"AUTOMATION, PRODUCTION SYSTEMS, AND COMPUTERINTEGRATED MANUFACTURING, 2ND EDITION, MIKELL P. GROOVER, PRENTICE HALL, 2000, 856 PP., ISBN 0-13-088978-4, LIST: $100." , 1(1-2), pp. 155–156
In this article the mathematical relationship between tolerance design and axiomatic design is established through cost models and axiomatic design principles. Nonlinear optimization methods are used to find the optimum settings of design parameters tolerances. The cost models are served as objective functions that address the tolerance control cost and the variation reduction cost for satisfying the functional requirements. The constraints in the optimization formulation address the requirements for independence, variation control, and design simplicity. The linkage between robust design method and axiomatic design principles is discussed.
This article presents a computerized multi-criteria evaluation system for assembly sequences, which evaluates all feasible assembly sequences based on ease of assembly criteria and finds the most efficient assembly sequence in the Flexible Assembly Systems (FAS) environment. The proposed system, called CASES (Computer-aided Assembly Sequence Evaluation System), automatically evaluates assembly sequences in two stages: (1) Evaluation based on assembly activity performance and (2) Evaluation based on the FAS performance. The first stage of CASES aims to reduce the solution space of all the feasible assembly sequences and finds only promising feasible sequences. The evaluation criteria are based on assembly activity performance such as equipment constraints, ease of part handling, ease of part joining, and the number of tool changes during an assembly process. With the reduced set of assembly sequences, the second stage finds the most efficient assembly sequence in the FAS environment. For evaluation based on the FAS performance, CASES solves a scheduling problem, MFASSP (Modified FAS Scheduling Problem), to find an assembly sequence which provides the minimal makespan. CASES is illustrated with a real assembly.
Abstract There is now greater demand for product customization. For large-scale mechanical product customization, there is great need to develop new methods that will ensure shorter design cycle, shorter time to market, reduced life cycle cost, and higher product quality. The emergence of the Internet makes mass customization possible. It is essential that tools that will support customer participation in product design and realization be developed. The critical tool is an easy-to-use Computer-Aided Design (CAD) system over the Internet where customers' preferences can be captured by functionality-based formalism. A new conceptual design methodology, functionality-based modular design, is presented in this article with the capability of high flexibility and speed, which supports mechanical product customization. A model is developed for modularization to accommodate different system behavior requirements from users of CAD. An XML-compatible language, PML, which has good properties of interoperability, sca...
Uncertainty is inevitable at every stage of the life cycle development of a product. To make use of probabilistic information and to make reliable decisions by incorporating decision maker’s risk attitude under uncertainty, methods for propagating the effect of uncertainty are therefore needed. When designing complex systems, the efficiency of methods for uncertainty analysis becomes critical. In this paper, a most probable point (MPP) based uncertainty analysis (MPPUA) method is proposed. The concept of the MPP is utilized to generate the cumulative distribution function (CDF) of a system output by evaluating the probability estimates at a serial of limit states. To improve the efficiency of locating the MPP, a novel MPP search algorithm is presented that employs a set of searching strategies, including evaluating derivatives to direct a search, tracing the MPP locus, and predicting the initial point for MPP search. A mathematical example and the Pratt & Whitney (PW) engine design are used to verify the effectiveness of the proposed method. With the MPPUA method, the probabilistic distribution of a system output can be generated across the whole range of its performance.
ABSTRACT It is well known that manufacturing cost of robots increase rapidly as the tolerances on the components are made tighter. The allocation of proper tolerances is therefore one of the most important tasks if the finished design is to achieve its intended purpose and yet be economical to produce. In order to minimize the manufacturing process cost, we propose a least-cost tolerance method used at the design stage of robots. In particular, pseudo-boolean programming is used as a method to allocate tolerances to robot parameters. Statistical approach rather than arithmetic approach is adopted in tolerance and error analysis. By Denavit-Hartenberg representation and the first order approximation of differential changes, the position and orientation errors of the end effector are estimated statistically. Techniques to reduce the problem size are also provided. In the proposed model, position and orientation errors of the end effector become constraints to be met. The model is applied to a revolute joint with six degrees of freedom.
Hybrid cellular manufacturing (CM) systems are well represented in manufacturing practice and thus constitute a fertile area of research. This work studies the performance of hybrid CM systems by means of data envelopment analysis (DEA). Since DEA allows for simultaneous analysis of multiple inputs and outputs of a system, it provides for a comprehensive investigation of CM performance under a wide variety of experimental conditions. For the purpose of measuring system efficiency, the number of machines and the degree of set up reduction are used as inputs, and flow time, work-in-progress (WIP) inventory and job tardiness are used as outputs. Results indicate that the hybrid systems in the study perform best at a low to intermediate degree of cellularization.
Abstract Increasingly, designers are asked to consider additional types of requirements, including the environmental impact of their designs during the product’s lifetime and after its useful life. Two of these types of requirements are investigated in this paper. Demanufacturing is the process of dismantling a product and preparing for the disposal or recycling of components, modules, or materials. Product reuse retains a greater portion its value and often requires remanufacturing, the refurbishment of a product or product modules. Assessments of a product’s ease of de- and remanufacture are often desirable at various stages during the design process. In this paper, requirements for CAD representations to support de- and remanufacture assessments are identified. Information requirements are classified into categories according to the difficulty in extracting the information from assembly-based CAD representations. Information not obtainable from CAD representations is noted. Methods for querying CAD systems to extract significant amounts of this information are presented. These methods have been incorporated into the prototypical CAD system CODA. This work is applied to the design for de- and remanufacture of an automotive instrument cluster. It is shown that feasible disassembly sequences can be generated, ideal parts can be recognized, disassembly times can be reliably estimated, and input can be generated for spreadsheet-based assessment tools.
Taguchi-type experimental design and Monte-Carlo simulation are used to perform the operational analysis of industrial robots. The developed models will predict the robot's process capability based on the accuracy and the repeatability errors. A graphical representation of the result is displayed with the measure of performance. There are two major components at the core of this developed system: (1) the ability to define the geometric error and the related kinematics error and (2) the set of charts illustrating the capability of the robot for performing the required tasks. The system is developed in conjunction with a kinematics error model to assist management in making high-risk investment decisions.
GQFD-II (Green Quality Function Deployment II) is an innovative methodology for developing environmentally conscious products. However, GQFD-II has several shortcomings that make it difficult to use. GQFD-II depends on a detailed and time consuming LCA (Life Cycle Analysis) that requires designers to have a comprehensive understanding of environmental science. Further, product comparisons made using GQFD-II rely on a complex decision making algorithm that lacks a coherent quantitative basis. To address these shortcomings, GQFD-III has been developed. In this paper, a methodology for Life Cycle Impact Assessment is integrated into the Green House, and AHP (Analytical Hierarchy Process) is used for selecting the best product concept. Finally, the GQFD-III methodology has been illustrated using an example in which three coffee makers are compared in terms of quality, cost and environmental performance. Further research needs to be done for developing a DSS (Decision Support System) based on the GQFD-III methodology. This can help a design team to concurrently consider the quality, cost, and environmental aspects of products early in the design stages, thereby resulting in reduced product development times.
ABSTRACT It is well documented in complex systems design that changing the design of one subsystem will affect the design of other subsystems. Many times capturing any relationship beyond experience-based heuristic rules is difficult because of the complexity of the disciplinary codes and the associated nonlinearities. In this paper, a probabilistic approach to modeling the effects any subsystem design change has on other subsystems is described. Computer-based simulation is used to build approximations of the interfaces and demonstrate how the necessary assumption of the presence of random error in probabilistic statistics is satisfied. A passenger aircraft example is used to verify and validate the approach.
A Fuzzy Logic Controller (FLC) for dispatching Automated Guided Vehicles (AGVs) in a job shop that operates under a Capacitated Constant Work In Process (C-CONWIP) control policy is presented. The FLC utilizes the available information about the state of the job shop and suggests a real time dispatching decision. The design of the FLC is modular and the controller uses 11 rules. For an automated job shop the proposed dispatching procedure is compared with the Modified First Come First Serve (MFCFS) dispatching rule under various conditions. The simulation results show that the proposed FLC statistically outperforms the MFCFS dispatching rule.
(2001). FACTORY PHYSICS: FOUNDATIONS OF MANUFACTURING MANAGEMENT, 2ND EDITION, WALLACE J. HOPP AND MARK L. SPEARMAN, IRWIN MCGRAW-HILL, 2001, 720 PP., ISBN 0-256-24795-1, LIST: $96. Journal of Design and Manufacturing Automation: Vol. 1, No. 3, pp. 230-231.
This article presents a framework for generating knowledge using computer simulation for problems that are constrained by expert availability and knowledge acquisition. The methodology is developed in the context of sawmill industry. A range of sawmill manufacturing configurations are modeled and analyzed under different operating conditions. The simulation results are used to develop relationships among operating variables and system constraints. Simulation experiments are then used to identify and evaluate solution strategies for handling the constraints. The result is a simulation model with an embedded knowledge base for decision support in sawmill management.
Abstract This article discusses how simulation can be successfully implemented to improve the productivity of newspaper preprint insertion processes. Specifically, the author demonstrates that production time loss induced by excessive queue build-up in a particular insertion process could be drastically reduced if a close-coupled machine layout was adopted in place of a remote-coupled configuration. In the remote-coupled layout, a packaging line is connected to a palletizer via a tray system under the supervision of a control room. In the close-coupled configuration, packaging line and palletizer are directly connected by a simple belt conveyor. The simulation model presented in this application suggests that the close-coupled layout could reduce production time loss induced by excessive queue build up by 95%, depending on the operating conditions.
Abstract Discrete-event simulation is one of the most effective techniques for analyzing a manufacturing system. Unfortunately, little attention is given to using simulation models to estimate the economic impact of a proposed system configuration. This paper defines how activity-based costing (ABC) concepts can be incorporated into a discrete-event simulation model. Special emphasis is on demonstrating how decision making can be aided by having the simulation create a detailed “Bill of Activity” describing costs associated with manufacturing a part. The integration of ABC and simulation is illustrated by evaluating the impact of a proposed manufacturing cell configuration. The additional costing information aids in cell design, determining part sequencing and scheduling, and provides a quick evaluation of product mix changes for a part family.
A Knowledge-Based Interval Modeling (KBIM) Method is introduced for empirical search of the global optimum. The method takes advantage of the a priori knowledge of the system in terms of the linear/nonlinear, monotonic/nonmonotonic, sensitivity information between the objective function/constraints and the system variables and incorporates it in an interval model. This enables the KBIM Method to uniquely interleave model-building and model-refinement in the optimal search process, which makes it ideally suited to cases where exact input-output relationship is not defined. The efficiency of the KBIM Method is enhanced by an on-line learning scheme which improves the accuracy of the interval model after each search iteration by comparing the estimated range of its outputs with the actual outputs. The KBIM method has several advantages over conventional empirical search methods: (1) the interval model provides a generic form of representation for linear and nonlinear problems alike; therefore, there is no need for selecting the form of the empirical model through trial and error, (2) the use of a priori knowledge in modeling eliminates the need for initial trials to construct an empirical model, so from the beginning a plausible region can be identified within the input-space as the basis of search for the global optimum, (3) the use of intervals relaxes the need for precise information, so there is less demand for exploration within the input-space, and (4) the identification of a plausible region early on focuses the search within the plausible region, leading to a more complete model of this region by using the input/output data from the search for learning.
This paper presents a formal method for the process planning of machined components using a statistical methodology that can be used in design to determine the percent defective for feature-based tolerance specifications when used in conjunction with a process planning system or more specifically a process tolerance chart. The paper begins with an approach for process selection and then goes through an illustration of a tolerance specification for hole features under a location specification of Regardless of Feature size. Once a formal process plan has been developed, a probability model is used in conjunction with a process tolerance chart to determine the effects of feature size and location specification on percent defective and product cost. A discussion is presented and the model is expanded to include location specification under Maximum Material Condition and Least Material Condition.
Abstract This article presents a structure of a simulation model of a multicell multiproduct manufacturing system coordinated by Production Authorization Cards (PAC system). The PAC system provides a general framework for management and control of a production process because, through the appropriate choice of parameters, it can be specialized into a wide variety of well-known production policies. The article illustrates how this complex manufacturing control strategy can be simulated using a small number of event routines. The resulting program is fast and flexible enough to be used in other studies as a performance evaluation subroutine in an overall parameter optimization.
The purpose of this paper is to demonstrate the importance of clamping sequences in fixturing processes. We analyze the stability of fixturing prismatic workpieces during different stages of clamping. Sequences of clamping described in this research assure the stability of the workpiece during clamping processes and relax the stringent requirement on the positioning accuracy of placing the clamps
