A computer-aided graphical technique for the optimization of machining conditions

Kılıç, S. Engin; Çoğun, Can; Şen, Deniz

doi:10.1016/0166-3615(93)90099-m

Cited by 22 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Initial research in machining process optimization focused on single pass operations (Taylor, 1906;Gilbert, 1950;Petropoulos, 1973;Abuelnaga and El-Dardiry, 1984). The mathematical programming techniques like linear programming (Ermer and Patel, 1974), graphical methods (Kiliç et al, 1993) and geometric programming (Ermer, 1971;Petropoulos, 1973) had been used to solve optimization problems of machining parameters in multi-pass turnings. But, these methods of optimization do not fare well over a broad spectrum of problem domains.…”

Section: Introductionmentioning

confidence: 99%

Multi Pass Optimization of Cutting Conditions by Using the Genetic Algorithms

Djenane¹,

Djari²,

Benbouta³

et al. 2016

RJASET

View full text Add to dashboard Cite

Production of high-quality products with lower cost and shorter time is an important challenge to face of increasing global competition. Determination of optimal cutting parameters is one of the most important elements in any planning process of metal parts. In this study we present a multi-optimization technique based on genetic algorithms and dynamic programming, to search for optimal cuttings parameters such as cutting depth, feed rate and cutting speed of multi-pass turning processes. Two conflicting objectives, the production cost and operation time are simultaneously optimize under a set of practical of machining constraints. The proposed model deals with multi-pass turning processes in which the cutting operations are divided into multi-pass rough machining and finish machining. Results obtained from Genetic algorithms method are used to define the optimum number of machining passes by dynamic programming; such technique helps us in the decision making process. An example is presented to develop the procedure of this technique.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi Pass Optimization of Cutting Conditions by Using the Genetic Algorithms

Djenane¹,

Djari²,

Benbouta³

et al. 2016

RJASET

View full text Add to dashboard Cite

show abstract

Modeling and Optimization of Machining Processes

Rao

2010

Springer Series in Advanced Manufacturing

View full text Add to dashboard Cite

Statistical and Computational Techniques in Manufacturing

Davim¹

2012

View full text Add to dashboard Cite

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.

show abstract

A computer-aided graphical technique for the optimization of machining conditions

Cited by 22 publications

References 5 publications

Multi Pass Optimization of Cutting Conditions by Using the Genetic Algorithms

Multi Pass Optimization of Cutting Conditions by Using the Genetic Algorithms

Modeling and Optimization of Machining Processes

Statistical and Computational Techniques in Manufacturing

Contact Info

Product

Resources

About