A predictive model on surface roughness during internal traverse grinding of small holes

2019 ◽  
Vol 103 (5-8) ◽  
pp. 2069-2077 ◽  
Author(s):  
Hao Su ◽  
Changyong Yang ◽  
Shaowu Gao ◽  
Yucan Fu ◽  
Wengfeng Ding
Keyword(s):  
Surface Roughness ◽  
Predictive Model ◽  
Internal Traverse Grinding ◽  
Traverse Grinding ◽  
Small Holes

Small Holes Fabrication on Soda Glass by Self-Elastic Polycrystalline Diamond Tool

2012 ◽  
Vol 516 ◽  
pp. 516-521
Author(s):  
Chung Chieh Cheng ◽  
Dong Yea Sheu
Keyword(s):  
Surface Roughness ◽  
Diamond Tool ◽  
Polycrystalline Diamond ◽  
The Self ◽  
High Quality ◽  
Drilling Tool ◽  
Drilling Tools ◽  
Absorbing Materials ◽  
New Type ◽  
Small Holes

This study describes a novel process to drill small holes in brittle materials such as glass, silicon and ceramic using a self-elastic polycrystalline diamond (PCD) drilling tool. In order to improve the surface roughness and reduce crack of the small holes, a new type of self-elastic PCD drilling tool equipped with vibration absorbing materials inside the housing was developed to fabricate small holes in glass in this study. The self-elastic PCD drilling tools could absorb the mechanical force by the vibration absorbing materials while the PCD tool penetrates into the small holes. Compared to conventional PCD drilling tools, the experimental results show that high-quality small holes drilled in glass can be achieved with cracking as small as 0.02mm on the outlet surface using the self-elastic PCD drilling tool.

scholarly journals Determination of the Thermal Load Distribution in Internal Traverse Grinding using a Geometric-Kinematic Simulation

Procedia CIRP ◽  
2015 ◽  
Vol 31 ◽  
pp. 322-327 ◽  
Author(s):  
S. Schumann ◽  
T. Siebrecht ◽  
P. Kersting ◽  
D. Biermann ◽  
R. Holtermann ◽  
...  
Keyword(s):  
Thermal Load ◽  
Load Distribution ◽  
Kinematic Simulation ◽  
Internal Traverse Grinding ◽  
Traverse Grinding

Modelling and simulation of Internal Traverse Grinding: bridging meso- and macro-scale simulations

2015 ◽  
Vol 9 (4) ◽  
pp. 451-463 ◽  
Author(s):  
Raphael Holtermann ◽  
Andreas Menzel ◽  
Sebastian Schumann ◽  
Dirk Biermann ◽  
Tobias Siebrecht ◽  
...  
Keyword(s):  
Modelling And Simulation ◽  
Macro Scale ◽  
Internal Traverse Grinding ◽  
Traverse Grinding

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.

scholarly journals Evolutionary Design of a System for Online Surface Roughness Measurements

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1904
Author(s):  
Valentin Koblar ◽  
Bogdan Filipič
Keyword(s):  
Machine Learning ◽  
Surface Roughness ◽  
Machine Vision ◽  
Predictive Model ◽  
Learning Algorithm ◽  
Paper Machine ◽  
Machine Learning Algorithm ◽  
Key Characteristics ◽  
Evolutionary Optimization Algorithms ◽  
Vision Algorithm

Surface roughness is one of the key characteristics of machined components as it affects the surface quality and, consequently, the lifetime of the components themselves. The most common method of measuring the surface roughness is contact profilometry. Although this method is still widely applied, it has several drawbacks, such as limited measurement speed, sensitivity to vibrations, and requirement for precise positioning of the measured samples. In this paper, machine vision, machine learning and evolutionary optimization algorithms are used to induce a model for predicting the surface roughness of automotive components. Based on the attributes extracted by a machine vision algorithm, a machine learning algorithm generates the roughness predictive model. In addition, an evolutionary algorithm is used to tune the machine vision and machine learning algorithm parameters in order to find the most accurate predictive model. The developed methodology is comparable to the existing contact measurement method with respect to accuracy, but advantageous in that it is capable of predicting the surface roughness online and in real time.

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