A Framework for Integrated Problem Solving in Manufacturing

In this paper, we present a comprehensive view of problem solving in manufacturing. The central tenet of this paper is that in addressing complex manufacturing problems, the major focus should be on key decisions and tasks, with the specific aim of improving the effectiveness of the manager's problem solving process. That entails modeling the decision-making process and integrating that with the rest of the problem solving process. A decision support system (DSS) framework that attempts to address these concerns in a unified manner is proposed.     

Integrated optimization of machine product design and process design

In order to attain the true integration of computer-aided design and computer-aided manufacturing not only is a smooth flow of information required, but also decision making for both product design and process design must be synthesized. In this paper an integrated design process is proposed in which decisions concerning both product design and process design are simultaneously made. According to the proposed design procedures, an integrated optimization problem is formulated. This optimization is expressed as a multiobjective optimization problem which produces many Pareto optimum solution sets corresponding to combinations of materials used for parts. The algorithm for solving the problem is also presented. The proposed method is applied to designing a cylindrical co-ordinate robot, thereby demonstrating the effectiveness of conducting a simultaneous process through product design and process design.     

基于U-TRIZ理论的产品概念设计SAFC模型分析

目的 针对经典TRIZ体系过于庞大,解题工具之间缺乏集成联系,以及难以掌握和运用等现状,通过运用U-TRIZ理论以功能为导向,以属性为核心,将因果分析模型和功能分析模型通过物质属性有机地集成,构建的物质—属性—功能—因果（SAFC）分析模型将有效解决上述问题。方法 分析SAFC模型在产品概念设计过程中的应用现状和研究进展,综述SAFC分析模型在矛盾类型问题求解、置换型问题求解、内调型问题求解的解题方法,给出SAFC分析模型的基本构成、功能因果链绘制图和差异性特征展示表,阐述U-TRIZ理论的解题流程总框架。结果 SAFC模型是一个从物场模型到功能模型演化而成的实用问题分析工具,有多种问题解题模式,能有效解决各种问题。结论 以智能手机概要设计为例,验证了SAFC分析模型的相关解题方法,该研究将为手机企业寻找创新方向和解决工程难题,提供一种可供借鉴的解决方案。     

Towards a generic product modelling framework

In response to the current rapidly changing manufacturing environment, product modelling technology has been widely applied to provide the essential information for product development (PD) processes. The traditional product modelling technologies are unable to support the information exchange and share in various stages of PD processes that could be taken place among departments in a company or even among companies in distributed manufacturing environment. This has caused many problems such as information loss, data format incompatibility and reduced efficiency and effectiveness of product data applications. This has consequently created bottlenecks for the integration of PD processes. In this paper, a generic product modelling framework (GPMF) is proposed to overcome the abovementioned problems in today's manufacturing environment. This framework uses the standard for the exchange product model data (STEP) as a foundation. It consists of four functional components: an EXPRESS data model namely EDM; a STEP–based modelling environment; a “five-phase” modelling method; and three EDM data exchange and sharing methods. Case studies show that the product models built based on the GPMF are capable of integrating information in product design, manufacturing and assembly. The GPMF is compatible, comprehensive, and flexible, and is able to support information exchange and sharing.     

Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)—the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.     

A study of convergence in decentralized design processes

The decomposition and coordination of decisions in the design of complex engineering systems is a great challenge. Companies who design these systems routinely allocate design responsibility of the various subsystems and components to different people, teams or even suppliers. The mechanisms behind this network of decentralized design decisions create difficult management and coordination issues. However, developing efficient design processes is paramount, especially with market pressures and customer expectations. Standard techniques to modeling and solving decentralized design problems typically fail to understand the underlying dynamics of the decentralized processes and therefore result in suboptimal solutions. This paper aims to model and understand the mechanisms and dynamics behind a decentralized set of decisions within a complex design process. By using concepts from the fields of mathematics and economics, including Game Theory and the Cobweb model, we model a simple decentralized design problem and provide efficient solutions. This new approach uses matrix series and linear algebra as tools to determine conditions for convergence of such decentralized design problems. The goal of this paper is to establish the first steps towards understanding the mechanisms of decentralized decision processes. This includes two major steps: studying the convergence characteristics and finding the final equilibrium solution of a decentralized problem. Illustrations of the developments are provided in the form of two decentralized design problems with different underlying behavior.     

Process-oriented tolerancing for multi-station assembly systems

In multi-station manufacturing systems, the quality of final products is significantly affected by both product design as well as process variables. Historically, however, tolerance research has primarily focused on allocating tolerances based on the product design characteristics of each component. Currently, there are no analytical approaches to optimally allocate tolerances to integrate product and process variables in multi-station manufacturing processes at minimum costs. The concept of process-oriented tolerancing expands the current tolerancing practices, which bound errors related to product variables, to explicitly include process variables. The resulting methodology extends the concept of “part interchangeability” into “process interchangeability,” which is critical due to increasing requirements related to the selection of suppliers and benchmarking. The proposed methodology is based on the development and integration of three models: (i) the tolerance-variation relation; (ii) variation propagation; and (iii) process degradation. The tolerance-variation model is based on a pin-hole fixture mechanism in multi-station assembly processes. The variation propagation model utilizes a state space representation but uses a station index instead of a time index. Dynamic process effects such as tool wear are also incorporated into the framework of process-oriented tolerancing, which provides the capability to design tolerances for the whole life-cycle of a production system. The tolerances of process variables are optimally allocated through solving a nonlinear constrained optimization problem. An industry case study is used to illustrate the proposed approach.     

A systematic approach to determine the optimal maintenance policy for an automated manufacturing system

We propose a systematic approach to determine the optimal maintenance policy for an automated manufacturing system which includes a flexible manufacturing cell (FMC) and several automated machine shops. The systematic approach combines simulation, fractional factorial design, noise or outer array of Taguchi design, regression metamodelling, and classical queueing analysis. A useful expression of the fractional utilization of the manufacturing system is derived and incorporated into formulating and solving the corresponding decision problem. The systematic approach provides an effective implementation procedure to handle practical maintenance problems found in a complex manufacturing environment. © 1997 John Wiley & Sons, Ltd.     

A Heaviside function-based density representation algorithm for truss-like buckling-induced mechanism design

Motivated by key advances in manufacturing techniques, the tailoring of materials to achieve novel properties such as energy dissipation properties has been the focus of active research in engineering and materials science over the past decade. The goal of material design is to determine the optimal spatial layout to achieve a desired macroscopic constitutive response. However, the manufacturing abilities are the key factors to constrain the feasible design space, eg, minimum length and geometry complexity. Traditional density-based method, where each element works as a variable, always results in complicated geometry with large number of small intricate features. To address the aforementioned challenges, a new density field representation technique, named, Heaviside function-based geometric representation algorithm, is proposed in this paper, where density field is represented by truss-like components. Truss-like components have less control parameters and easier to handle for sensitivities derivation, especially for distance sensitivities. Using bar components to explicitly represent density field can explore design space effectively and generate simple structures without any intricate small features at borders. Furthermore, this density representation method is mesh independent and design variables are reduced significantly so that optimization problem can be solved efficiently using small-scale optimization algorithm, eg, sequential quadratic programming. However, finding a reasonable initial component distribution is critical to avoid optimization failure. To overcome this difficulty, a jump-start method is proposed by solving inverse subproblem. The overall optimization progress is divided into three stages, ie, the first stage is obtaining coarse snap-through material configuration based on traditional density-based method; the second stage is an inverse optimization problem to fit the geometry component to the solution obtained in stage I; and the stage III is maximizing the energy dissipation capacity. To demonstrate the powerful ability in design buckling-induced mechanism of the proposed density representation algorithm, buckling-induced energy dissipation mechanism with snap-through behavior to achieve the desired energy dissipation capacity considering failure constraint is demonstrated through four numerical examples.     

Mathematical modelling of a robust inspection process plan: Taguchi and Monte Carlo methods

This study develops a new optimisation framework for process inspection planning of a manufacturing system with multiple quality characteristics, in which the proposed framework is based on a mixed-integer mathematical programming (MILP) model. Due to the stochastic nature of production processes and since their production processes are sensitive to manufacturing variations; a proportion of products do not conform the design specifications. A common source of these variations is maladjustment of each operation that leads to a higher number of scraps. Therefore, uncertainty in maladjustment is taken into account in this study. A twofold decision is made on the subject that which quality characteristic needs what kind of inspection, and the time this inspection should be performed. To cope with the introduced uncertainty, two robust optimisation methods are developed based on Taguchi and Monte Carlo methods. Furthermore, a genetic algorithm is applied to the problem to obtain near-optimal solutions. To validate the proposed model and solution approach, several numerical experiments are done on a real industrial case. Finally, the conclusion is provided.     

Hierarchical subspace evolution method for super large parallel computing: A linear solver and an eigensolver as examples

Solving linear equations and finding eigenvalues are essential tasks in many simulations for engineering applications, but these tasks often cause performance bottlenecks. In this work, the hierarchical subspace evolution method (HiSEM), a hierarchical iteration framework for solving scientific computing problems with solution locality, is proposed. In HiSEM, the original problem is converted to a corresponding minimization function. The problem is decomposed into a series of subsystems. Subspaces and their weights are established for the subsystems and evolve in each iteration. The subspaces are calculated based on local equations and knowledge of physical problems. A small-scale minimization problem determines the weights of the subspaces. The solution system can be hierarchically established based on the subspaces. As the iterations continue, the degrees of freedom gradually converge to an accurate solution. Two parallel algorithms are derived from HiSEM. One algorithm is designed for symmetric positive definite linear equations, and the other is designed for generalized eigenvalue problems. The linear solver and eigensolver performance is evaluated using a series of benchmarks and a tower model with a complex topology. Algorithms derived from HiSEM can solve a super large-scale problem with high performance and good scalability.     

A HIERARCHICAL DIVISIVE CLUSTERING METHOD FOR MACHINE-COMPONENT GROUPING PROBLEMS

This paper proposes a new approach based on graph theory for the machine-component grouping problem in a cellular manufacturing system. The objective is to form components into part families such that the degree of interrelations is high among components within the same part family and low between components of different part families.

Since finding an optimal solution through total enumeration is prohibitive in terms of time and efforts even for problems with a moderate number of components, a heuristic algorithm is proposed. The algorithm is hierarchical and divisive in nature and illustrated with numerical examples.     

Application of fuzzy analytic network process for agile concept selection in a manufacturing organisation

Twenty-first century manufacturing organisations are capable of satisfying the varied requirements of modern customers called agile manufacturing (AM). AM enables an organisation to produce a variety of products within a short period of time in a cost-effective manner. Few researchers have brought out models for enabling organisations to improve their agility level. Total agile design system (TADS) is a model that is analogous to the technology integrated agile product development cycle. Concept selection is an important phase of TADS which is a multi-criteria decision-making (MCDM) problem. Selection of the best concept from agile perspective gains vital importance. Analytical network process (ANP) is a holistic approach that considers interactions and dependencies of various attributes and alternatives in a hierarchical framework used in MCDM problems. The concepts of fuzzy logic have been integrated with ANP in order to overcome the vagueness and uncertainty associated with opinions of the decision makers. Fuzzy ANP has been used to enable the selection of best concept. The case study has been carried out in an Indian traditional manufacturing organisation. The results of the validation indicate that fuzzy ANP is an effective approach for selecting best concept thereby improving agility of product development process.     

Towards Aspect Based Components Integration Framework for Cyber-Physical System

Cyber-Physical Systems (CPS) comprise interactive computation, networking, and physical processes. The integrative environment of CPS enables the smart systems to be aware of the surrounding physical world. Smart systems, such as smart health care systems, smart homes, smart transportation, and smart cities, are made up of complex and dynamic CPS. The components integration development approach should be based on the divide and conquer theory. This way multiple interactive components can reduce the development complexity in CPS. As reusability enhances efficiency and consistency in CPS, encapsulation of component functionalities and a well-designed user interface is vital for the better end-user's Quality of Experience (QoE). Thus, incorrect interaction of interfaces in the cyber-physical system causes system failures. Usually, interface failures occur due to false, and ambiguous requirements analysis and specification. Therefore, to resolve this issue semantic analysis is required for different stakeholders’ viewpoint analysis during requirement specification and components analysis. This work proposes a framework to improve the CPS component integration process, starting from requirement specification to prioritization of components for configurable. For semantic analysis and assessing the reusability of specifications, the framework uses text mining and case-based reasoning techniques. The framework has been tested experimentally, and the results show a significant reduction in ambiguity, redundancy, and irrelevancy, as well as increasing accuracy of interface interactions, component selection, and higher user satisfaction.     

Solving coupled task assignment and capacity planning problems for a job shop by using a concurrent genetic algorithm

The problems of task assignment and capacity planning of manufacturing systems have been researched for many years. However, in the existing literature, these two types of problems are researched independently. Namely, when solving the task assignment problem, it is usually assumed that the production capacity of the manufacturing systems has been determined. On the other hand, when solving the capacity planning problem, the production tasks assigned to the workstations in the manufacturing system have also been determined. Actually, the task assignment problem and the capacity planning problem are coupled with each other. When we assign production tasks to workstations, production capacities of these workstations should be regulated so that they are enough for completing the tasks. At the same time, when planning the production capacity, we must know what production tasks are assigned to what workstations. This research focuses on the coupling relations between the two problems for a closed job shop, in which the total work-in-process (WIP) is assumed to be constant. The objective of the task assignment problem is to balance the workloads of the workstations and the objectives of the capacity planning problem are maximising the throughput and minimising total costs of machine purchasing and WIP inventory. We construct the fundamental system architecture for controlling the two coupled optimisation processes, and propose a concurrent genetic algorithm (CGA) to solve the two coupled optimisation problems. The influences of the decision variables of one problem on the objective function of the other problem are taken into consideration when the fitness functions of the CGA are constructed. Numerical experiments are done to verify the effectiveness of the algorithm.     

Multiple attribute decision-making solution to material selection problem based on modified fuzzy axiomatic design-model selection interface algorithm

Material selection, one of the mostly encountered decision problems in material science literature, is still an onerous task for manufacturing organisations. Achieving accurate solution to this issue, the paper developed a model selection interface to enable analytical solutions to different problem concepts in material selection under multiple attribute decision-making (MADM) environments. Specifically, the generic framework of the fuzzy axiomatic design-model selection interface (FAD-MSI) is modified and successfully applied to the different material selection problem concepts. Consequently, the derived problem-model sets can be referred to accomplish further proposals on this era.     

A systems approach to photolithography process optimization in an electronics manufacturing environment

There are many complex problems in the optimization of an electronics manufacturing environment, and it is the view of the authors that these problems should not be solved and optimized in isolation, but analysed in the framework of a system. A systems approach offers an overall approach for solving problems, and optimizing the whole of the system as well as discrete subsystems. The research introduced in this paper integrates several techniques, namely: Integrated computer aided manufacturing DEFinition (IDEF), and experimental design and response surface methods for the analysis, control and optimization of electronic manufacturing processes. Electronics manufacturing includes three major processes; Printed Circuit Board (PCB) manufacturing, semiconductor device manufacturing and electronics assembly. This paper describes a novel generic systematic methodology that has been used to create a model to optimize the photolithography process in PCB manufacture. For this, photolithography has been considered as a whole system made up of several sub-systems. This is shown in the process map for PCBs that focuses on photolithography and its subprocesses. A model of the manufacturing process is then given with the results of this being validated using an industrial study. Optimized settings for processing equipment are given resulting in an increase in process yield within industry.     

Improved design of CMS by considering operators decision-making styles

Cell formation is one of the oldest problems in cellular manufacturing systems (CMS) including assigning parts, machines and operators to cells. Cell manufacturing contains a number of cells where each cell is responsible for processing the family of similar parts. Another important aspect of cell formation is worker assignment to cells. Since operators work together in long periods, it is suggested to consider operators’ personal characteristics to increase their satisfaction and the productivity of system. This paper considers decision-making styles of operators (as an index of operator’s personal characteristics) and presents a new mathematical programming model for clustering parts, machines and workers simultaneously. The model includes two objectives; (1) minimization of intracellular movements and cell establishment costs, (2) minimization of decision-making style inconsistency among operators in each cell. The paper applies ε-constraint method for solving the problem and gathering non-dominated solutions such as Pareto optimal solutions. Furthermore, this paper uses common weighted multicriteria decision analysis (MCDA)-data envelopment analysis method to choose the best solution from the candidate Pareto optimal solutions that have been achieved by solving the mathematical model. A real case study is investigated to show the capability of the proposed model to design CMS in the assembly unit. The proposed design assists decision-makers to develop cellular systems with more operators’ satisfaction and productivity.     

DECISION INTEGRATION FUNDAMENTALS IN DISTRIBUTED MANUFACTURING TOPOLOGIES

Advances in control, communications, computer science and engineering have made it possible to design and implement large scale systems, where the decision making, control and information processing are distributed. This research effort attempts to establish the theoretical foundation of operational decision integration for such systems. Decision integration is a method to improve the quality of decision making. The basic elements of the integration process are defined. A simple distributed hypothesis testing model is employed to demonstrate that properly designed integration always improves the quality of the decisions. The problem of organizing decision making agents into architectures of integration (parallel versus hierarchical) is addressed. Several elementary decision architectures for small organizations are analyzed, and their performance is compared. The results are also extended for the case of flexible architectures with adaptive topology. The implications of integration are discussed with respect to the overall performance of an organization.     

Delta Function Boundary Operator Method for the Rigorous Solution of the Problem of Scattering from Rough Surfaces

Abstract

A rigorous method for computing the field scattered by a two-dimensional, perfectly conducting rough surface is introduced. This method leads to the calculation of a ψ-distribution which is the integral representation of the impedance operator. This calculation is completed by solving a DBO (delta boundary operator) problem using an integral equation. The ψ-distribution allows one to achieve the solution of a large class of scattering problems by a simple integration, whatever the incident field. Numerical results are shown to be very accurate and it will be seen that the method appears to be very capable of generalization to three-dimensional problems of scattering.