In traditional machining operations, cutting parameters are usually selected prior to machining according to machining handbooks and user’s experience. However, this method tends to be conservative and sub-optimal since part accuracy and non machining failures prevail over machining process efficiency. In this paper, a comparison between traditional cutting parameter optimisation by an expert machinist and an experimental optimisation procedure based on Soft Computing methods is conducted. The proposed methodology increases the machining performance in 6.1% and improves the understanding of the machining operation through the use of Adaptive Neuro-fuzzy Inference System
AbstractIn the current trends of optimizing machining process parameters, various evolutionary or me...
In the current trends of optimizing machining process parameters, various evolutionary or meta-heuri...
In current research, artificial neural network (ANN) and Multi objective genetic algorithm (MOGA) ha...
In traditional machining operations, cutting parameters are usually selected prior to machining acc...
In this competition era, manufacturing and industrial industries are switching to the non convention...
The machining process shows ambiguous behavior and often cannot be linearly extrapolated in a wide r...
Purpose: This paper proposes a methodology for analysis and modeling of machining conditions by opti...
The purpose of this research is to investigate different milling parameters for optimization to achi...
This study presents a novel soft computing procedure based on the application of artificial neural n...
Machinability data selection is complex and cannot be easily formulated by any mathematical model to...
The optimization process is applied to the machining operations in order to provide continual improv...
AbstractThe objective of this paper is to present an open and modular expert rule-based system in or...
This paper describes a computer-aided approach for the optimum selection of cutting conditions for a...
AbstractThis paper develops a predictive and optimization model by coupling the two artificial intel...
The objective of this paper is to present an open and modular expert rule-based system in order to a...
AbstractIn the current trends of optimizing machining process parameters, various evolutionary or me...
In the current trends of optimizing machining process parameters, various evolutionary or meta-heuri...
In current research, artificial neural network (ANN) and Multi objective genetic algorithm (MOGA) ha...
In traditional machining operations, cutting parameters are usually selected prior to machining acc...
In this competition era, manufacturing and industrial industries are switching to the non convention...
The machining process shows ambiguous behavior and often cannot be linearly extrapolated in a wide r...
Purpose: This paper proposes a methodology for analysis and modeling of machining conditions by opti...
The purpose of this research is to investigate different milling parameters for optimization to achi...
This study presents a novel soft computing procedure based on the application of artificial neural n...
Machinability data selection is complex and cannot be easily formulated by any mathematical model to...
The optimization process is applied to the machining operations in order to provide continual improv...
AbstractThe objective of this paper is to present an open and modular expert rule-based system in or...
This paper describes a computer-aided approach for the optimum selection of cutting conditions for a...
AbstractThis paper develops a predictive and optimization model by coupling the two artificial intel...
The objective of this paper is to present an open and modular expert rule-based system in order to a...
AbstractIn the current trends of optimizing machining process parameters, various evolutionary or me...
In the current trends of optimizing machining process parameters, various evolutionary or meta-heuri...
In current research, artificial neural network (ANN) and Multi objective genetic algorithm (MOGA) ha...