D. Ravindran

A simple heuristic for linear sequencing of machines in layout design

This paper presents a simple heuristic to determine a common linear machine sequence for multiple products with different operation sequences and a limited number of duplicate machine types available for the job. The heuristic is based on minimisation of the total flow distance travelled by a product on the linear machine sequence. It is assumed that the flows of products are allowed only in the forward direction, either in-sequence or by-pass. It is also assumed that backtrack movements are not allowed. The effectiveness of the proposed heuristic is demonstrated through the solutions of two typical layout design problems taken from the literature. Subsequently, a number of additional problems are solved and their results are compared with the results applying existing methods. The results indicate that the proposed method can be an effective tool in solving layout design problems.

A Tabu Search for Multi-Objective Single Row Facility Layout Problem

This paper addresses the problem of multi-objective facility layout planning. The aim is to solve the single row facility layout problems (SRFLP) and find the linear machine sequence which minimizes the following: The total investment cost of machines; the total material handling cost; the total number of machines in the final sequence; and the total flow distance of the products in units. The tabu search algorithm (TSA) which has now become a very useful tool in solving a variety of combinatorial optimization problems is made use of here. TSA is developed to determine the product sequence based on which a common linear machine sequence is found out for multi-products with different machine sequences. We assume that, limited number of duplicate machine types available for job. The results are compared with other approaches and it shows the effectiveness of the TSA approach as a practical decision support tool to solve problems in SRFLP.

An integrated approach for optimal fixture layout design

Fixture layout optimization is a procedure to optimize position of locators and clamps in order to minimize specified objectives. Deformation of workpiece is a serious problem of concern while designing a fixture. Proper positioning of fixture elements is indispensable to achieve desired machining accuracy, better surface finish and high productivity. In this work, a novel methodology is proposed that incorporates full factorial design of experiments and statistical analysis. Furthermore, the stability of the workpiece is ensured prior to the prediction of objective function. The result of the proposed technique is compared with the results of the genetic algorithm–based optimization technique. A case study has been considered to evaluate the proposed methodology. The objective function determines maximum elastic deformation of the workpiece during the entire machining process. Finite element method is used to formulate the objective functions. The constraints are natural frequency of the workpiece–fixture system and reaction forces. Besides, artificial neural network–based model is developed to predict the elastic deformation of the workpiece–fixture system within the range of design parameters.

Optimum tolerance synthesis of simple assemblies with nominal dimension selection using genetic algorithm

Optimum tolerance allocation plays a vital role in minimization of the direct manufacturing cost, and it is sensitive to tolerances related to variations in manufacturing processes. However, optimal adjustment of both nominal dimensions and selection of tolerances may further reduce assembly manufacturing cost and wastage of materials during processing. Most studies in existing literature focus on optimum tolerance allocation for the assemblies without considering nominal dimension selection. The method proposed in this work uses genetic algorithm techniques to allocate tolerances to assembly components, thereby minimizing costs. The component alternate nominal dimensions are predicted based on critical dimensions and its tolerances. The effectiveness of the developed algorithms demonstrated using randomly generated problems as well as sample problems taken from the literature. Test results are compared with those obtained using the Lagrange multiplier method. It is shown that by adjusting the nominal dimensions, the proposed method yields considerable savings in manufacturing costs.

ECM Parameters Modeling and Optimization Using WSGA

Electrochemical machining (ECM) is a non tradition process used for the machining of metal matrix composites. Metal matrix composites are used for applications in aero scope, automobile industries and medical field. Determination of optimal process parameter is difficult in ECM machining process for obtaining maximum Material Removal Rate (MRR) and good Surface Roughness (SR).The multiple regression model was used to obtain the relationship between process parameters and output parameters and Weighted Sum Genetic Algorithm (WSGA) optimization was proposed to optimize the ECM process parameter. .The Voltage, Current, Feed Rate and Electrolyte Concentration are considered as decision variables, MRR and SR are the machining parameters used in the proposed work.

Tolerance allocation of complex assembly with nominal dimension selection using Artificial Bee Colony algorithm

Optimum tolerance allocation plays a vital role in minimizing the direct manufacturing cost of mechanical assembly. It is very sensitive due to the variations in manufacturing processes of the components. Most of the earlier studies are aiming at optimum tolerance allocation for assemblies without considering the selection of nominal dimensions of components and considering them as discrete values. It is proposed to minimize the manufacturing cost of an assembly with tolerance allocation and alternate nominal dimension selections by considering them in closer decimal intervals. The evolutionary algorithms such as Genetic and Artificial Bee Colony algorithms are developed and proposed to achieve the above objectives. The performance of the algorithms has been enhanced with the seed solution obtained using Lagrange Multiplier method. The complex assembly problems proposed by various authors with the required parameters have been considered for investigating the proposed method. The critical dimensions of the assemblies are fixed and the nominal dimension has been varied with its tolerances. The resultant manufacturing cost by various methods is presented and compared with corresponding nominal dimensions and tolerances. Based on the percentage of improvement of manufacturing cost, it is observed that the Artificial Bee Colony algorithm outperforms.

Prediction of Workpiece Elastic Deformation Using FEM Based Contact Analysis

The proper selection of fixtures and its locations is necessary to avoid the dimensional and form inaccuracies that are developed due to elastic deformation of the workpiece during machining. It is necessary to predict the elastic deformation with higher precision in order to eliminate the above inaccuracies during the design of fixtures. In the present work, a Finite Element Method (FEM) based 3D contact analysis has been proposed to evaluate the elastic deformation of the workpiece under the influence of fixtures and machining forces. In the proposed model, workpiece has been considered as flexible body and fixture elements (locators and clamps) as rigid bodies. A new concept of pre-stressed harmonic analysis has been introduced in order to simulate the machining forces. The contact area of the locators has been considered using contact elements and the clamping forces as point forces. The model for the analysis is developed in ANSYS environment with suitable elements. The effect of material removal has also been considered in the analysis in view of obtaining more accurate and realistic results. The deformation of the workpiece has been predicted for a particular location of clamps, locators and machining forces and presented. It is possible to optimize the location of clamps and locators under the influence of machining forces, the above analysis is suggested.