.

scholarly journals Comparison of Fuzzy Logic and Neural Network for Modelling Surface Roughness in EDM

2014 ◽  
Vol 3 (3) ◽  
pp. 69-78 ◽  
Author(s):  
Dragan Rodic ◽  
Marin Gostimirovic ◽  
Pavel Kovac ◽  
Miroslav Radovanovic ◽  
Borislav Savkovic
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Fuzzy Logic

scholarly journals The Effects of Cylindrical Grinding Factors in the Production of Grinding Spindle using Hardened EN353 Steel

2019 ◽  
Vol 8 (4) ◽  
pp. 10640-10649
Keyword(s):  
Heat Treatment ◽  
Surface Roughness ◽  
Treatment Process ◽  
Grinding Process ◽  
Heat Treatment Process ◽  
Rough Machining ◽  
Cylindrical Grinding ◽  
Grinding Machine ◽  
Experimental Runs ◽  
Product Specification

The objective of this study is to produce the best possible grinding spindle using hardened EN 353 steel through the cylindrical grinding process. Primarily the EN 353 steel specimens are cut according to the product specification and subjected to rough machining. Then the steel specimens are subjected to a heat-treatment process to enhance the mechanical property hardness so that the specimen becomes wear-resistant. The experimental runs are planned based on Taguchi’s L27(37 ) array and conducted in a cylindrical grinding machine (Toyoda G32 cylindrical grinding machine). The surface roughness of the machined specimens is measured using a calibrated surface roughness tester. A prediction model is created through regression analysis for the outcome. The significance of the selected grinding factors and their levels on surface roughness is found by analysis of variance (ANOVA) and F-test and finally, a affirmation test is conducted to confirm the optimum factors.

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