Positional, geometrical, and thermal errors compensation by tool path modification using three methods of regression, neural networks, and fuzzy logic

2012 ◽  
Vol 65 (9-12) ◽  
pp. 1635-1649 ◽  
Author(s):  
Sina Eskandari ◽  
Behrooz Arezoo ◽  
Amir Abdullah
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Tool Path ◽  
Thermal Errors ◽  
Path Modification ◽  
Tool Path Modification

A Tool Path Modification Approach to Cutting Engagement Regulation for the Improvement of Machining Accuracy in 2D Milling With a Straight End Mill

2007 ◽  
Vol 129 (6) ◽  
pp. 1069-1079 ◽  
Author(s):  
M. Sharif Uddin ◽  
Soichi Ibaraki ◽  
Atsushi Matsubara ◽  
Susumu Nishida ◽  
Yoshiaki Kakino
Keyword(s):  
Cutting Force ◽  
Case Studies ◽  
Tool Path ◽  
Free Form ◽  
Geometric Accuracy ◽  
End Mill ◽  
Workpiece Surface ◽  
Engagement Angle ◽  
Path Modification ◽  
Tool Path Modification

In two-dimensional (2D) free-form contour machining by using a straight (flat) end mill, conventional contour parallel paths offer varying cutting engagement with workpiece, which inevitably causes the variation in cutting loads on the tool, resulting in geometric inaccuracy of the machined workpiece surface. This paper presents an algorithm to generate a new offset tool path, such that the cutting engagement is regulated at a desired level over the finishing path. The key idea of the proposed algorithm is that the semi-finish path, the path prior to the finishing path, is modified such that the workpiece surface generated by the semi-finish path gives the desired engagement angle over the finishing path. The expectation with the proposed algorithm is that by regulating the cutting engagement angle along the tool path trajectory, the cutting force can be controlled at any desirable value, which will potentially reduce variation of tool deflection, thus improving geometric accuracy of machined workpiece. In this study, two case studies for 2D contiguous end milling operations with a straight end mill are shown to demonstrate the capability of the proposed algorithm for tool path modification to regulate the cutting engagement. Machining results obtained in both case studies reveal far reduced variation of cutting force, and thus, the improved geometric accuracy of the machined workpiece contour.

Thermal Errors Modeling of a CNC Machine’s Axis Using Neural Network and Fuzzy Logic

2011 ◽  
Vol 110-116 ◽  
pp. 2976-2982 ◽  
Author(s):  
Sina Eskandari ◽  
Behrooz Arezoo ◽  
Amir Abdullah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Previous Method ◽  
Data Set ◽  
Inference System ◽  
Thermal Errors ◽  
Squared Error

Thermal errors of CNC machines have significant effects on precision of a workpiece. One of the approaches to reduce these errors is modeling and on-line compensating them. In this study, thermal errors of an axis of the machine are modeled by means of artificial neural networks along with fuzzy logic. Models are created using experimental data. In neural networks modeling, MLP type which has 2 hidden layers is chosen and it is trained by backpropagation algorithm. Finally, the model is validated with the aid of calculating mean squared error and correlation coefficients between outputs of the model and a checking data set. On the other hand, an adaptive neuro-fuzzy inference system is utilized in fuzzy modeling which uses neural network to develop membership functions as fuzzifiers and defuzzifiers. This network is trained by hybrid algorithm. At the end, model validation is done by mean squared error like previous method. The results show that the errors of both modeling techniques are acceptable and models can predict thermal errors reliably.

scholarly journals Machining Time Reduction by Tool Path Modification to Eliminate Air Cutting Motion for End Milling Operation

2020 ◽  
Vol 14 (3) ◽  
pp. 459-466 ◽  
Author(s):  
Isamu Nishida ◽  
◽  
Keiichi Shirase
Keyword(s):  
Tool Path ◽  
End Milling ◽  
Axial Direction ◽  
Contour Line ◽  
Product Model ◽  
Machining Time ◽  
High Productivity ◽  
Product Surface ◽  
Path Modification ◽  
Tool Path Modification

A method to uniquely calculate the tool path and to modify the tool path during air cutting motion to reduce the machining time is proposed. This study presents a contour line model, in which the product model is minutely divided on a plane along an axial direction, and the contour line of the cross-section of the product is superimposed. A method is then proposed to calculate the tool position according to the degree of interference between the product surface and the tool. Furthermore, this study proposes a technique to reduce the machining time by tool path modification during air cutting motion. This is determined by the geometric relationship between the product surface and the tool, and not based on cutting simulations. A cutting experiment was conducted to validate the effectiveness of the proposed method. Based on the results, it was confirmed that the difference in machining time between the tool path with modification and the tool path without modification was large. Moreover, the machining time was significantly reduced by the tool path modification. The results showed that the proposed method has good potential to perform customized manufacturing, and to realize both high productivity and reliability in machining operation.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

Withdrawn: Diabetes Mellitus Prognosis Using Fuzzy Logic and Neural Networks Case Study: Alexandria Vascular Center (AVC)

2019 ◽  
Vol 13 ◽  
Author(s):  
Abeer A. Amer ◽  
Soha M. Ismail
Keyword(s):  
Diabetes Mellitus ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Computer Science ◽  
Recent Advances ◽  
Vascular Center ◽  
Copyright Permission ◽  
Legal Right

The following article has been withdrawn on the request of the author of the journal Recent Advances in Computer Science and Communications (Recent Patents on Computer Science): Title: Diabetes Mellitus Prognosis Using Fuzzy Logic and Neural Networks Case Study: Alexandria Vascular Center (AVC) Authors: Abeer A. Amer and Soha M. Ismail* Bentham Science apologizes to the readers of the journal for any inconvenience this may cause BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

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