Abstract
The objective of this present study is to develop a system to optimize cutting insert selection and cutting parameters. The proposed approach addresses turning processes that use technical information from a tool supplier. The proposed system is based on artificial neural networks and a genetic algorithm, which define the modeling and optimization stages, respectively. For the modeling stage, two artificial neural networks are implemented to evaluate the feed rate and cutting velocity parameters. These models are defined as functions of insert features and working conditions. For the optimization problem, a genetic algorithm is implemented to search an optimal tool insert. This heuristic algorithm is evaluated using a custom objective function, which assesses the machining performance based on the given working specifications, such as the lowest power consumption, the shortest machining time or an acceptable surface roughness.
Highlights
Nowadays, there is great demand in the manufacturing industry for technologies that can deal with dynamic environments and customized products
These breakthroughs come from the implementation of automation approaches, such as adaptive control and active control. They allow companies to achieve higher operation performances [1]. Manufacturing processes, such as computer-aided process planning (CAPP), expert processes planning systems (PP), computer-aided design (CAD) and computer-aided manufacturing (CAM) are based on intelligent machining [2,3], which allow for the simulation and evaluation of variable environments
Our research considers the insert information from a tool supplier to obtain neural network models of insert before determining the optimal cutting parameters
Summary
There is great demand in the manufacturing industry for technologies that can deal with dynamic environments and customized products. Accurate performance can be defined only within a working optimal range This optimal range is evaluated by models, which generally relate the working conditions to cutting parameters and tool features. In the paper by Quiza Sardinas et al [20], a multi-objective optimization of cutting parameters in turning operations is presented This approach entails the use of an objective function based on power consumption, cutting forces and surface roughness. This algorithm is defined by a heuristic search of insert features and cutting parameters, which are evaluated by the neural network models This heuristic search is set up under a defined objective function, which is a combination of the lowest power consumption, the shortest machining time and an acceptable surface roughness. From working conditions, not relate insert features to the cutting parameter.ItItisisdefined defined by by different different working material specifications and their impact on the cutting parameters
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