RC汽車模型底盤的簡要設(shè)計與制造
RC汽車模型底盤的簡要設(shè)計與制造,rc,汽車模型,底盤,簡要,扼要,設(shè)計,制造
本科畢業(yè)設(shè)計(論文)
外文翻譯（附外文原文）
學 院：機械與控制工程學院
課題名稱：RC模型汽車底盤的簡要設(shè)計與制造
專業(yè)(方向)：機械設(shè)計制造及其自動化（機械裝備）
班 級：機械11-2班
學 生：黃紹樣
指導教師：孫金榮
日 期：2015年3月10日
Optimization of Tools for CNC Machine: AnExplication & An Overview
N.S. Pohokar, L.B.Bhuyar
Abstract: Over the last few decades, the range of engineering materials encountered in machine shops has increased greatly, as has thevariety of cutting tools that are capable of machining these materials. Recent advances in computer hardware and software technologyhave led to research in calculation of efficient cutting parameters and design and development of a tool or combination of tools for aspecific operation or set of operations. In this paper an attempt is made to review the literature on optimizing machining parameters andgeometric parameters in CNC machine. Various conventional techniques employed for machining optimization include geometricprogramming, geometric plus linear programming, goal programming, sequential unconstrained minimization technique, dynamicprogramming etc. The latest techniques for optimization include fuzzy logic, scatter search technique, genetic algorithm, Taguchi technique and response surface methodology. This paper gives the machine tool methodology optimization.
Index Terms: CNC, Machining optimization, goal programming, fuzzy logic,genetic algorithms, Taguchi technique, response surface methodology.
1 INTRODUCTION
As the tool life & tool wear is a major problem faced by theindustry, it has long been recognized that conditions during cutting, such as feed rate, cutting speedand depth of cut, should be selected to optimize the economics of machining operations, as assessed by productivity, total manufac-turing cost per component or some other suitable criterion.Taylor showed that an optimum or economic cutting speed exists which could maximize material removal rate. Manufacturing industries have long depended on the skill and experience of shop- oor machine-tool operators for optimal selection of cutting conditions and cutting tools. Considerable efforts are still in progress on the use of handbook based conservative cutting conditions and cutting tool selection at the process planning level. The most adverse effect of such a not very scienti c practice is decreased productivity due to suboptimal use of machining capability. 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 over come.The nonavailability 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. While comprehensive sets of equations are found in some Chinese and Russian handbooks as well in the American handbook and Kroneberg’s textbook most authors have not included discussions on the more modern tools, new work materials and tool coatings. Dif culties are experienced in locating the empirical performance equations for moderntool designs because these are hidden under computerized databases in proprietary software as noted in recent investigations.
Recent works are concentrated on design and development of tool, milling operations for determining machining parameters. The optimization strategy for single pass end milling on CNC machine tools, allowing for many practical constraints has been based on minimum production time per component.
Different optimization procedures have been developed using new approaches such as genetic algorithm (GA), hill climbing algorithm and memetic algorithm to optimize machining parameters for milling operations.
Modeling and simulation of machining processes is a critical step in the realization of high quality machined parts. To precisely simulate the machining operations, accurate models of cutting tools used in the machining processes are required.In metal cutting industry, an end mill cutter plays an impor-
tant role for obtaining the desired shape and size of a component. A variety of helical end mill cutters are used in the industry. Helical cylindrical, helical ball, taper helical ball, bullnosed and special purpose end mills are widely used in aerospace, automotive and die machining industries. The analysis
of the geometry of the tool surfaces and cutting flutes along with the cutting forces acting on the end mill plays an important part in the design of the end mill and the quality of the manufacturing process. Traditionally, the geometry of cutting tools has been defined using the principles of projective geometry. The advancements in the domain of Computer Aided Design (CAD) allow a designer to specify the cutting tool surfaces in terms of biparametric surface patches. Using such an approach, one may develop the comprehensive threedimensional (3D) surface based definitions of the cutting tools.
Hu Gong describes an optimized positioning procedure for flank milling ruled surfaces with cylindrical cutter. The proposition that the envelope surface of cylindrical cutter is the offset surface of tool axis trajectory surface is proved using kinematics approach. It is a complement of Bedi’s analysis about the envelope surface of cylindrical cutter.Optimization procedures based on the genetic algorithm, hill climbing algorithm and memetic algorithm were demonstrated for the optimization of machining parameters for milling operation developed by basker et al. They describe development and utilization of an optimization system, which determines optimum machining parameters for milling operations like face milling, corner milling, pocket milling and slot milling.
Sheen et al developed an effective method for identifying machining features. It reliably determines the shapes between top and bottom profiles of workpiece. Most of the isolated, intersecting or 2.5D or 3D features can be recognized. Furthermore, the merging of the slot, step and notch features simplifies the manufacturing sequence and minimizes burr produced by discontinuous machining. Using the information of machining features, the manufacturing sequence can be automatically arranged and different kinds of tool paths can be efficiently generated.
A geometric approach to the problem of multi-patch machining by generating bisectors to partition the region into smaller sub regions, and generates the toolpath by offsetting the sub region boundary by using a special offset function was introduced by Li. The author developed this approach for generating a boundary conformed toolpath for free-form multi-patch surface machining.
Saffar et al. develops a 3D simulation system which is employed in order to predict cutting forces and tool deflection during end-milling operation. In order to verify the accuracy of 3D simulation, results (cutting forces and tool deflection) were compared with those based on the theoretical relationships may be attributed to the followings:
(1) Material properties in the simulations are defined based on the Johnson–Cook theory, i.e. they are a function of strain,strain rate, and workpiece temperature whereas in the theoretical relationships, properties are simply defined using the constant material coefficient.
(2) In simulation, non-linear geometric boundaries such as the free surface of the chip can be represented and used while theoretical relationships are based on linear geometric boundaries.
2 REVIEW OF LITURATURE
2.1 Conventional Optimization Technique
The follwing are some convetional optimization technique suggested by the concern researchers. Gilbert [2] studied the optimization of machining parameters in turning with respect to maximum production rate and minimum production cost as criteria. Armarego & Brown [10] investigated unconstrained machine-parameter optimization using differential calcu- lus. Brewer & Rueda [6] carried out simpli ed optimum analysis for non-ferrous materials. For cast iron (CI) and steels,they employed the criterion of reducing the machining cost to a minimum. A number of nomograms were worked out to facilitate the practical determination of the most economic machining conditions. They pointed out that the moredif cult-to-machine materials have a restricted range of parameters over which machining can be carried out and thus any attempt at optimizing their costs is arti cial. Brewer [8] suggested the use of agrangian multipliers for optimization of the constrained problem of unit cost, with cutting power as the main constraint. Bhattacharya et al [11] optimized the unit cost for turning, subject to the constraints of surface roughness and cutting power by the use of Lagrange’s method. Walvekar & Lambert [12] discussed the use of geometric programming to selection of machining variables. They optimized cutting speed and feed rate to yield minimum production cost. Petropoulos [13] investigated optimal selection of machining rate variables, viz. cutting speed and feed rate, by geometric programming. A constrained unit cost problem in turning was optimized by machining SAE 1045 steel with a cemented carbide tool of ISO P-10 grade. Sundaram [16] applied a goalprogramming technique in metal cutting for selecting levels of machining parameters in a ne turning operation on AISI 4140 steel using cemented tungsten carbide tools. Ermer & Kromodiharajo [19] developed a multi-step mathematical model to solve a constrained multi-pass machining problem. They cocluded that in some cases with certain constant total depths of cut, multi-pass machining was more economical than singlepass machining, if depth of cut for each pass was properly allocated. They used high speed steel (HSS) cutting tools to machine carbon steel. Hinduja et al [21] described a procedure to calculate the optimum cutting conditions for turning operations with minimum cost or maximum production rate as the objective function. For a given combination of tool and work material, the search for the optimum was con ned to a feed rate versus depth-of-cut plane de ned by the chip-breaking constraint. Some of the other constraints considered include power available, work holding, surface nish and dimensional accuracy. Tsai [25] studied the relationship between the multipass machining and single-pass machining. He presented the concept of a break-even point, i.e. there is always a point, a certain value of depth of cut, at which single-pass and doublepass machining are equally effective. When the depth of cut drops below the break-even point, the single-pass is more economical than the double-pass, and when the depth of cut rises above this break-even point, double-pass is better. Carbide tools are used to turn the carbon steel work material. Gopalakrishnan & Khayyal [37] described the design and development of an analytical tool for the selection of machine parameters in turning. Geometric programming was used as the basic methodology to determine values for feed rate and cutting speed that minimize the total cost of machining SAE 1045 steel with cemented carbide tools of ISO P-10 grade. Surface nish and machine power were taken as the constraints while optimizing cutting speed and feed rate for a given depth of cut. Agapiou [38] formulated single-pass and multi-pass machining operations. Production cost and total time were taken as objectives and a weighting factor was assigned to prioritize the two objectives in the objective function. He optimized the number of passes, depth of cut, cutting speed and feed rate in his model, through a multi-stage solution process called dynamic programming. Several physical constraints were considered and applied in his model. In his solution methodology, every cutting pass is independent of the previous pass, hence the optimality for each pass is not reached simultaneously.Prasad [43] reported the development of an optimization module for determining process parameters for turning operations as part of a PC-based generative CAPP system. The work piece materials considered in their study include steels, cast iron, aluminium, copper and brass. HSS and carbide tool materials are considered in this study. The minimization of production time is taken as the basis for formulating the objective function. The constraints considered in this study include power, surface nish, tolerance, work piece rigidity, range of cutting speed, maximum and minimum depths of cut and total depth of cut. Improved mathematical models are formulated by modifying the tolerance and work piece rigidity constraints for multi-pass turning operations. The formulated models are solved by the combination of geometric and linear programming techniques.
2.2 Current Techniques
The latest techniques for optimization include fuzzy logic,scatter search technique, genetic algorithm, Taguchi technique and response surface methodology.
2.2.1 Fuzzy Logic
Fuzzy logic has great capability to capture human commonsense reasoning, decision-making and other aspects of human cognition. Kosko [42] shows that it overcomes the limitations of classic logical systems, which impose inherent restrictions on representation of imprecise concepts. Vagueness in the coef cients and constraints may be naturally modelled by fuzzy logic. Modelling by fuzzy logic opens up a new way to optimize cutting conditions and also tool selection.Methodology: As per Klir & Yuan [44] fuzzy logic involves a fuzzy interference engine and a fuzzi cation-defuzzi cation module. Fuzzi cation expresses the input variables in the form of fuzzy membership values based on various membership functions. Governing rules in linguistic form, such as if cutting force is high and machining time is high, then tool wear is high, are formulated on the basis of experimental observations. Based on each rule,inference can be drawn on output grade and membership value. Inferences obtained from various rules are combined to arrive at a nal decision. The membership values thus obtained are defuzzi ed using various techniques to obtain true value, say of ank wear.
2.2.2 Genetic algorithm (GA)
These are the algorithms based on mechanics of natural selection and natural genetics, which are more robust and more likely to locate global optimum. It is because of this feature that GA goes through solution space starting from a group of points and not from a single point. The cutting conditions are encoded as genes by binary encoding to apply GA in optimization of machining parameters. A set of genes is combined together to form chromosomes, used to perform the basic mechanisms in GA, such as crossover and mutation. Crossover is the operation to exchange some part of two chromosomes to generate new offspring, which is important when exploring the whole search space rapidly. Mutation is applied after crossover to provide a small randomness to the new chromosomes. To evaluate each individual or chromosome,the encoded cutting conditions are decoded from the chromosomes and are used to predict machining performance measures. Fitness or objective function is a function needed in the optimization process and selection of next generation in genetic algorithm. Optimum results of cutting conditions are obtained by comparison of values of objective functions among all individuals after a number of iterations. Besides weighting factors and constraints, suitable parameters of GA are required to operate ef ciently. GA optimization methodology is based on machining performance predictions models developed from a comprehensive system of theoretical analysis,experimental database and numerical methods. The GA parameters along with relevant objective functions and set of machining performance constraints are imposed on GA optimization methodology to provide optimum cutting conditions.
Implementation of GA: First of all, the variables are encoded as n-bit binary numbers assigned in a row as chromosome strings. To implement constraints in GA, penalties are given to individuals out of constraint. If an individual is out of constraint, its tness will be assigned as zero. Because individuals are selected to mate according to tness value, zero tness individuals will not become parents. Thus most individuals in the next generation are ensured in feasible regions bounded by constraints. The GA is initialized by randomly selecting individuals in the full range of variables. Individuals are selected to be parents of the next generation according to their tness value. The larger the tness value, the greater their possibility of being selected as parents. Wang & Jawahir [58] have used this technique for optimization of milling machine parameters. Kuo & Yen [50] have used a genetic algorithm based parameter tuning algorithm for multidimensional motion control of a computer numerical control machine tool.
2.2.3 Scatter Search Technique (SS)
This technique originates from strategies for combining decision rules and surrogate constraints. SS is completely generalized and problem-independent since it has no restrictive assumptions about objective function, parameter set and constraint set. It can be easily modi ed to optimize machining operation under various economic criteria and numerous practical constraints. It can obtain near-optimal solutions within reasonable execution time on PC. Potentially, it can be extended as an on-line quality control strategy for optimizing machining parameters based on signals from sensors. Chen & Chen [51] have done extensive work on this technique.
Methodology: First of all, machining models are required to determine the optimum machining parameters including cut- ting speed, feed rate and depth of cut, in order to minimize unit production cost. Unit production cost can be divided into four basic cost elements:
●Cutting cost by actual cut in time
●Machine idle cost due to loading and unloading operation and idling tool motion cost
●Tool replacement cost
●Tool cost
For the optimization of unit production cost, practical constraints which present the state of machining processes need to be considered. The constraints imposed during machining operations are:
●Parameter constraint – Ranges of cutting speed, feed rateand depth of cut
●Tool life constraint – Allowable values of ank wear width and crater wear depth
●Operating constraint – Maximum allowable cutting force,power available on machine tool and surface nish requirement.
An optimization model for multi-pass turning operation can be formulated. The multi-pass turning model is a constrained nonlinear programming problem with multiple variables (machining variables). The initial solution for SS is picked in a random way. The user-speci ed parameters have to be given. The experimentation can be run on a PC with Pentium800Mhzprocessor. The computational results validate the advantage of SS in terms of solution quality and computational requirement.
2.2.4 Response surface methodology (RSM)
Experimentation and making inferences are the twin features of general scienti c methodol-ogy. Statistics as a scientic discipline is mainly designed to achieve these objectives. Planning of experiments is particula
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