Sankha Deb

GENERATION OF OPTIMAL SEQUENCE OF MACHINING OPERATIONS IN SETUP PLANNING BY GENETIC ALGORITHMS

This paper aims at automatic generation of optimal sequence of machining operations in setup planning by Genetic Algorithm (GA) based on minimizing the number of setup changes and tool changes, subject to various machining precedence constraints. The GA has been reconstructed as the method of representing an operation is not as simple as assigning it a binary digit as in case of a chromosome in traditional GA but it has to be a distinct real number. Accordingly, the GA operators had to be modified. At the end of each GA cycle, there might be chromosomes having high fitness values but not conforming to constraints. Moreover, due to randomness of GA, the conformable chromosomes might tend to get lost. In order to minimize such losses, the elitist model is used for selection of chromosomes. Furthermore, a special subroutine has been developed to check the chromosomes for conformability and modify/repair those that violate the constraints.

Assembly sequence optimization using a flower pollination algorithm-based approach

One of the important decisions in assembly process planning is determination of assembly sequence. Choice of the optimum sequence is made difficult due to various reasons. There are various precedence constraints and optimization criteria. Moreover, a product may be possible to assemble in many alternative ways following different sequences, thus making assembly sequence optimization a multi-modal optimization problem with multiple optimum solutions. It is necessary to generate as many unique optimum solutions as possible in order to allow the process planner to take a decision. Moreover, with increase in part count, the number of feasible sequences rises staggeringly, thereby making assembly sequence optimization laborious and time consuming. Most conventional mathematical algorithms are known to perform poorly when used to obtain multiple optimum solutions. On the other hand, soft computing based evolutionary optimization algorithms are good candidates for multi-modal optimization. Another challenge is to develop an algorithm that can automatically maintain diversity in the optimum solutions found over the generations (i.e. optimum solutions having the same fitness but unique). Keeping the above in mind, in the present paper, an intelligent assembly sequence optimization methodology based on application of flower pollination algorithm (FPA) has been developed to automatically generate multiple unique optimal assembly sequences, subject to various precedence constraints, based on minimisation of number of orientation changes and tool changes. Since in the present paper, FPA has been applied for the first time to a discrete optimization problem like assembly sequence optimization, the main challenge before us in applying FPA was the continuous nature of the original FPA. Therefore, modifications have been made by us in the rules for local and global pollination of FPA to make it suited for solving the given discrete optimization problem. In order to evaluate the performance of FPA, the results have been compared with two other well-known soft computing techniques namely, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) and also with a recently published soft computing based algorithm, Improved Harmony Search (IHS). It was found that the novelty of the proposed FPA lies in its capability to find multiple unique optimum solutions in one single simulation run and capability to automatically maintain diversity in the optimum solutions found over the generations. On the other hand, in case of GA, ACO and IHS, it is not possible to maintain the diversity in multiple optimum solutions as the complete population finally converges to a few unique optimum solutions. Therefore, it can be concluded that FPA performs better in solving the given multi-modal optimization problem of assembly sequence optimization.

Intelligent Machining: Computational Methods and Optimization

This chapter provides an introduction to intelligent machining. The various computational techniques to achieve the goal of intelligent machining are described. First, a description of neural networks and fuzzy set theory is presented. These are soft computing techniques. Afterwards the application of the finite element method to the machining processes is briefly mentioned. Finally, the optimization of machining processes is described.

An Intelligent Methodology for Assembly Tools Selection and Assembly Sequence Optimisation

In the present paper, an intelligent methodology has been used for assembly tool/gripper selection and determination of the optimal assembly sequence. Since it is well known that assembly sequence planning (ASP) is an NP-hard combinatorial optimization problem, in particular, with increase in number of components in the assembly, the computational complexity involved in searching for the optimal assembly sequence in such a large solution space also increases. Furthermore, assembly process planning, tool/gripper selection is also an important decision making task and becomes tedious and time consuming for an assembly with large number of components. Keeping the above in mind, in the present work, a knowledge based system has been developed for selection of assembly tools and grippers for performing the assembly, while a Genetic Algorithm (GA) based approach has been used to determine the feasible and optimal assembly sequences considering minimum number of tool changes and assembly direction changes.

A GENETIC ALGORITHM-BASED APPROACH FOR OPTIMIZATION OF SCHEDULING IN JOB SHOP ENVIRONMENT

The present work aims to develop a genetic algorithm-based approach to solve the scheduling optimization problem in the Job Shop manufacturing environment. A new encoding scheme for chromosome representation has been developed for this purpose that denotes a priority sequence of operations, from which a schedule can be generated if the precedence constraints are known. The successful implementation of the proposed encoding scheme has been presented and its performance has been compared with the existing operation-based scheme found in literatures across different test cases by varying the number of jobs and machines in the shop floor.

Fabrication of Micro-cutting Tools for Mechanical Micro-machining

Micro-cutting processes are very effective manufacturing methods for complex micro-parts used in MEMS, micro-dies, micro-structured surfaces on nonconductive materials, etc. Main challenge in employing conventional machining methods for fabrication of micro-parts and features is the unavailability of the smaller tools. Popular method like grinding is failed in miniaturizing the cutting tools because of rigidity problems. This became a driving force to research the alternative processes. Different processes like electro-discharge machining, wire electro-discharge machining, laser beam machining, focused ion beam machining, etc., are evolved to accomplish the need of new fabrication methods for micro-cutting tools. Each process has showed its capabilities and limitations through various machining experiments. In this chapter, an overview of the micro-tool fabrication processes along with their characteristics is presented. New micro-end mill tool geometry and a tool fabrication method are also presented.

A Three Finger Tendon Driven Robotic Hand Design and Its Kinematics Model

Anatomy of human hand is very complex in nature. The structure of human hand consists of number of joints, bones, muscles and tendons, which creates a wide range of movements. It is very difficult to design a robotic hand and incorporate all the features of a normal human hand. In this paper, the model of a three finger robotic hand has been proposed. To replace the muscles and tendons of real human hand, it is proposed to use tendon wire and place the actuator at the palm. The advantage of using tendon and placing actuator at remote location is that it actually reduces the size of the hand. Pulling the tendon wire produces flexor motion in the hand finger. Currently torsional spring is considered at the joint for the extension motion of the finger. The purpose of design of such a hand is to grasp different kinds of object shapes. The paper further presents a kinematics model of the three finger hand and a mapping function to map the joint space coordinates to tendon space coordinates. Finally the hand model is simulated to validate the kinematics equations.

Effect of datum surface roughness on parallelism and perpendicularity tolerances in milling of prismatic parts

Abstract Surface roughness of the datum face is an important criterion to be considered for the selection of the datum during set-up planning as it can affect the tolerances among features. In this work, an experimental study on the effect of datum surface roughness on parallelism and perpendicularity tolerances in machining of prismatic parts by milling is carried out. A simplified mathematical model is developed to analyse the experimental findings. Statistical analysis of the experimental results has also been carried out. It is observed that sometimes a rough datum can provide better parallelism and perpendicularity than the smooth datum. Thus, the surface roughness of the datum should be decided judiciously considering the prescribed geometric tolerances and machining characteristics on a particular machine tool as well as the required surface integrity.

Experimental and theoretical investigation into simultaneous deburring of microchannel and cleaning of the cutting tool in micromilling

High material removal rate, high resolution and reproducibility of micromilling make it a suitable process for making microfeatures on different materials. However, existence of unwanted protrusions or surface irregularities on the machined surface known as burrs demands some post-processing to make it qualify for end use. In addition, clogging of materials on the flute surface increases the effective edge radius and tool loading during polymer machining. In the present work, a new strategy is demonstrated for simultaneous deburring of microchannel and cleaning of cutting tool during micromilling of polymethyl methacrylate. The performance of the process has been further evaluated by milling of microchannels in oxygen-free high conductivity copper workpiece. Burr formation, surface roughness and tool loading have been found to be significantly reduced by using the proposed method. A theoretical study of the process has also been carried out to understand the mechanism of the proposed method.

Development of a Flexible Assembly System Using Industrial Robot with Machine Vision Guidance and Dexterous Multi-finger Gripper

In today’s era of mass customization, assembly automation systems should be designed with necessary production flexibility to cope with the growing product varieties to adapt to diverse customer requirements, yet the production costs should not be significantly different from those of comparable products made by mass production. In order to cope with this product variety-cost trade-off, robotics offers a flexible automation technology for turning assembly systems into efficient and flexible systems. Despite their great potential for high flexibility, there is a range of issues which must be addressed for its successful implementation. This chapter examines some of these key issues and challenges, reviews the results of previous research and describes our ongoing research on development of a flexible assembly system for mechanical products, using an industrial robot with machine vision guidance and dexterous multi-finger gripper. As part of the research work reported in this chapter, a Sexual Genetic Algorithm (SGA)-based approach for generation of optimal assembly sequence, a knowledge-based system for generating the robot task-level plan, a multi-finger robot gripper for flexible assembly based on a tendon-driven mechanism and an impedance control algorithm, and finally a strategy for implementation of robotic assembly under machine vision guidance have been presented.