Search

This paper proposes a topology modification method of generating gear grinding based on multi-axis linkage parameter optimization. In order to improve the accuracy of tooth flank modification in generating gear grinding and to reduce the transmission error of modified gear pair, a multi-objective optimization genetic algorithm is adopted to adjust machine movement. Firstly, according to the position and motion relationship among diamond wheel, worm wheel, and workpiece gear, the mathematical models of worm wheel dressing and generating gear grinding are established. The linkage relationships between motion axes are redefined to achieve topology modification. The radial feed F X 1 , tangential feed F Y 1 of worm wheel, and gear rotational angle φ g are defined as the fourth-order polynomial function of axial feed F Z 1 . Secondly, the transmission error model of modified gear is developed. And the polynomial coefficients of each axis are optimized with the objective of minimizing total tooth flank deviation and transmission error. Finally, the effectiveness of this method is verified by three numerical examples with different parameter groups. In addition, the diamond wheel and worm wheel used in the study are standard profile, which saves the cost of gear machining and improves the efficiency of production cycle. The research results could provide a theoretical reference for the improvement of generating gear grinding process. [ABSTRACT FROM AUTHOR]

Product design and development (PDD) involves various types of tradeoffs, for which multi-objective optimization has been a prevailing approach. Complex PDD optimization problems are associated with multidisciplinary decision makers and may encompass different levels of the design decision hierarchy. A practical concern is that many conflicting goals with diverse decision variables have to compete to arrive at equilibrium solutions, entailing a paradigm of leader-follower joint optimization (LFJO). In line with a typical decision chain of engineering optimization, comprising such consecutive stages as problem formation, optimization modeling, model solution, and optimization evaluation, this paper provides a state-of-the-art review of the LFJO problems that are widely observed in the PDD literature, along with a survey of LFJO mathematical modeling methods. Also reviewed is the status quo of bilevel programming (BLP) models for LFJO problems with focus on their engineering implications for PDD. The paper also reviews the prevailing algorithms for solving BLP mathematical models, as well as common practice of LFJO model evaluation in the PDD literature. Finally, the critical issues of LFJO problems and outlook for further research are discussed. [ABSTRACT FROM AUTHOR]

The paper presents results of CFD parametric study of centrifugal compressor stage vane diffusers in the Ansys CFX. The results obtained will be used to build a mathematical model of vane diffuser in optimization design system. Objects of research are vane diffusers with external relative diameter (relative to the diameter of the impeller) equal to 1.5; vane inlet angle of 20°; relative vane heights of 0.025, 0.034, 0.045, 0.06, and 0.08; and vane profile curvature angles of 10, 15, and 20 °. The characteristics of polytrophic efficiency, loss coefficient, recovery coefficient, ratio of inlet and outlet velocities, and flow deviation angle versus incidence angle are set. The analysis of the flow structure in the vane diffuser channels is presented. Unlike with a straight vane cascade, the deviation angle in the circular rows of vane diffusers tends to increase with increasing row density. This may be due to the complex nature of the interaction of the active part of the flow with separation zones. In rows with almost straight vanes at a lower density, the separation zone on the pressure side decreases and even shifts to the very end of the suction side. [ABSTRACT FROM AUTHOR]

In this paper, the linear and nonlinear mathematical force models are presented for an end-milling process. The nonlinear force model is compared with the updated linear force model. The mechanistic identification of the cutting force coefficients for the linear and nonlinear force models of the end milling process is presented with experimental validation. The cutting force coefficients are determined for an end-milling process using two methods, the average force method and the optimization technique method. The average force method uses equations given by Altintas and Budak [-] for calculating the cutting coefficients of the linear force model. However, the equations for the nonlinear model are derived and used in this paper. The second method uses the optimization technique for identifying the cutting coefficients in the milling process by forming objective functions using the optimization technique to minimize the error between the experimental and the analytical forces. Moreover, this method produced a good force model that approximates the experimental force results, which compared with the average force method for both models. [ABSTRACT FROM AUTHOR]

This paper presents a review of the literature on generating cutting paths for laser cutting machines. Firstly, the cutting path problem is defined including all relevant technical side constraints which exist plentifully in laser cutting. Secondly, a former classification method is updated to include all types of cutting path problems. Thirdly, a comprehensive review of solution methods and related applications is presented. Throughout the literature review, trends in research in cutting path generation and interesting areas for future research are identified. [ABSTRACT FROM AUTHOR]

This paper presents a tabu search algorithm that solves the flexible job shop scheduling problem to minimize the makespan time. As a context for solving sequencing and scheduling problems, the flexible job shop model is highly complicated. Alternative operation sequences and sequence-dependent setups are two important factors that frequently appear in various manufacturing environments and in project scheduling. In this paper, we present a model for a flexible job shop scheduling problem while considering those factors simultaneously. The purpose of this paper is to minimize the makespan time and to find the best sequence of operations and the best choice of machine alternatives, simultaneously. The proposed tabu search algorithm is composed of two parts: a procedure that searches for the best sequence of job operations, and a procedure that finds the best choice of machine alternatives. Randomly generated test problems are used to evaluate the performance of the proposed algorithm. Results of the algorithm are compared with the optimal solution using a mathematical model solved by the traditional optimization technique (the branch and bound method). After modeling the scheduling problem, the model is verified and validated. Then the computational results are presented. Computational results indicate that the proposed algorithm can produce optimal solutions in a short computational time for small and medium sized problems. Moreover, it can be applied easily in real factory conditions and for large size problems. The proposed algorithm should thus be useful to both practitioners and researchers. [ABSTRACT FROM AUTHOR]

This paper deals with a discrete simulation optimization method for designing a complex probabilistic discrete event system. The proposed algorithm in this paper searches the effective and reliable alternatives satisfying the target values of the system to be designed through a single run in a relatively short time period. It tries to estimate an auto-regressive model, and construct mean and confidence interval for evaluating correctly the objective function obtained by a small amount of output data. The experimental results using the proposed method are also shown. [ABSTRACT FROM AUTHOR]

Discrete event simulation (DES) has been widely applied to modelling and simulation of computer and engineering systems and is an active field of research that has now evolved from 2D to 3D discrete event simulation. This paper attempts to address several key issues in a successful implementation of DES models based on our own and the previous experiences of others. It describes the common basis, which forms the core for the application of modelling and simulation methodologies that are available to support manufacturing systems analysis, design and performance evaluation. Through a comprehensive literature survey, this paper summarises and compares the most widely used optimisation techniques for simulation of manufacturing systems; an overview of the recent and popular simulation languages and packages available for the modelling and simulation community and the classification of their utility for modelling and simulation of manufacturing systems is also given. Finally, this paper summarises and reports the latest development in the most exciting world wide web (www)-based simulation techniques that represent a future that may completely change the nature and future exploitation of modelling and simulation technology in industry. [ABSTRACT FROM AUTHOR]

The optimization of machining parameters for machine tools using the criterion of maximum productivity rate is complex and, in many cases, an unresolved issue. The intensification of machining processes leads to changes in the productivity rate. In the case of multi-tool machining processes, engaging cutters simultaneously to define optimal machining parameters for the maximum productivity rate is an important problem. Such multi-tool processes are conducted in multi-spindle and multi-station machine tools, turret-type lathes, automated lines, etc. This paper is intended to formulate a mathematical model for the optimization of multi-tool cutting processes on machine tools based on the criterion of maximum productivity rate. The mathematical model of the new productivity rate equation (for machine tools with changes in cutting for the optimal multi-tooling operation with simultaneous action) is confirmed by practical test. [ABSTRACT FROM AUTHOR]

This paper presents the optimization compensation based on the mathematical expressions of geometric error model for the accuracy enhancement of five-axis machine tools. This method belongs to mathematical optimization. At first, optimal polynomials of basic error components of each axis are established by fitting the measured data of errors. The constant term is set as zero based on the properties of error components. The appropriate degree of polynomials is determined by introducing F value in statistics. Second, the bi-directional transformation between tool poses and NC codes is built with the forward and inverse kinematics of machine tools. More specifically, the postprocessor of SmartCNC500_DRTD five-axis machine tool is proposed for the calculation of NC codes. The obtained NC codes reflect the real movements of all axes relative to their zero positions. Then, one novel geometric error model is established with mathematical expressions. The model contains the ideal tool pose with the input of nominal NC code. It can evaluate the effect of compensation. It lays the foundation for the optimization. Next, the particle swarm optimization (PSO) is used to seek the optimal NC code. Particles are defined as tool poses to accord with the physical meanings of integrated geometric errors. The initial particles are generated around the ideal tool pose. New moving of particles is proposed to avoid the local optimum. The postprocessor is used to transform particles to NC codes to calculate their fitness. Finally, the experiments are conducted on this SmartCNC500_DRTD five-axis machine tool to testify the effectiveness of optimization compensation. [ABSTRACT FROM AUTHOR]

This research paper develops an effective methodology to determine the optimum etching conditions for slider fabrication that meet hard disk drive (HDD) industrial targets using a reactive ion etching (RIE) machine of central composite design (CCD), coupled with RIE simulation. CCD is utilized to create an efficient analytical model for multi-response incorporating the RIE process parameters of pressure and coil and platen power. The research first determined the factors that cause production defects, using the design of experiments (DOE) method. Then, the most promising production parameters were selected. To optimize the parameters, an efficient CCD technique was proposed. Unfortunately, the CCD technique generated a large number of experimental combinations, leading to prohibitively high experimental costs. Simulation application software was used as a cost-efficient alternative, although RIE simulation and actual process parameters differ. Mathematical equations showing the parameter relationships were formulated. CCD interfaced with RIE simulation was further applied to find the optimal conditions for multi-responses. Finally, the optimal conditions obtained by desirability optimization technique were verified with experimental results and found to agree with HDD industrial standards. [ABSTRACT FROM AUTHOR]

Flexible automated assembly is an emerging need in several industries. In the case of a very wide set of models and a total medium/low derived production volume, the proper assembly system to use is a single cell with high flexibility capabilities. An innovative concept in flexible automated assembly has recently been introduced [, ]: the fully flexible assembly system (F-FAS). The F-FAS relies on a single-station robotized assembly system, where a unique fully flexible feeder is responsible for the delivery of the parts needed for assembly, guaranteeing a higher level of flexibility than the traditional automated FAS. The mixed-model sequencing (MMS) problem is typically related to the assembly line system. The aim of this paper is to introduce a new class of MMS problem: the single-station mixed-model sequencing problem that arises when the parts to assemble are randomly presented on the working plane, as in the F-FAS. The authors first define the MMS in such a single-station assembly system and then propose different sequencing algorithms in order to solve it. The authors first define the problem and then propose different sequencing algorithms. With the aim of finding the best sequencing approach to use in such an assembly system, the algorithms are compared through ad hoc developed benchmarking tests, using a dedicated software application that simulates the real behavior of the work cell. [ABSTRACT FROM AUTHOR]

This paper develops an optimization model to investigate the lengths of the optimal burn-in and warranty period, so that the mean of total product servicing cost is minimised. It is assumed that the cost of a minimal repair to a component at age t is a continuous non-decreasing function of t. Moreover, we model the customer dissatisfaction with product failures after the warranty within the product useful lifetime by introducing a post-warranty failure penalty cost to the manufacturer. The properties of the optimal burn-in time and optimal warranty policy are also analysed. Finally, under different product lifetime distributions, numerical examples and sensitivity analysis with respects to the values of the model parameters are provided. [ABSTRACT FROM AUTHOR]

This paper presents a method for the process planning optimization of hole-making operations, where the machining process of a hole consists of several individual operations with different machining tools. The optimization objective is to minimize the total auxiliary time of the machining process, including the airtime and the tool changing time. The machining process of a part is represented as a dynamic directed graph, where each graph node represents a single operation in the hole-making process. The objective of minimizing the total auxiliary time is to find the shortest path of the directed graph, and this objective has a structure similar to the traveling salesman problem. A mathematical model that describes the hole-making problem is proposed based on this graph structure, and an ant colony algorithm is applied to solve the optimization, since the problem is NP-complete. It is demonstrated with examples that the proposed method can significantly reduce the auxiliary time of hole-making process. [ABSTRACT FROM AUTHOR]

Process planning is a function that establishes the technological requirements necessary to convert a part from its raw material to the finished form. Generally, the result of process planning is delivered to the workshop to guide the manufacturing process in the form of process plan. However, a part always has multi alternative process plans for the processing means and techniques are not unique, therefore, optimization and selection of process plans is an important task of flexible process planning. In this paper, the flexibility of process planning and the AND/OR network adopted to represent the flexibility of process plans were described, and a mathematical model for the optimization of flexible process planning based on the AND/OR network was established. On this basis, a new heuristic method, called cross-entropy (CE) approach, was proposed to optimize flexible process planning. In order to facilitate the implementation of the CE-based approach, the new sample representation and probability distribution parameter were introduced; meanwhile, the new sample generation mechanism was presented and the updating expression of probability distribution parameter was deduced. Case studies, used for comparing this approach with genetic algorithm (GA) and genetic programming (GP)-based approach, were discussed to indicate the performance and adaptability of the proposed CE-based approach in terms of the solution quality and computational efficiency of the algorithm. The results show that the CE-based approach is effective for the optimization research of flexible process planning. [ABSTRACT FROM AUTHOR]

Electrochemical machining (ECM) is well-established in certain areas, such as the aerospace, defence, and medical industries for machining complex parts. ECM is an effective way to produce compression blades. However, with advances in the development of aero engines, the accuracy of blades becomes higher and their shapes have also become more complex. Thus, considerable attention has been devoted to improving the machining accuracy of blades by ECM. In blades machined by ECM, the angle between the cathode feeding directions and the normal of the anode profile greatly affects the accuracy of the ECM process, and it is determined by the angle combination of the anode installation and the cathodes feeding direction. It is very important to determine the best angle combinations that could minimise the angle between the cathode feeding directions and the normal of the anode profile. The present paper focuses on the optimization method of the angle combinations of the cathode feeding directions and anode installation. A theoretical model describing the optimization method of the cathode feeding directions and position of the anode was developed, and the experimental investigations were conducted in order to evaluate the rationality of the optimization method. The results show that with the optimized combination of the cathode feeding directions and position of the anode, the maximum angle between the cathodes feeding directions and the normal of the anode profile is minimised and the inter-electrode gap between the cathode and anode is more uniform. Thus, the machining accuracy could be clearly improved and the maximum errors of the convex part and concave part were only 0.054 and 0.047 mm, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem where after usage the finished goods are collected at a constant rate and returned to the manufacturer who uses them to produce new products. In order to optimize this kind of closed-loop supply chain, it is necessary to synchronize the forward flow and the reverse flow in establishing the operational policies for both the lot size and the delivery frequency between products consumption and parts recovery. Additionally, the decision regarding recovery policy with lot-streaming policy should be considered deliberately to minimize overall supply chain cost. We develop the optimization model for controlling the lot-streaming policy in the closed-loop logistics scheme and propose the solution procedure for determining the lot-streaming policy and recovery policy. From the sensitivity analyses, we observe that optimal lot-streaming policy and recovery policy could be characterized by either pure strategy or mixed strategy according to the changes in the relevant cost factors. So, an integrated management of both lot-streaming policy and recovery decision could be a critical managerial issue in a closed-loop supply chain. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop mathematical model and computational procedure for optimisation of feed rate at CNC routing operations of wooden furniture parts. The proposed approach takes into consideration special characteristics of solid wood, like changes in cutting forces and obtained surface quality along with cutting direction. A multistage optimisation procedure is developed, which employs binary search method and genetic algorithm. Because of the problem of full specification of feasible region in the form of inequalities, a fuzzy logic is used for correction of intermediate results within computational procedure. The developed method is verified by optimisation of routing operation of a typical furniture part, i.e. commode top. As a result of optimisation, the processing time is reduced by 54 %. [ABSTRACT FROM AUTHOR]

This paper presents a method for tolerance balancing in machining process planning. The surface position tolerance (SPT) method offers several advantages in comparison with the tolerance charting technique. These advantages arise from the separate consideration of the datum and machining surface position capabilities. To solve the problem of tolerance balancing, the mathematical model used in the literature is adapted to the SPT method. When solving the problem, the objective of maximizing process tolerances was chosen. The proposal was applied to a problem that has been used by many authors in the literature and so a wide comparison can be made of the results. The solution enables the tolerances in the process to be improved and the solution can also be optimized with respect to the capability of the fixture. [ABSTRACT FROM AUTHOR]

Jobs with different processing complexity may have different effects on human learning, which in turn will affect the job processing times. So, the learning effect on job processing times in scheduling is not only sum-of-processing-time-based and job-position-based, but also depends on the processing complexity of the jobs already processed. We introduce in this paper a general learning effect model for scheduling. We provide some properties that are helpful for finding the optimal solutions for some single-machine and flowshop scheduling problems under the general effect model. [ABSTRACT FROM AUTHOR]

Multiple-route job shop scheduling problem (MRJSP) is a generalization of job shop scheduling problem in which each job may have more than one route for its production and the numbers of operations associated to the alternative routes of the job are not necessarily equal. MRJSP is recognized to be extremely difficult because of its combinatorial nature of integer optimization and the large size of the real problem, necessitating the use of heuristic approaches or dispatching rules for its solution. In this paper, mathematical formulation of MRJSP is first presented. Then, a two-phase algorithm is proposed to deal with the two major sub-problems in MRJSP, namely, the route selection and the sequencing sub-problems. In order to evaluate the effectiveness of the proposed algorithm, several problems (in small, medium, and large sizes) are designed and solved using the proposed algorithm. The problems are also solved using some other dispatching rules, and comparisons are provided. The computational results show that the proposed algorithm generates high-quality schedules in a timely fashion. [ABSTRACT FROM AUTHOR]

This paper presents a new integrated methodology based on evolutionary algorithms (EAs) to model and optimize the laser beam cutting process. The proposed study is divided into two parts. Firstly, genetic programming (GP) approach is used for empirical modelling of kerf width (Kw) and material removal rate (MRR) which are the important performance measures of the laser beam cutting process. GP, being an extension of the more familiar genetic algorithms, recently has evolved as a powerful optimization tool for nonlinear modelling resulting in credible and accurate models. Design of experiments is used to conduct the experiments. Four prominent variables such as pulse frequency, pulse width, cutting speed and pulse energy are taken into consideration. The developed models are used to study the effect of laser cutting parameters on the chosen process performances. As the output parameters Kw and MRR are mutually conflicting in nature, in the second part of the study, they are simultaneously optimized by using a multi-objective evolutionary algorithm called non-dominated sorting genetic algorithm II. The Pareto optimal solutions of parameter settings have been reported that provide the decision maker an elaborate picture for making the optimal decisions. The work presents a full-fledged evolutionary approach for optimization of the process. [ABSTRACT FROM AUTHOR]

The present paper discusses response surface methodology (RSM) as an efficient system for building a mathematical model and optimization of the process of polyurethane coating on acrylonitrile butadiene styrene using a single axis robotic applicator. In this work, RSM is used to optimize the process inputs such as part-applicator distance, paint flow rate, and paint viscosity to obtain the optimal values of process outputs; dry film thickness (DFT), rating value ( R), and distinctness of image (DOI). The optimization was done to achieve high values of R and DOI with minimal DFT to reduce overall process cost. A 2 full factorial central composite design experimental design was employed. ANOVA showed satisfactory levels of coefficient of determination values ( R) for all three responses. The optimized results were validated experimentally. Under optimal value of process parameters high rating value (7.76) and high distinctness of image (93.137) was achieved with low dry film thickness of 29.99 μm. [ABSTRACT FROM AUTHOR]

Regression analysis is one of the most applicable methods in statistical methodology used to find the best regression model according to the relationship among several variables in a system. The estimation of regression model, which is solved as a formulate optimization problem and making use of heuristic algorithms, is much simpler and faster than classic methods. Genetic algorithm (GA) as one of the heuristic algorithms had been used to solve this problem. In this paper, we extend the noising method as a recent combinatorial optimization problem to estimate the best regression model and evaluate its performances compared to GA. Also, in order to enhance the performance of our GA, we apply the Taguchi experimental design method to tune the parameters of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the hot rolling batch scheduling problem is formulated as a multi-objective vehicle routing problem with double time windows model, in which the first time window deals with the surface grade constraint and the second one is for the linkage modes. In view of the complexity of the proposed model and the priority of considered objectives in practical production, a decomposition-based hierarchical optimization algorithm is proposed to solve the model. Firstly, the model is decomposed into two sub-problems: vehicle routing problem with time windows (VRPTW) and single vehicle routing problem with time windows (SVRPTW). Secondly, MACS-VRPTW is used to optimize the VRPTW sub-problem, in which the first objective is prior to the second one. Then, dynamic programming and genetic algorithm are used to optimize the SVRPTW sub-problem so as to reach a higher hot charge temperature. Experimental results based on the practical production instances have indicated that the proposed model and algorithm are effective and efficient. [ABSTRACT FROM AUTHOR]

This paper introduces a mathematical model of tolerance chart balancing for machining process planning under complete inspection. The criteria considered in this study are based on the combined effects of manufacturing cost and quality loss, under constraints such as process capability limits, product design specifications, and product quality requirements. Manufacturing costs include the machining cost, part cost, inspection cost, reworking cost, and replacement cost. The machining cost is expressed in geometrical decreasing functions, which represent tolerances to be assigned. Process variability is expressed in quadratic loss functions, which represent the deviation between the part measurement and the target value. An example is presented to demonstrate the proposed model. A comparison made with previous methods shows that the proposed model minimizes the total cost of manufacturing activities and quality-related issues in machining process planning, particularly in the early stages. Moreover, the applications are not be limited to machining process planning but can also be used in other forms of production planning. [ABSTRACT FROM AUTHOR]

In distributed manufacturing environments, the real competitive edge of an enterprise is directly related to the optimization level of its supply chain deployment in general, and, in particular, to how it allocates diverse manufacturing resources optimally. This is faced with increasing challenges caused by the conflicting objectives in manufacturing integration over distributed manufacturing resources. This paper presents a new manufacturing resource allocation method using extended genetic algorithm (GA) to support the multi-objective decision-making optimization for supply chain deployment. A new multi-objective decision-making mathematical model is proposed to evaluate, select, and sequence the candidate manufacturing resources allocated to sub-tasks composing the supply chain, by dealing with the trade-offs among multiple objectives including similarity, time, cost, quality, and service. An extended GA approach with problem-specific two-dimensional representation scheme, selection operator, crossover operator, and mutation operator is proposed to solve the mathematical model optimally by designing a chromosome containing two kinds of information, i.e., resource selection and resource sequencing. A case study is carried out to demonstrate the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

The characteristic feature of edge disintegration easily appears in the electric discharge machining (EDM) drilling processing of cobalt-bonded tungsten carbide (WC-Co). Such tendency reduces the strength against fatigue and results in a poor assembly tolerance. The objective of this paper was to present the mathematical models for modeling and analysis of the effects of process parameters, including the discharge current, pulse time on, duty factor, and capacitance value, on the disintegration factor at the entrance edge of drilled hole in the EDM drilling process of cobalt-bonded tungsten carbide. An experimental plan of a central composite design based on the response surface methodology (RSM) was employed to carry out the experimental study. The quadratic model of RSM associated with the sequential approximation optimization method was used to find the optimum settings of processing parameters. With the experimental values up to a 95% confidence interval, it is fairly well for the experimental results to present the mathematical model of disintegration factor. The results show that the interaction effect of discharge current with capacitance value has the greatest influence on the disintegration factor, followed by the capacitance value and the quadratic term of duty factor. The optimal settings of processing parameters obtained in this study represent the reduction of the 5.53% disintegration factor, which were compared with the results of initial processing parameters for drilling the cobalt-bonded tungsten carbide in the EDM process. [ABSTRACT FROM AUTHOR]

ive flow machining (AFM) is gaining widespread application finishing process on difficult to reach surfaces in aviation, automobile, and tooling industry. Al/SiC-MMC is a promising material in these industries. Here, AFM has been used to finish conventionally machined cylindrical surface of Al/15 wt% SiC-MMC workpiece. This paper presents the utilization of robust design-based Taguchi method for optimization of AFM parameters. The influences of AFM process parameters on surface finish and material removal have been analyzed. Taguchi experimental design concept, L (6 × 3) mixed orthogonal array is used to determine the S/N ratio and optimize the AFM process parameters. Analysis of variance and F test values also indicates the significant AFM parameters affecting the finishing performance. The mathematical models for R, R, Δ R, and Δ R and material removal are established to investigate the influence of AFM parameters. Conformation test results verify the effectiveness of these models and optimal parametric combination within the considered range. Scanning electron micrographs testifies the effectiveness of AFM process in fine finishing of Al/15 wt% SiC-MMC. [ABSTRACT FROM AUTHOR]

The Taguchi method of experimental design is widely used for optimization of process performance. However, this method has been developed to optimize single-response processes. But, in many situations, the engineers are required to determine the process settings that can simultaneously optimize multiple responses. In the recent past, researchers have proposed several systematic procedures for multi-response optimization. Most of these methods use complicated statistical/mathematical models and are, therefore, not easily comprehendible to the engineers who do not have a strong background in mathematics. Only a few methods, e.g. weighted signal-to-noise (WSN) ratio, Grey relational analysis, multiple-response signal-to-noise ratio, VIKOR ( VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian), and weighted principal component methods, use relatively simpler procedures. In this paper, the computational procedures for these five methods are standardized. Three sets of experimental data are analyzed using these standardized procedures and the predicted optimization performances of the five methods are compared. The results show that no method can give better optimization than the WSN method. [ABSTRACT FROM AUTHOR]

The disassembly line is the best choice for automated disassembly of disposal products. Therefore, disassembly line should be designed and balanced so that it can work as efficiently as possible. In this paper, a mathematical model for the multi-objective disassembly line balancing problem is formalized firstly. Then, a novel multi-objective ant colony optimization (MOACO) algorithm is proposed for solving this multi-objective optimization problem. Taking into account the problem constraints, a solution construction mechanism based on the method of tasks assignment is utilized in the algorithm. Additionally, niche technology is used to embed in the updating operation to search the Pareto optimal solutions. Moreover, in order to find the Pareto optimal set, the MOACO algorithm uses the concept of Pareto dominance to dynamically filter the obtained non-dominated solution set. To validate the performance of algorithm, the proposed algorithm is measured over published results obtained from single-objective optimization approaches and compared with multi-objective ACO algorithm based on uniform design. The experimental results show that the proposed MOACO is well suited to multi-objective optimization in disassembly line balancing. [ABSTRACT FROM AUTHOR]

Milling operations are very common in manufacturing. Often it represents the last operation, determining the final product quality. Then an accurate mathematical model is important in order to design the cutting process, in terms of cutting process, and the geometry of the insert, for tool manufacturers. The finite element modeling (FEM) simulation permits the prediction of the cutting forces, stresses, and temperatures of the cutting process. The 2D FEM can be a reasonable approximation, where the deformation can be considered plain. For the milling operations, this assumption can be suitable if the depth of cut is much bigger than nose radius. But in the normal situation the insert has a complex geometry and the bidimensional model of the milling operation is not appropriate. The 3D FEM involves different element formulations, different remeshing algorithm, and different boundary conditions, so an independent approach is necessary. The approach followed in this paper is to model three-dimensionally the milling operation, considering the real geometry of the insert. The FEM simulation is carried out with a commercial code (3D DEFORM™). First the rheological model has been calibrated using OXCUT software, developed at the ERC/NSM, and a sensitivity analysis about friction model has been performed. Milling tests are conducted and the measured cutting forces are compared to finite element modeling results. The results show an acceptable agreement with experimental results in the range of cutting speed and feed rate considered. [ABSTRACT FROM AUTHOR]

The traditional manufacturing system research literature generally assumed that there was only one feasible process plan for each job. This implied that there was no flexibility considered in the process plan. But, in the modern manufacturing system, most jobs may have a large number of flexible process plans. So, flexible process plans selection in a manufacturing environment has become a crucial problem. In this paper, a new method using an evolutionary algorithm, called genetic programming (GP), is presented to optimize flexible process planning. The flexible process plans and the mathematical model of flexible process planning have been described, and a network representation is adopted to describe the flexibility of process plans. To satisfy GP, it is very important to convert the network to a tree. The efficient genetic representations and operator schemes also have been considered. Case studies have been used to test the algorithm, and the comparison has been made for this approach and genetic algorithm (GA), which is another popular evolutionary approach to indicate the adaptability and superiority of the GP-based approach. The experimental results show that the proposed method ispromising and very effective in the optimization research of flexible process planning. [ABSTRACT FROM AUTHOR]

This paper presents an approach based on the constraints network to support collaborative design. Firstly, from the point of view of collaborative design, the model of the constraints network for coordination and optimization is studied. Secondly, a general consistency algorithm is designed to predict conflict and refine the intervals for parameter coordination, which verify the design stage early in the process and assist the designers in determining design variables to reduce the multidisciplinary iterations in collaborative design. Finally, a constraints-network-based parameter optimization model is established, and the multiobjective optimization approach is presented. The method is demonstrated by a bogie parameter design problem. [ABSTRACT FROM AUTHOR]