scholarly journals Comparison of artificial neural network, fuzzy logic and genetic algorithm for cutting temperature and surface roughness prediction during the face milling process

2020 ◽  
Vol 15 (2) ◽  
pp. 137-150
Author(s):  
B. Savkovic ◽  
P. Kovac ◽  
D. Rodic ◽  
B. Strbac ◽  
S. Klancnik
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Cutting Temperature ◽  
Milling Process ◽  
Face Milling ◽  
The Face ◽  
Roughness Prediction

scholarly journals Research and Optimization of Surface Roughness in Milling of SLM Semi-Finished Parts Manufactured by Using the Different Laser Scanning Speed

Materials ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 9 ◽  
Author(s):  
Andrzej Matras
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Laser Scanning ◽  
Cutting Tool ◽  
Scanning Speed ◽  
Milling Process ◽  
Face Milling ◽  
Machining Parameters ◽  
The Face ◽  
Laser Scanning Speed

The paper studies the potential to improve the surface roughness in parts manufactured in the Selective Laser Melting (SLM) process by using additional milling. The studied process was machining of samples made of the AlSi10Mg alloy powder. The simultaneous impacts of the laser scanning speed of the SLM process and the machining parameters of the milling process (such as the feed rate and milling width) on the surface roughness were analyzed. A mathematical model was created as a basis for optimizing the parameters of the studied processes and for selecting the sets of optimum solutions. As a result of the research, surface with low roughness (Ra = 0.14 μm, Rz = 1.1 μm) was obtained after the face milling. The performed milling allowed to reduce more than 20-fold the roughness of the SLM sample surfaces. The feed rate and the cutting width increase resulted in the surface roughness deterioration. Some milled surfaces were damaged by the chip adjoining to the rake face of the cutting tool back tooth.

scholarly journals Surface Roughness Optimization of Polyamide-6/Nanoclay Nanocomposites Using Artificial Neural Network: Genetic Algorithm Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Mehdi Moghri ◽  
Milos Madic ◽  
Mostafa Omidi ◽  
Masoud Farahnakian
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Nonlinear Function ◽  
Polyamide 6 ◽  
Machining Performance ◽  
Milling Parameters ◽  
Artificial Neural

During the past decade, polymer nanocomposites attracted considerable investment in research and development worldwide. One of the key factors that affect the quality of polymer nanocomposite products in machining is surface roughness. To obtain high quality products and reduce machining costs it is very important to determine the optimal machining conditions so as to achieve enhanced machining performance. The objective of this paper is to develop a predictive model using a combined design of experiments and artificial intelligence approach for optimization of surface roughness in milling of polyamide-6 (PA-6) nanocomposites. A surface roughness predictive model was developed in terms of milling parameters (spindle speed and feed rate) and nanoclay (NC) content using artificial neural network (ANN). As the present study deals with relatively small number of data obtained from full factorial design, application of genetic algorithm (GA) for ANN training is thought to be an appropriate approach for the purpose of developing accurate and robust ANN model. In the optimization phase, a GA is considered in conjunction with the explicit nonlinear function derived from the ANN to determine the optimal milling parameters for minimization of surface roughness for each PA-6 nanocomposite.

Surface Roughness Prediction in Turning of Free Machining Steel 1215 by Artificial Neural Network

2011 ◽  
Vol 188 ◽  
pp. 535-541
Author(s):  
Xiao Jiang Cai ◽  
Z.Q. Liu ◽  
Q.C. Wang ◽  
Shu Han ◽  
Qing Long An ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Mean Squared Error ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Ann Model ◽  
Roughness Prediction ◽  
Artificial Neural

Surface roughness is a significant aspect of the surface integrity concept. It is efficient to predict the surface roughness in advance by a prediction model. In this study, artificial neural network is used to model the surface roughness in turning of free machining steel 1215. The inputs considered in the prediction ANN model were cutting speed, feed rate and depth of cut, and the output was Ra. Several feed-forward neural networks with different architectures were compared in terms of prediction accuracy, and then the best prediction model, a 3-4-1-1 ANN was capable of predicting Ra with a mean squared error 5.46%, was presented.

scholarly journals The influence of technological parameters on surface roughness during turning and roughness prediction using artificial neutral networks

Mechanik ◽  
2018 ◽  
Vol 91 (10) ◽  
pp. 898-900 ◽  
Author(s):  
Ireneusz Zagórski ◽  
Monika Kulisz ◽  
Tomasz Warda
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Roughness Parameter ◽  
Cutting Speed ◽  
Roughness Parameters ◽  
Technological Parameters ◽  
Surface Roughness Parameters ◽  
Roughness Prediction

The purpose of this investigation was to determine whether and to what extent the technological parameters of turning (feed, cutting speed) affect selected surface roughness parameters of aluminum alloy EN-AW 7075 (AlZn5.5MgCu). The principal findings indicate a significant impact of feed and show on the surface roughness and simultaneously show that cutting speed has no effect on the value of surface roughness parameters under investigation. An artificial neural network was employed to evaluate the prediction of surface roughness parameter Rz in turning.

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