Response Ant Colony Optimization of End Milling Surface Roughness

Metal cutting processes are important due to increased consumer demands for quality metal cutting related products (more precise tolerances and better product surface roughness) that has driven the metal cutting industry to continuously improve quality control of metal cutting processes. This paper presents optimum surface roughness by using milling mould aluminium alloys (AA6061-T6) with Response Ant Colony Optimization (RACO). The approach is based on Response Surface Method (RSM) and Ant Colony Optimization (ACO). The main objectives to find the optimized parameters and the most dominant variables (cutting speed, feedrate, axial depth and radial depth). The first order model indicates that the feedrate is the most significant factor affecting surface roughness

Application of DEA in the Taguchi Method for Multi-Response Optimization

Quality and productivity are two important but conflicting criteria in any machining operations. In order to ensure high productivity, extent of quality is to be compromised. It is, therefore, essential to optimize quality and productivity simultaneously. Productivity can be interpreted in terms of material removal rate in the machining operation and quality represents satisfactory yield in terms of product characteristics as desired by the customers. Dimensional accuracy, form stability, surface smoothness, fulfilment of functional requirements in prescribed area of application etc. are important quality attributes of the product. Increase in productivity results in reduction in machining time which may result in quality loss. On the contrary, an improvement in quality results in increasing machining time thereby, reducing productivity. Therefore, there is a need to optimize quality as well as productivity. Optimizing a single response may yield positively in some aspects but it may affect adversely in other aspects. The problem can be overcome if multiple objectives are optimized simultaneously. It is, therefore, required to maximize material removal rate (MRR), and to improve product quality simultaneously by selecting an appropriate (optimal) process environment. To this end, the present work deals with multi-objective optimization philosophy based on Data Envelopment Analysis (DEA) and Taguchi method applied in CNC end milling operation

Comparison of different optimization and process control procedures

This paper includes a comparison of different optimization methods, used for optimizing the cutting conditions during milling. It includes also a part of using soft computer techniques in process control procedures. Milling is a cutting procedure dependent of a number of variables. These variables are dependent from each other in consequence, if we change one variable, the others change too. PSO and GA algorithm are applied to the CNC milling program to improve cutting conditions, improve end finishing, reduce tool wear and reduce the stress on the tool, the machine and the machined part. At the end a summary will be given of pasted and future researches.Peer Reviewe

Optimization of Cutting Conditions in End Milling Process with the Approach of Particle Swarm Optimization

Milling is one of the progressive enhancements of miniaturized technologies which has wide range of application in industries and other related areas. Milling like any metal cutting operation is used with an objective of optimizing surface roughness at micro level and economic performance at macro level. In addition to surface finish, modern manufacturers do not want any compromise on the achievement of high quality, dimensional accuracy, high production rate, minimum wear on the cutting tools, cost saving and increase of the performance of the product with minimum environmental hazards. In order to optimize the surface finish, the empirical relationships between input and output variables should be established in order to predict the output. Optimization of these predictive models helps us to select appropriate input variables for achieving the best output performance. In this paper, four input variables are selected and surface roughness is taken as output variable. Particle swarm optimization technique is used for finding the optimum set of values of input variables and the results are compared with those obtained by GA optimization in the literature

Investigating the Effect of Process Parameters on Surface Roughness Indicators Produced by End Milling Operation When End Milling Solid Material AISI D3 Tool Steel

Machining parameters such as speed, feed and depth of cut play a vital role in machining the given work piece to the required shape. These have a major effect on the quality as well as on quantity of production, cost of production and production rate; hence their judicious selection assumes significance. A study of surface roughness and cutting force during end milling on this material will be quite useful. In the present work the 2 level full factorial design has been selected for development of prediction models as well as for the optimization of milling parameters for minimum surface roughness and minimum cutting forces. An effort has also been made to investigate the effect of end milling parameters on surface roughness indicators end milling of AISI D3 tool steel. This work concluded that feed is the most significant and influential machining parameter that affect the surface roughness indicator (Ra, Rq and Rt) followed by depth of cut. The cutting speed has insignificant influence on the surface roughness parameters. The mathematical models developed clearly show that surface roughness indicators increases with increasing the feed rate but decreases with increasing the cutting speed. The results of ANOVA and the confirmation runs verify that the developed mathematical models for surface roughness parameters shows excellent fit and provide predicted values of surface roughness that are close to the experimental values, with a 95 per cent confidence level. The percentage error between the predicted and experimental values of the response factor during the confirmation experiments are within 5 per cent. The study was undertaken to investigate the effect of process parameters on surface roughness indicators produced by end milling operation when end milling solid material AISI D3 tool steel. The end milling operation was carried out using various cutting parameters by using a cutting insert. Machining data of surface roughness indicators were tabulated by using surface roughness measurement apparatus. A Surface Roughness Tester (Stylus equipment) measuring instrument was used to process the measured profile data

Parametric optimization for cutting forces and material removal rate in the turning of AISI 5140

The present paper deals with the optimization of the three components of cutting forces and the Material Removal Rate (MRR) in the turning of AISI 5140 steel. The Harmonic Artificial Bee Colony Algorithm (H-ABC), which is an improved nature-inspired method, was compared with the Harmonic Bee Algorithm (HBA) and popular methods such as Taguchi’s S/N ratio and the Response Surface Methodology (RSM) in order to achieve the optimum parameters in machining applications. The experiments were performed under dry cutting conditions using three cutting speeds, three feed rates, and two depths of cuts. Quadratic regression equations were identified as the objective function for HBA to represent the relationship between the cutting parameters and responses, i.e., the cutting forces and MRR. According to the results, the RSM (72.1%) and H-ABC (64%) algorithms provide better composite desirability compared to the other techniques, namely Taguchi (43.4%) and HBA (47.2%). While the optimum parameters found by the H-ABC algorithm are better when considering cutting forces, RSM has a higher success rate for MRR. It is worth remarking that H-ABC provides an effective solution in comparison with the frequently used methods, which is promising for the optimization of the parameters in the turning of new-generation materials in the industry. There is a contradictory situation in maximizing the MRR and minimizing the cutting power simultaneously, because the affecting parameters have a reverse effect on these two response parameters. Comparing different types of methods provides a perspective in the selection of the optimum parameter design for industrial applications of the turning processes. This study stands as the first paper representing the comparative optimization approach for cutting forces and MRR

Adaptive control optimization in micro-milling of hardened steels-evaluation of optimization approaches

Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high-quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micro-manufacturing. In this paper, an adaptive control optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: (1) a monitoring sensor system composed of a dynamometer, (2) an estimation module with Artificial Neural Networks models, (3) an optimization module with evolutionary optimization algorithms, and (4) a CNC interface module. In order to operate in a nearly real-time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as particle swarm optimization (PSO), genetic algorithms (GA), and simulated annealing (SA) in terms of accuracy, precision, and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3 and 29 % in comparison with conservative and high-production strategies, respectively

A hybrid approach of anfis—artificial bee colony algorithm for intelligent modeling and optimization of plasma arc cutting on monel™ 400 alloy

This paper focusses on a hybrid approach based on genetic algorithm (GA) and an adaptive neuro fuzzy inference system (ANFIS) for modeling the correlation between plasma arc cutting (PAC) parameters and the response characteristics of machined Monel 400 alloy sheets. PAC experiments are performed based on box-behnken design methodology by considering cutting speed, gas pressure, arc current, and stand-off distance as input parameters, and surface roughness (Ra), kerf width (kw), and micro hardness (mh) as response characteristics. GA is efficaciously utilized as the training algorithm to optimize the ANFIS parameters. The training, testing errors, and statistical validation parameter results indicated that the ANFIS learned by GA outperforms in the forecasting of PAC responses compared with the results of multiple linear regression models. Besides that, to obtain the optimal combination PAC parameters, multi-response optimization was performed using a trained ANFIS network coupled with an artificial bee colony algorithm (ABC). The superlative responses, such as Ra of 1.5387 µm, kw of 1.2034 mm, and mh of 176.08, are used to forecast the optimum cutting conditions, such as a cutting speed of 2330.39 mm/min, gas pressure of 3.84 bar, arc current of 45 A, and stand-off distance of 2.01 mm, respectively. Furthermore, the ABC predicted results are validated by conducting confirmatory experiments, and it was found that the error between the predicted and the actual results are lower than 6.38%, indicating the adoptability of the proposed ABC in optimizing real-world complex machining processes

ROUGHNESS OF A MACHINED SURFACE IN MILLING OPERATION FOR FERROUS AND NON FERROUS A FUZZY LOGIC BASED MODEL TO PREDICT SURFACE MATERIALS USING HSS END MILL CUTTING TOOL

Nowadays every manufacturing and industrial industry has to focus on the manufacturing of quality products. Manufacturing of these kinds product with higher accuracy, better surface finish, lower maintenance and lower process planning and manufacturing cost are very important factor that can achieved by using non-conventional optimization techniques instead of conventional techniques. Many of non-conventional optimization techniques like Fuzzy Logic approach based technique, Genetic algorithms, Artificial Neural Network, Particle Swarm optimization, Ant colony optimization, Scatter search technique and simulated Annealing etc. are used to optimization of surface roughness. Milling is a machining operation in which workpiece is fed below the cylindrical rotating multi point cutting tool, multi point cutting tool having multiple cutting edges. On the basis of literature review, many machining parameters such as cutting speed, feed rate, depth of cut, cutting fluid pressure etc. and performance parameters as surface roughness, material removal rate, tool wear ratio, tool vibration etc., were observed for CNC milling operation. The correct selection of machining parameters is very important factor to achieve best performance measure. In this research work, spindle speed (SS), feed rate (FR) and depth of cut (DOC) are selected as machining parameters while surface roughness is considered as performance parameters to perform end milling operation on the workpiece materials of 6101 Aluminum alloy, Copper of electrolytic grade and Mild Steel 2062 by using High Speed Steel (HSS) end mill cutter of 12 mm diameter. Minimum experiment trials are designed by Taguchi based L9 (3^3) orthogonal array with the help of Minitab 17.0 software and a fuzzy logic approach based model is taken as to predict the value of surface roughness of a machined surface in 6101 aluminum alloy, Copper of Electrolytic grade and Mild Steel 2062 milling operation using HSS end mill cutter of 12 mmdiameter. Three membership functions are allocated to be connected with each input of the model. The predicted results achieved via fuzzy logic model are compared to the experimental result. The result demonstrated settlement between the fuzzy model and experimental results with the 95.618% model accuracy for 6101 aluminum alloy material, 83.849% for copper (Electrolytic grade) and 98.334% Mild Steel 2062