17-08-2012, 01:56 PM
Optimization algorithms
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INTRODUCTION
OPTIMIZATION
Optimization algorithms are becoming increasingly popular in engineering activities, primarily because of the availability and affordability of high-speed computers. They are extensively used in those engineering problems where the emphasis is on maximizing or minimizing of a certain goal. For example, optimization algorithms are routinely used in aerospace design activities to minimize the overall weight, simply because every element or component adds to the overall weight of the aircraft. Chemical engineers on the other hand are interested in designing or operating a process plant for an optimum rate of production. Mechanical engineers design mechanical components for the purpose of achieving either a minimum manufacturing cost or maximum rate of production.
Production engineers are interested in designing optimum schedules of various machining operations to minimize the idle time of machines and the overall completion time. Civil engineers are involved in designing buildings, dams and other structures in order to achieve a minimum overall cost or maximum safety or both. Electronics engineers are interested in designing communication networks so as to achieve minimum time for communication from one node to another. Thus, the ultimate aim of the optimization is to improve an existing process that meets the given requirements and satisfies all the restrictions/constraints placed on it. This is called the optimum process. Optimization of cutting parameters in machining processes has been an important area of research. Starting from Taylor, there has been an increasing interest in machining process optimization. Selection of optimum cutting conditions can lead to a substantial reduction in the operating costs. Commonly used objective functions are the minimization of production cost, the maximization of production rate and the maximization of profit rate. Abuelnaga and El-Dardiry discussed a number of traditional optimization methods and highlighted the relative advantages and disadvantages of the methods for solving the problems of machining economics. These objectives can be represented in terms of the machining parameters such as cutting speed, feed, depth of cut and the number of passes.
During selection of the parameters, care must be taken to ensure that the essential constraints are satisfied. Cutting force, power, surface finish and tool life are some of the commonly considered constraints. Initial research in machining process optimization focused on single pass operations.
However, due to restrictions on the amount of material that can be removed in a single pass, multi-pass operations are required. Machining is done in two stages in multipass operations. Majority of material removal takes place in a series of rough passes in the first stage. In the next stage a small amount of material is removed by a single finish pass. Typically, two distinct approaches have been used for optimization of multi-pass operations. One is the equal depth of strategy in which the depth of cut in all the rough. Many researchers have dealt with the problem of machining parameters for turning process. Relatively less research has been done in optimization of multi-point cutting operations such as milling. Recently there have been quite a few reports in literature on optimization of milling operations. Sonmez et al. used dynamic programming to obtain the optimum number of passes and geometric programming for obtaining the optimum cutting conditions. Their work showed that the performance of multi-pass milling operations is better than that of single pass operations. Shunmugam et al. minimized the production cost for rough passes and finish passes in two stages. In the first stage, separate minimum costs for an individual rough and finish pass were determined and tabulated for various fixed values of depth of cut selected from a series of depths. In the second stage, genetic algorithm (HA) was used for finding the optimum number of rough passes and allocation of total stock in each of the rough passes and the final pass to achieve a minimum total production cost. An and Chen used a similar technique for first stage and integer programming for the second stage for solving the same problem. The two stage strategy for optimization leads to a large number of computations and increases the computational time when there are a large number of passes to consider. Wang et al. have presented a new methodology involving the use of HA for the selection of cutting conditions for multi-pass face milling operations based on a comprehensive criterion of integrating the contributing effects of all major machining performance measures. They first used Taguchi method for design of experiments to predict machining performance measures and then utilized genetic algorithms to optimize the cutting conditions. In their method, optimization of parameters in the rough and finish passes have been done simultaneously instead of the two stage procedure. Since Genetic Algorithms suffer from the problem of premature convergence
to local optimum, some researchers have focused on using Hybrid Genetic Algorithms in which a local search based optimization procedure is used in conjunction with HA for optimizing machining operations .
OPTIMIZATION IN MACHINING
Machining parameters such as speed, feed and depth of cut play vital role in machining the given work piece to the required shape. These have a major affect on the quantity of production, cost of production and production rate; hence their judicious selection assumes significance. The selected machining parameters should yield desired quality on the machined surface while utilizing the machining resources such as machine tool and cutting tool to the fullest extent possible, consistent with the constraints on these resources. Traditionally the selection of machining parameters is carried out based on the experience of the machinist or the planner and referring the available catalogues and handbooks.
Manual selection of machining parameters reflects the problem of variability in experience and judgment among the planners. In addition to this, the induction of cost intensive NC machines onto the shop floor, stresses more emphasis on the effective utilization of these resources using the optimal machining parameters. Present industries use both conventional and NC machines on their shop floor, hence it becomes necessary to go for automated methods to select the optimal machining parameters that suit the demands of the present industries. The need for selecting and implementing optimal machining conditions and the most suitable cutting tool has been felt over the last few decades. Despite Taylor’s early work on establishing optimum cutting speeds in single pass turnings, progress has been slow since all the process parameters need to be optimized. Furthermore, for realistic solutions, the many constraints met in practice, such as low machine tool power, torque, force limits and component surface roughness, must be overcome. The non-availability of the required technological performance equation represents a major obstacle to implementation of optimized cutting conditions in practice. This follows since extensive testing is required to establish empirical performance equations for each tool coating–work material combination for a given machining operation, which can be quite expensive when a wide spectrum of machining operations is considered. Further the performance equations have to be updated as new coatings, new work materials and new cutting tools are introduced.
Computer aided procedures have been found reliable for their fastness, accuracy and consistency in the automated selection of machining parameters compared to their manual counterparts. Various optimization techniques can be used to find the optimal machining parameters for a particular machining operation. The determination of optimal cutting parameters such as axial depth of cut, radial depth of cut and feed rate, which are applicable for assigned cutting tools, is one of the vital modules in process planning of metal parts, since the accuracy of machined surface plays an important role in increasing productivity and competitiveness. One of the purposes is to investigate the optimal cutting parameters to minimize tool deflection for error compensation on the machined surface while maintaining material removal rate and stability of the cutting process. The main parameters in machining affecting tool deflection and surface finish are axial depth of cut, radial depth of cut and feed rate. The optimal cutting parameters are subjected to an objective function of tool deflection with the feasible range of cutting parameters. The user of the machine tool must know how to choose cutting parameters in order to minimize cutting time, cutting force and produce better surface finish (surface roughness) under stable conditions. Normally, feed rate,axial depth of cut and radial depth of cut immersion are chosen according to the technical guide. But these parameters are strongly dependent on the static and dynamic properties of the tool. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process takes place. This study introduces a developed computer algorithm to optimize the cutting parameters to minimize tool deflection and increase tool life and surface roughness for a constant material removal rate. The system is mainly based on a powerful artificial intelligence (AI) tool, called genetic algorithms (HA).The use of the impact and the power of AI techniques have been reflected on the performance of the optimization system. The methodology of the developed optimization system is illustrated by practical examples throughout the study. Optimization of the machining parameters increases the product quality to a great extent
APPROACHES FOR MACHINING PARAMETER SELECTION
The advances in the manufacturing engineering and developments in the areas of computer aided design and computer aided manufacturing, give a high level of automation in the present competitive environment. In accordance with this, attempts have also been made to automate the selection of machining parameters. The traditional methods have been replaced by reliable computer aided procedures in the selection of machining parameters. Much of the research work has been done and ample literature is available in this direction. The published literature reveals that there are two most popular approaches for automated selection of machining parameters as give below:
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DATA STORAGE AND RETRIEVAL APPROACH
This approach consists of two steps as shown in the figure (1.1). In the first step, recommended parameters for various combinations of work material, tool material and machining operation are collected from the shop floor, cutting tool and machine tool manufacturer’s catalogues and handbooks. The milling operation was carried out on Universal milling machine on steel AISI 1045 work piece material using two HSS tools. Optimization has been performed using HA to decide the best possible combination of feed rate, axial depth of cut and radial depth of cut by satisfying constraints including tool deflection, cutting force, tool life and surface roughness. Figure 5 shows the effect of tool deflection on the machined surface. In this work, error of tool deflection on the machined surface has been compensated by using genetic algorithm. Then, these parameters are stored in a database in a structured format as shown in the figure 1.1 (a). Based on the user inputs, the proper set of cutting conditions is retrieved from the database as shown in figure 1.1 (b) and presented to the user.