scholarly journals A Comparative Analysis of Surface Roughness Prediction Models Using Soft Computing Techniques

2020 ◽  
pp. 149-155
Author(s):  
Girish Kant Garg ◽  
Shailendra Pawanr ◽  
Kuldip Singh Sangwan
Keyword(s):  
Surface Roughness ◽  
Comparative Analysis ◽  
Soft Computing ◽  
Prediction Models ◽  
Surface Roughness Prediction ◽  
Soft Computing Techniques ◽  
Roughness Prediction

scholarly journals Transfer Learning Based Surface Roughness Prediction Integrating Tool Wear Under Variable Cutting Parameters

2021 ◽  
Author(s):  
Yahui Wang ◽  
Lianyu Zheng ◽  
Yiwei Wang ◽  
Jian Zhou ◽  
Fei Tao
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Surface Quality ◽  
Transfer Learning ◽  
Prediction Models ◽  
Short Term Memory ◽  
Cutting Parameters ◽  
Practical Significance ◽  
Surface Roughness Prediction ◽  
Roughness Prediction

Abstract The monitoring of surface quality in machining is of great practical significance for the reliability and life of high-value products such as rocket, spacecraft and aircraft, particularly for their assembly interfaces of these products. Surface roughness is an important metric to evaluate the surface quality. The current research of online surface roughness prediction has the following limitations. The effect of the varying tool wear on the surface roughness is rarely considered in machining. In addition, the deteriorating trend of surface roughness and tool wear is different under variable cutting parameters. Prediction models trained under one set of cutting parameters fail when cutting parameters change. This paper proposes a surface roughness prediction method considering the varying tool wear under variable cutting parameters. A stacked autoencoder and long short-term memory network (SAE-LSTM) is designed as the basic surface roughness prediction model that uses tool wear conditions and sensor signals as the input. The transfer learning strategy is applied on SAE-LSTM such that the surface roughness online prediction under variable cutting parameters can be realized. Machining experiments for the assembly interface (Ti6Al4V material) of the aircraft’s vertical tail are conducted and the monitoring data are used to validate the proposed method. Ablations studies are carried out to evaluate the key modules of the proposed model. The experimental results show that the proposed method outperforms other models and well track the true surface roughness over time.

scholarly journals Surface roughness prediction in milling based on tool displacements

2011 ◽  
Vol 13 (2) ◽  
pp. 133-140 ◽  
Author(s):  
Jean Philippe Costes ◽  
Vincent Moreau
Keyword(s):  
Surface Roughness ◽  
Surface Roughness Prediction ◽  
Roughness Prediction

Comparative study for deriving stagedischarge– sediment concentration relationships using soft computing techniques

2021 ◽  
Vol 2 (104) ◽  
pp. 57-76
Author(s):  
P. Sihag ◽  
M.R. Sadikhani ◽  
V. Vambol ◽  
S. Vambol ◽  
A.K. Prabhakar ◽  
...  
Keyword(s):  
Random Forest ◽  
Soft Computing ◽  
Sediment Load ◽  
Prediction Models ◽  
Absolute Error ◽  
Load Prediction ◽  
Statistical Parameters ◽  
Hydro Power ◽  
Input Combination ◽  
Soft Computing Techniques

Purpose: Knowledge of sediment load carried by any river is essential for designing and planning of hydro power and irrigation projects. So the aim of this study is to develop and evaluating the best soft-computing-based model with M5P and Random Forest regressionbased techniques for computation of sediment using datasets of daily discharge, daily gauge and sediment load at the Champua gauging site of the Upper Baitarani river basin of India. Design/methodology/approach: Last few decades, the soft computing techniques based models have been successfully used in water resources modelling and estimation. In this study, the potential of tree based models are examined by developing and comparing sediment load prediction models, based on M5P tree and Random forest regression (RF). Several M5P and RF based models have been applied to a gauging site of the Baitarani River at Odisha, India. To evaluate the performance of the selected M5P and RF-based models, three most popular statistical parameters are selected such as coefficient of correlation, root mean square error and mean absolute error. Findings: A comparison of the results suggested that RF-based model could be applied successfully for the prediction of sediment load concentration with a relatively higher magnitude of prediction accuracy. In RF-based models Qt, Q(t-1), Q(t-2), S(t-1), S(t-2), Ht and H(t-1) combination based M10 model work superior than other combination based models. Another major outcome of this investigation is Qt, Q(t-1) and S(t-1) based model M4 works better than other input combination based models using M5P technique. The optimum input combination is Qt, Q(t-1) and S(t-1) for the prediction of sediment load concentration of the Baitarani River at Odisha, India. Research limitations/implications: The developed models were tested for Baitarani River at Odisha, India.

Modeling and Analysis of Surface Roughness with Statistical and Soft Computing Approach

2021 ◽  
Vol 106 ◽  
pp. 109-115
Author(s):  
L.B. Abhang ◽  
M. Hameedullah
Keyword(s):  
Surface Roughness ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Prediction Models ◽  
Tool Geometry ◽  
Modeling And Analysis ◽  
Empirical Prediction ◽  
Lathe Machine ◽  
Carbide Inserts ◽  
Computing Approach

The objective of this study focuses on developing empirical prediction models using response regression analysis and fuzzy-logic. These models latter can be used to predict surface roughness according to technological variables. The values of surface roughness produced by these models are compared with experimental results. Experimental investigation has been carried out by using scientific composite factorial design on precision lathe machine with tungsten carbide inserts. Surface roughness measured at end of each experimental trial (three times), to get the effect of machining conditions and tool geometry on the surface finish values. Research showed that soft computing fuzzy logic model developed produces smaller error and has satisfactory results as compared to response regression model during machining.

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