Multi-objective optimization for optimum tolerance synthesis with process and machine selection using a genetic algorithm

This paper presents a new approach to the tolerance synthesis of the component parts of assemblies by simultaneously optimizing three manufacturing parameters: manufacturing cost, including tolerance cost and quality loss cost; machining time; and machine overhead/idle time cost. A methodology has been developed using the Genetic Algorithm (GA) technique to solve this multi-objective optimization problem. The effectiveness of the proposed methodology has been demonstrated by solving a wheel mounting assembly problem consisting of five components, two subassemblies, two critical dimensions, two functional tolerances, and eight operations. Significant cost saving can be achieved by employing this methodology

Concurrent tolerance allocation and scheduling for complex assemblies

Traditionally, tolerance allocation and scheduling have been dealt with separately in the literature. The aim of tolerance allocation is to minimize the tolerance cost. When scheduling the sequence of product operations, the goal is to minimize the makespan, mean flow time, machine idle time, and machine idle time cost. Calculations of manufacturing costs derived separately using tolerance allocation and scheduling separately will not be accurate. Hence, in this work, component tolerance was allocated by minimizing both the manufacturing cost (sum of the tolerance and quality loss cost) and the machine idle time cost, considering the product sequence. A genetic algorithm (GA) was developed for allocating the tolerance of the components and determining the best product sequence of the scheduling. To illustrate the effectiveness of the proposed method, the results are compared with those obtained with existing wheel mounting assembly discussed in the literature

An Integrated Optimization Model for Product Design and Production Allocation in a Make to Order Manufacturing System

A mechanical assembly consists of several components to perform an intended function. At the design stage, the intended function must be converted into critical product dimensions. After determining the dimensions, a designer must determine the assembly tolerance and allocate this tolerance to the tolerances of the corresponding components. After determining the optimal tolerances, process selection must be conducted along with production allocation to the selected process. There are three aspects in commercial competition that must be considered by a manufacturing company: cost, quality, and delivery. The aim of this research is to develop an optimization model for process selection for a make to order company to minimize manufacturing cost, quality loss, and lateness cost. The model attempts to determine optimal tolerance and production allocation, which takes into consideration the production capacity and process sequence. Hence, the model attempts to include not only the product design decision, but also to solve the process selection and allocation problems. A numerical example is provided to show the implementation of the model

A unified approach to modeling, verifying, and improving the manufacturability of mechanical assemblies

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004.Includes bibliographical references (p. 247-256).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.The goal of a design engineering organization is to design products that satisfy customers. Reaching this objective is dependent, among other things, on five parameters: the customer expectations, the target percentage of satisfied customers, the nominal performance of the design, the variability in the manufacturing processes, and the sensitivity of the design performance to such variability. This work presents a unified methodology that is amendable to computer implementation for modeling these five parameters for products that are primarily mechanical in nature. The validity of this methodology is subject to five major assumptions: the nominal performance of the design matches the performance expected by the customer, the set of customer expectations can be represented completely by a set of geometric relationships and tolerances between features in the assembly, the degradation in product performance is due solely to quantifiable variability or mean shift in the assembly geometry, the variability in each geometric relationship is independent of the variability in any other geometric relationship, and any compliant parts in the assembly can be accurately modeled as sets of rigid parts connected with linearly-compliant joints. The assembly model is developed using a combination of Screw Theory, Network Theory, Homogeneous Transformation Matrices, and Probability Theory. It is shown how this model can be used to verify the manufacturability of a mechanical assembly design. It is also shown how the model and the results obtained from it can be used to improve the level of manufacturability of a design if it is found to be unacceptably low. To validate the effectiveness and accuracy of the methodology, an automated version written for Matlab®(cont.) was used to model and analyze the manufacturability of an engine valvetrain. The results of this case study are presented and compared to results using existing industry-standard tools. Several suggestions for improving the manufacturability of the valvetrain are also proposed and discussed.by J. Michael Gray.S.M

Dimensional variation analysis of deformable aluminium-intensive vehicle assemblies

The thesis concerns dimensional management and the provision of tools and techniques to assist designers and body engineers in the automotive industry with the tolerance specification and variation analysis of deformable aluminium-intensive vehicle (AIV) assemblies. [Continues.

Self-resilient production systems : framework for design synthesis of multi-station assembly systems

Product design changes are inevitable in the current trend of time-based competition where product models such as automotive bodies and aircraft fuselages are frequently upgraded and cause assembly process design changes. In recent years, several studies in engineering change management and reconfigurable systems have been conducted to address the challenges of frequent product and process design changes. However, the results of these studies are limited in their applications due to shortcomings in three aspects which are: (i) They rely heavily on past records which might only be a few relevant cases and insufficient to perform a reliable analysis; (ii) They focus mainly on managing design changes in product architecture instead of both product and process architecture; and (iii) They consider design changes at a station-level instead of a multistation level. To address the aforementioned challenges, this thesis proposes three interrelated research areas to simulate the design adjustments of the existing process architecture. These research areas involve: (i) the methodologies to model the existing process architecture design in order to use the developed models as assembly response functions for assessing Key Performance Indices (KPIs); (ii) the KPIs to assess quality, cost, and design complexity of the existing process architecture design which are used when making decisions to change the existing process architecture design; and (iii) the methodology to change the process architecture design to new optimal design solutions at a multi-station level. In the first research area, the methodology in modeling the functional dependence of process variables within the process architecture design are presented as well as the relations from process variables and product architecture design. To understand the engineering change propagation chain among process variables within the process architecture design, a functional dependence model is introduced to represent the design dependency among process variables by cascading relationships from customer requirements, product architecture, process architecture, and design tasks to optimise process variable design. This model is used to estimate the level of process variable design change propagation in the existing process architecture design Next, process yield, cost, and complexity indices are introduced and used as KPIs in this thesis to measure product quality, cost in changing the current process design, and dependency of process variables (i.e, change propagation), respectively. The process yield and complexity indices are obtained by using the Stream-of-Variation (SOVA) model and functional dependence model, respectively. The costing KPI is obtained by determining the cost in optimizing tolerances of process variables. The implication of the costing KPI on the overall cost in changing process architecture design is also discussed. These three comprehensive indices are used to support decision-making when redesigning the existing process architecture. Finally, the framework driven by functional optimisation is proposed to adjust the existing process architecture to meet the engineering change requirements. The framework provides a platform to integrate and analyze several individual design synthesis tasks which are necessary to optimise the multi-stage assembly processes such as tolerance of process variables, fixture layouts, or part-to-part joints. The developed framework based on transversal of hypergraph and task connectivity matrix which lead to the optimal sequence of these design tasks. In order to enhance visibility on the dependencies and hierarchy of design tasks, Design Structure Matrix and Task Flow Chain are also adopted. Three scenarios of engineering changes in industrial automotive design are used to illustrate the application of the proposed redesign methodology. The thesis concludes that it is not necessary to optimise all functional designs of process variables to accommodate the engineering changes. The selection of only relevant functional designs is sufficient, but the design optimisation of the process variables has to be conducted at the system level with consideration of dependency between selected functional designs

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book