N. Baskar

Application of Particle Swarm Optimization technique for achieving desired milled surface roughness in minimum machining time

Face milling is a widely used machining operation to produce various components. The finished component depends not only on the dimensional accuracy but also on the surface finish. The present method of selection of machining parameters by trial and error, previous work experience of the process planner and machining hand books are time consuming and very tedious. There is a need to develop a technique that could able to find the optimal machining parameters for the required surface roughness in machining. In this work, experimental investigations are carried out on aluminium material to study the effect of machining parameters such as cutting speed, feed, and depth of cut on the surface roughness and to obtain the desired surface roughness on face milling process. Mathematical model has been developed for surface roughness prediction using Particle Swarm Optimization (PSO) on the basis of experimental results. The model developed for optimization has been validated by confirmation experiments. Physical constraints for both experiment and theoretical approach are the proposed machining parameters and surface roughness. It has been found that the predicted roughness using PSO is in good agreement with the actual roughness.

Optimization of Surface Grinding Operations Using Particle Swarm Optimization Technique

The development of comprehensive grinding process models and computer-aided manufacturing provides a basis for realizing grinding parameter optimization. The variables affecting the economics of machining operations are numerous and include machine tool capacity, required workpiece geometry, cutting conditions such as speed, feed, and depth of cut, and many others. Approximate determination of the cutting conditions not only increases the production cost, but also diminishes the product quality. in this paper a new evolutionary computation technique, particle swarm optimization, is developed to optimize the grinding process parameters such as wheel speed, workpiece speed, depth of dressing, and lead of dressing, simultaneously subjected to a comprehensive set of process constraints, with an objective of minimizing the production cost and maximizing the production rate per workpiece, besides obtaining the finest possible surface finish. Optimal values of the machining conditions obtained by particle swarm optimization are compared with the results of genetic algorithm and quadratic programming techniques.

Implementation of supervised statistical data mining algorithm for single machine scheduling

Purpose – Machine scheduling plays an important role in most manufacturing industries and has received a great amount of attention from operation researchers. Production scheduling is concerned with the allocation of resources and the sequencing of tasks to produce goods and services. Dispatching rules help in the identification of efficient or optimized scheduling sequences. The purpose of this paper is to consider a data mining‐based approach to discover previously unknown priority dispatching rules for the single machine scheduling problem.Design/methodology/approach – In this work, the supervised statistical data mining algorithm, namely Bayesian, is implemented for the single machine scheduling problem. Data mining techniques are used to find hidden patterns and rules through large amounts of structured or unstructured data. The constructed training set is analyzed using Bayesian method and an efficient production schedule is proposed for machine scheduling.Findings – After integration of naive Bayes...

The Simple Genetic Algorithm Approach for Optimization of Nesting of Sheet Metal Parts in Blanking Operation

The two-dimensional (2D) cutting stock is a common problem arising in the sheet metal industries, lock industries, textile industries, etc. Here, the problem is to reduce the wastage in order to increase the profit. This problem is also called as the general 2D problem or NP hard problems. The choice of chromosome representation in genetic algorithm (GA) depends on the variables of the optimization problem being solved. The main objectives of the work are the maximum utilization of part in the sheet and also minimizing the wastage.

Comparison of Response Surface Methodology and Taguchi Analysis for Determining Appropriate Drilling Parameters of Duplex 2205

The machinability studies of newer material have been emerging technology in the present decade because of the lacuna in area. Duplex stainless steel is widely used in chemical industries and structural components in coastal areas due to its higher corrosion resisting property. Assembling and processing of engineering components needs machining operation like turning, milling, drilling and grinding. From these drilling operation is most important manufacturing process for assembling the components. The main aim of this work is to investigate influence of process parameters on drilling operation in Duplex 2205. Here considered input parameters are spindle speed and feed rate, the responses considered are thrust force and torque. For investigating the drilling parameter influence Response Surface Methodology (RSM) and Taguchi analysis were used. The analysis of variance (ANOVA) table in RSM and rank table in Taguchi analysis are used to identify the parameter significance. Regression analysis is used to frame the empirical models. Further the developed empirical models are validated with experimental data proves its adequacy. Finally comparison made between RSM and Taguchi analysis discloses, RSM be the best among those analyses.

Investigation of optimal machining parameters for turning operation using intelligent techniques

Determination of optimum machining parameters for turning operation has been investigated using non-traditional optimisation techniques and compared the results with Nelder-Mead simplex (NMS) method. The combination minimum production time and cost is considered as the objective function. The optimum machining parameters such cutting speed, feed, depth of cut are determined by simulated annealing algorithm, genetic algorithm, particle swarm optimisation, memetic algorithm and hybrid algorithm. Physical constraints are speed, feed, depth of cut, power limitation, surface roughness, cutting force and temperature constraints.

Effect of process parameters on the electrical discharge machining of aluminum metal matrix composites through a response surface methodology approach

Abstract This work presents the multiobjective optimization of machining parameters during the electrical discharge machining (EDM) of aluminum (Al)-silicon carbide (SiC) metal matrix composites (MMC). The process parameters considered were current, pulse on-time, dielectric flushing pressure, and SiC particles. A copper rod was used as an electrode. An Al-SiC MMC with Al 6061 as matrix and SiC particles having three different sizes (i.e., 15, 25, and 40 μm) were used as workpieces. The experiments were planned using design of experiments through response surface methodology (RSM). The mathematical models were developed to predict the better performance measures such as the material removal rate (MRR), electrode wear rate (EWR), surface roughness (SR), and cylindricity (CY). The desirability approach in RSM was performed for optimization. It was found that the MRR increases with increasing peak current, pulse on-time, flushing pressure, and particle size. The EDM parameters are to be analyzed for the MRR, EWR, SR, and CY. The best one is proposed for validation.

An Application Traditional and Nontraditional Approach on Lock Industry for Optimization of Cutting Layout

Engineering becomes more advanced and the business in the industrial world becomes more competitive. The method of implementing optimization technique is essential. The nesting of two dimensional shapes for the press tool design is general optimization problem. In mass production industries; the small inefficiencies will have the huge wastage. This is also known as two-dimensional Cutting Stock Problem (CSP). The aim is to minimize wastage there by increasing the profit by means of arranging the parts into the master sheet. The parts are to be arranged in the sheet in a acceptable position. It needs a various algorithm for placing the parts into the sheets like Heuristic, Meta heuristic, Bottom left heuristics etc.

Optimization of Drilling Process Parameters for Material Removal Rate and Surface Roughness on Titanium Alloy using Response Surface Methodology and Fire Fly Algorithm

Material removal rate and surface roughness are the important aspects in manufacturing of components. In recent daysvarious newer materials is introduced in power generation, chemical processing, petroleum, automotive industries and armor applications due to their inherent properties. The application of titanium alloy is increased more rapidly than before this decade, since it can decrease lifespan costs through a broad range of equipment and processes. In addition, titanium is one of the newer materials and its machining parameters on drilling operation are to be investigated owing to its poor machinability. In the present work, the influences of spindle speed and feed rate on metal removal rate and surface roughness are studied. To support this objective, Response Surface Methodology (RSM) was used to make a relationship between drilling parameter and responses. Subsequently a new meta-heuristic Fire Fly Algorithm (FFA) was used to optimize the drilling parameters. The developed empirical model is solved by FFA for maximizing the volume of material removed and minimizing the surface roughness of titanium alloy.

Experimental Investigations on EDM Process for Optimum Cylindricity and SR through less Machining Time for Al6061/SiC Composites

Electrical Discharge Machining (EDM) is used for machining of hard materials with complex shapes. It is also having wide applications in machining of parts that are used in aero space industries. EDM involves high cost and consumes long time. The machining parameters of EDM process affect its performance measures. Achieving the better performance measures, particularly less machining time with good surface finish and good form tolerances are essential. In this work, Al 6061-SiC composites was chosen as work piece. Machining time (MT), Cylindricity (CY), and Surface Roughness (SR) are the performances considered in this work. The experiments were conducted with L18 OA, with different machining parameters namely current, pulse on time, pulse off time and flushing pressure. Three types of copper electrodes namely solid electrodes, bundled wire electrodes and multiple hole electrodes were used for ED Machining. The performances of solid electrodes, bundled wire electrodes and multiple hole electrode are compared. It was found that the performance of multiple hole electrode is superior compared to other two types of electrodes.