An automatic trajectory planning system (ATPS) for painting robots
is developed. Geometric modeling, painting mechanics, and robot dynamics
are considered to produce an optimal trajectory (in the sense of coating
uniformity and painting time) based on the CAD data describing the shape
of objects. The scheme was implemented on a SUN/4 workstation to develop
an ATPS for painting robots. To test the effectiveness and illustrate
the developed system, numerous experiments were performed and analyzed.
The practical application of the developed system is for planning an
optimal robot trajectory such that uniform coating thickness is achieved
in minimum painting time
An integrated scheme for robotic painting operation is presented
and an integrated robotic painting system (IRPS) was developed. The IRPS
is a unified system in which software and hardware are generically
integrated for robot motion planning and control, and can be viewed as
an extended robot controller containing an automatic programming
feature. In robot motion planning, the IRPS can automatically generate
efficient robot trajectories based on part geometry, painting mechanics
and robot kinematics. The planned and verified robot trajectories can be
executed by the control module of the IRPS. Integrated features
encompassing part description, automatic trajectory planning and robot
motion control, together with a built-in CMM feature, distinguishes the
IRPS from contemporary painting robot systems based on the lead-through
method and from off-line robot programming systems. The architecture and
capabilities of the IRPS, including software and hardware, are
described. The effectiveness of the prototype system is tested and
illustrated by both computer simulation and real experiment
Conventional cost accounting fails to provide manufacturers with reliable cost information due to inability of counting the intangible cost, inaccuracy in calculating overhead, and failure in predicting lifecycle costs. Therefore, manufacturers who consider factory automation have only two choices: either resort to erroneous cost systems only to make wrong decisions, or ignore accounting numbers only to make risky decisions. This paper develops a simulation-based manufacturing accounting (SBMA) model, an updated cost model for modern manufacturing management. Unlike traditional backward and a posteriori accounting, which backtracks historical costs for product costing, SBMA is forward and a priori accounting, which actively traces in real time the dynamic cost drivers (whether tangible or intangible) and predicts lifecycle costs through computer simulation. Therefore, SBMA can make significant contributions to manufacturing performance analysis, capital expenditure analysis, and manufacturing strategy assessment. Further, SBMA can complement existing product costing methods.
Efforts have long been devoted to developing a rapid and accurate method for dimensional inspection in a computer-integrated manufacturing environment. In this research, a general method for enhancing the accuracy of on-machine inspection is discussed. The objective is to develop a reference-part based error modeling algorithm to identify the parameters of positioning error for a machine with minimal measurement. Quadratic and cubic error models based on rigid body kinematics have been developed. The accuracy and complexity of both error models have also been discussed. Experimental results demonstrate the effectiveness of the error modeling algorithm with a ten-fold improvement. This error modeling technique can also be applied for error compensation, thus reducing the errors in material removal process.
This contribution deals with control systems for the electro discharge machining process. Special attention is paid to techniques of inprocess evaluation of the EDM process. The paper focuses on adaptive control systems used to optimize the process performances. Existing adaptive controllers for EDM are surveyed. Economic benefits as well as practical results which could be achieved with adaptive control systems are discussed.
This paper theoretically and experimentally analyzes an existing electrical discharge machine (EDM) servo mechanism and describes modeling of the EDM process. A new EDM servo controller, the self-tuning regulator with P1 control, has been proposed. In this control system, a high-speed computer directly regulates the servo feed rate instead of servo reference voltage, according to the stochastic characteristics of the process. The time ratios of gap states detected and measured by an EDM gap monitor are used as the feedback signal. The algorithm of this control strategy is simpler and has better control performance than many other available EDM servo adaptive control systems. Extensive experiments show that the productivity achieved by this control system is 17% higher than the model reference adaptive controller and 10% higher than the self-tuning regulator, developed earlier by the authors, and almost 25–35% higher than the existing control system on the machine.
Corporations in the US are showing a growing interest in the use of advanced technologies. They realize that to complete in today's world market, they must rely on the latest innovations in manufacturing technology. Techniques such as computer-aided design and computer-aided manufacturing, along with computer-controlled manufacturing equipment, are being introduced into computer-integrated manufacturing (CIM) and production systems. These advanced systems have great potential for improving manufacturing performance, but there is still a considerable gap between the decision to invest and the achievement of the promised benefits. The implementation of CIM normally entails a large initial investment under a long-term, uncertain environment. Many companies, generally, cannot afford this investment unless they can determine beforehand that CIM will work in their particular industry and that it will provide a coherent manufacturing strategy and a satisfactory return on investment. In many cases, it is even impossible to economically justify CIM if the benefits are achieved only after complete implementation.In this paper, we discuss ways to achieve CIM in progressive steps (a pay-as-you-go approach), beginning with a simple configuration and working up to a full configuration, as appropriate. In this way, partial benefits of CIM are reaped with each stage, and, at the minimum risk, successive steps are clearly justified in terms of the firm's objectives. We develop a decision tool that uses post-audit information to resolve the uncertainty about the expected net present value of a technically innovative process. The method was applied to an actual CIM project to document the economic justification steps.
The development of traditional cost accounting (TCA) systems is based on the mass production of a mature product with known characteristics and a stable technology. In particular, the traditional investment decisions in manufacturing are made based on cost accounting data collected for the production setting given, where the cost structure and uncertainty (in demand, actual costs, or delivery times) are assumed exogenous to the system. Recent manufacturing experience suggests, however, that these assumptions—stable product and mass production—are no longer valid for advanced manufacturing systems (AMSs). Thus, manufacturing companies have reduced their dependence on TCA systems by exploring activity-based cost (ABC) systems. Because TCA and ABC systems have different means of handling overhead costs, they may also have differences in their estimates of cash flows with cost accounting data. We will develop costing procedures for various manufacturing activities under ABC systems, and these procedures are incorporated into the proposed multistage investment decision model. Finally, a case study is presented to demonstrate the application of the economic decision model, and we conclude that opportunity costs such as waiting activity and idle activity play more critical roles than the right choice of cost accounting system in justifying investment in an AMS.
For U.S. industries to achieve excellence in world-wide manufacturing, it is important to consider strategic factors (qualitative nature) in conjunction with the traditional analytical methods of economic justification, such as, rate of return, pay back period, etc. However, it is difficult to assess and incorporate the impact of strategic factors in economic decision making, due to their inherent complexity. As part of the economic justification research, an attempt has been made to investigate the difficulties of incorporating the strategic factors in the overall economic justification process. In this paper a general framework for carrying out economic justification of advanced manufacturing systems is presented. The strategic optimization cycle forms the core of the economic justification framework proposed, and it involves four levels: (1) tentative selection of automation levels; (2) qualitative optimization; (3) quantitative optimization, and (4) final verification of the result.Since flexibility is an important evaluation factor in the overall economic justification process for advanced manufacturing systems, it is important to refine the existing method of quantifying flexibility before making any attempt to build the general framework for economic justification. A method of quantifying flexibility is also presented.Hypothetical case studies have been included to illustrate the application of the strategic optimization cycle, and the method of quantifying flexibility. It is believed that the framework for economic justification presented in this paper will lead to an in-depth assessment of the future competitive position of a company, thereby enabling it to increase their market share and volume coupled with significant industrial growth.
In a dynamic job shop, where jobs of various types enter and leave the production system continually in a random manner, completion time of a job is affected by many factors related to job characteristics and shop status and is very difficult to predict accurately. Also, the level of influence of each factor on the completion time of a job may depend on the production system characteristics, such as the dispatching rule used and shop utilization rate. Discussed in this paper are a method to identify factors that have significant effects on completion times of jobs in a job shop and a simulation study of the relative effects of these factors on performances of due-date predictability under various production conditions. The results indicate that the prediction capabilities of the due-date assignment rules, which are developed based on those significant factors identified, are significantly different, and the relative performances of them are affected by the dispatching rule used and shop utilization rate.
This paper describes various multi-agent architectures, including the heterarchical architecture. It reviews the claimed advantages for multi-agent heterarchies and describes the types of factories that could use this architecture. It surveys the three common types of factory control algorithms: dispatching algorithms, scheduling algorithms, and pull algorithms. It then asks the question: which of these algorithms can be implemented in a multi-agent heterarchy? This paper describes how all common factory control algorithms used in industry can be implemented in a multi-agent heterarchy. It discusses how many of the algorithms that are popular in current research can be implemented in a multi-agent heterarchy, while others will require further research.
The accuracy of an autonomous AGV in following a predetermined path is highly dependent on its navigational capability. Position recognition requires integrating communication, mapping, guidance, and navigation. The hardware and software development for such a system is presented. Aspects of the system's interaction are discussed and specific design attributes are evaluated and highlighted.
In this paper, we consider incorporating within department flows in formulating the problem of determining the pick-up and delivery stations in the design of single-loop AGV systems. We first show the impact of different locations of P/D stations by simulation. We then propose a model for determining optimal locations of P/D stations, which accounts for between and within department flows. We denote this procedure as the centroid projection method since material flow is assumed to occur between department centroids projected to the nearest aisle segment bounding each department. The model assumes the transportation cost ratio of the within and between department flows is known.
This paper describes a computer-aided process planning system for electrical discharge machining (EDM). Input to the system is a workpiece description based on process planning features. New process planning feature types have been defined because existing standards such as the CAM-1 feature catalog only describe features for prismatic parts. Each feature is characterized by a generic process plan. Generic process plans define possible manufacturing operations that can be applied to a feature. The developed process planning system evaluates the generic process plans of all features included in the workpiece description and combines them to an optimal process plan based on minimal cost. A general strategy to estimate EDM machining times is presented.
In this paper, we extend the domain of computer-aided process planning to include a new and powerful class of machine tools. A unique feature that characterizes these machine tools is their ability to perform machining in parallel. In particular, multiple tools can simultaneously machine a single workpiece and multiple workpieces can be machined simultaneously. Such machines can finish a part in a single setup resulting in shorter cycle times. These machines can also provide higher machining accuracy by drastically reducing the potential for tolerance buildup. We introduce and formalize a concept for parallel machines that can be extended to include machines more powerful and capable than the ones being used today. Next, we study the effects of parallelism on computer-aided process planning by identifying and examining some new issues that arise in the parallel machining domain. Finally, we propose and discuss a framework for a computer-aided process planning system in which these issues are incorporated.
Computer aided process planning (CAPP) forms the critical bridge between computer aided design (CAD) and computer aided manufacturing (CAM). Generating consistent and flexible process plans to suit a dynamic production environment is an important objective. Geometric tolerancing and dimensioning when properly applied ensures the most economical and effective production of part features. This can provide a functional production technique by which the engineering drawing specifications are translated into production instructions by means of a process plan. Most of the process planning systems currently available do not generate alternate process plans. Process planning is a decision making task whose decisions should take into account the production control objectives. Any fixed sequence of operations that is generated by a process plan is not necessarily the best possible sequence since the production environment changes as do the criteria for optimality (such as quality, quantity, machine utilization, etc.). The aim then should be to generate all the possible feasible operation sequences. The decision maker can then use the necessary optimality criteria to get the best sequence for the prevailing operating environment. A methodology is presented in this paper for the generation of alternate feasible operation sequences using a tree structure approach.
This paper is concerned with minimizing the sum of the tool setup and volume removal times associated with metal cutting operations on a flexible machine. The problem is of interest to most repetitive manufacturers because any reduction in processing time translates directly into cost savings. Findings optimal solutions, though, has proven quite difficult for manufacturing engineers due to the large number of cutting path and tool combinations that may be selected.The problem is modeled an as integer program but transformed by relaxing some of the constraints into one of finding a minimum cut on a simple network. After obtaining a tentative solution at this step, an efficient, but suboptimal, procedure is used to generate alternative process plans. These are seen to speed convergence during branch and bound. Overall performance is judged by examining a wide variety of test problems derived from actual manufacturing data.
This paper presents an overview of a high level planning and control system (HLPCS) designed to permit CAD/CAM integration of the manufacturing processes of a generic airframe manufacturing facility. This analysis was performed in part under the direction of the United State Air Force Integrated Computer Aided Manufacturing Program (project priority 6104). The analysis was conducted through the design/communication tool of IDEF0 (ICAM definition language). This paper will therefore rely heavily on discussions utilizing IDEF0. The concepts of the HLPCS will be discussed, the aspects of the system enhancements that encourage integration will be highlighted, and conclusions regarding system improvements will be identified.
Three components of a machine cell formation process—similarity coefficients, clustering algorithms, and performance measures—are studied. A new performance measure is introduced and a comparative study of three different similarity coefficients—the Jaccard's similarity coefficient, weighted similarity coefficient, and commonality score—is conducted.
Workpiece localization plays an important role in automation of many manufacturing processes, such as workpiece setup, refixturing, dimensional inspection, and robotic assembly/manipulation. This paper provides unified treatment of three geometric algorithms for workpiece localization. Local convergence of these localization algorithms is shown, and new techniques are developed to make these local algorithms globally convergent. A method is presented for analyzing the reliability of localization solutions. Along with extensive simulation results, the performance of the algorithms is studied and analyzed in terms of accuracy, convergence, and computational efficiency. The Hong-Tan algorithm has better accuracy and computational efficiency than two other algorithms tested. Finally, the experimental implementation of the algorithms is performed, and the experimental results are presented to confirm the proposed global convergence method and the reliability analysis method.
In this paper we consider the single machine earliness/tardiness scheduling problem with di?erent release dates and no unforced idle time. We present several heuristic algorithms based on the beam search technique. These algorithms include classical beam search procedures, with both priority and total cost evaluation functions, as well as the filtered and recovering variants. Both priority evaluation functions and problem-specific properties were considered for the filtering step used in the filtered and recovering beam search heuristics. Extensive preliminary tests were performed to determine appropriate values for the parameters used by each algorithm. The computational results show that the recovering beam search algorithms outperform their filtered counterparts in both solution quality and computational requirements, while the priority-based filtering procedure proves superior to the rules-based alternative. The beam search procedure with a total cost evaluation function provides very good results, but is computationally expensive and can therefore only be applied to small or medium size instances. The recovering algorithm is quite close in solution quality and is significantly faster, so it can be used to solve even large instances.
This paper presents the concept of Manufacturing DEcision MAking (MADEMA)—a multicriteria decision-making procedure for manufacturing systems. The concept is based on a hierarchical structure of the manufacturing system and calls for the execution of four consecutive steps to make decisions on the assignment of manufacturing resources to production tasks. MADEMA utilizes decision theory in tandem with randomized search methods and has been experimentally implemented on a software system written in LISP. Particular emphasis is given to its flexibility in terms of the decision-making criteria used and the tradeoff between system performance and computational effort. This flexibility permits adjustment of the procedure to suit the available computational environment and the required decision making time. This paper includes preliminary simulation results of applying MADEMA to a real life manufacturing workcenter.
Robots are sharing the manufacturing work environment with humans at an ever increasing rate. As such it has become imperative to analyze the manufacturing tasks and allocate them properly between humans and robots. A systems approach to task allocation has been taken in this paper that includes inventory of anticipated common tasks in manufacturing, design of products to be manufactured, allocation of tasks between humans and robots, and iterative improvement in product design.
The paper presents an alternative replacement approach to Terborgh's MAPI procedure. Presented in the format of a case study, this illustrative example incorporates considerations on different machine types, factors of production, expansion of output, finite planning horizon, and the 1983 Accelerated Cost Recovery System (ACRS) depreciation schedule along with other factors traditionally considered. A solution procedure is also provided to determine the optimal replacement policy; the criterion is the maximization of the production process' future worth at the end of the planning horizon. A general literature review is included.
One of the major issues in controlling a flexible manufacturing system (FMS) is the handling of interruptions due to machine breakdowns and rush orders. These interruptions may or may not require revisions in the manufacturing plan. By minimizing the effects of interruptions, supervisors can achieve more effective control and increase the utilization of an FMS. However, the control problem in an FMS is inherently complex and difficult to solve. Furthermore, the total time spent in analyzing interruptions, evaluating several alternative control policies, and choosing the best decision becomes critical. This issue is even more critical if on-line control is of interest. This paper describes a new integrated system that has been designed, implemented, and tested for analyzing and handling interruptions in an FMS. The design utilizes knowledge-based on-line simulation concepts. The implementation is restricted to two types of interruptions—machine breakdowns and rush orders. The recommended control decisions affect the original loading and scheduling of parts and machines in the FMS. Several experiments have been conducted to evaluate the performance of the implemented system. A case example is presented to illustrate the applicability and validity of the new system.
In the apparel industry, cells for manufacturing and assembly are called modules. A simulation model has been developed to study the behavior of a unique module that employed five multifunctional, walking workers and 13 sewing stations with great variations in processing times. Cells are the major components in integrated pull manufacturing systems (IPMSs). IPMSs are the designs for the factories with a future.
One problem that has often been overlooked in research in flexible manufacturing systems (FMSs) design and operation is deadlocking. An FMS deadlock is a situation where machines have been allocated parts so that further part movement is inhibited. In our earlier paper, a procedure to detect deadlocks was presented. In this paper, two approaches to resolve deadlock problems, namely avoidance and recovery, are presented and analyzed. These approaches can be used to avoid or resolve deadlocks during active control of the FMS. A simulation study to compare deadlock resolution approaches to conventional approaches for avoiding deadlocks is also presented. Results of the analysis are presented.
Total quality management for the product lifecycle requires integrating quality control systems with product development, production, and support systems. Integrating automated inspection with advanced computer manufacturing systems components greatly enhances the improvement of products and processes. Presented is an approach to integrate inspection systems with automated manufacturing systems. This step completes the computer-integrated manufacturing loop. Described is an architecture to integrate quality considerations with conventional product characteristics. Elements of an operational environment for this approach are described, along with a framework and functional components that fit in the framework. The framework and the functional components form an architecture called the Expert Programming System-One (EPS-1). An example illustrates the operation and functioning characteristics of the EPS-1.
The use of expert systems in manufacturing is steadily increasing. The majority of these systems are designed to perform process control or to diagnose process malfunction. Though quality control is generally not an explicit development goal, a number of manufactures have realized quality improvements from the use of expert systems. The premise of this paper is that, instead of accepting quality improvements as a windfall from the development of intelligent systems, manufacturers could benefit from expert systems designed to support quality management activities. General object-based architectures for expert systems developed from the principles of two proven quality management methods—Quality Function Deployment and Quality Planning—are proposed.
While there have been several control architectures proposed, a framework that identifies the relationships between such architectures has been missing. The purpose of this paper is to trace the evolution of control structures for automated manufacturing systems (AMS), identify the relationships between the architectures, and identify key design decisions that are affected by each type of control structure. AMS control architectures are defined, as are their importance as a critical design decision for automated manufacturing systems. The demands that automated manufacturing make upon a control architecture are discussed. Subsequently the four basic control architectures representing an evolution of design are presented. The characteristics, advantages, and disadvantages of each of the basic forms are reviewed. Examples are presented to highlight these attributes. The paper concludes with projections on possible future directions of control architectures.
This paper reviews important theoretical and practical developments in job shop control. The distinguishing feature of this paper is the identification and summary of important concepts and procedures useful for incorporation into computerized job shop control systems. The first section briefly examines the job shop control problem, and presents a broad outline of commonly used subproblems and methods. A review of the past work done in the areas of scheduling and sequencing, workload balancing, work flow structure analysis, and job shop capability evaluation is then presented. The final section lists important concepts and procedures for developing computer integrated job shop control systems.
This paper describes ART1 neural models for GT part family and machine cell forming. An ART1 neural model was first implemented in C and was tested with examples taken from the literature. The ART1 model was then integrated with a feature-based design system for automatic GT coding and part family forming. It was finally incorporated into a three-stage procedure for designing cellular manufacturing systems. Our evaluation concludes that ART1, when compared with nonlearning algorithms, is best suited for GT applications due to its fast processing speed, fault tolerance and learning abilities, ease of classifying new parts, etc.
This paper discusses research which has led to a working program based on artificial intelligence techniques for automatically writing a part program for milling. The paper discusses the approach used and gives details of the implementation. The input to the program is the graphic representation of the part (a drawing), and user-defined items such as tool details, material type, and so on. The program has an initial state, the shape of the raw material, and a goal state, the shape of the part. The program solves the problem of achieving the goal state from the initial state by using machining moves, and hence writes the part program. The current implementation produces a part program for a 2 part on a 3-axis CNC milling machine.
A simulation approach is used to evaluate the performance of an automobile welding assembly line that requires modification to achieve integrated manufacturing. The simulation of the existing line using GPSS/H confirmed operational problems previously identified and allowed validation of the computer model. The computer model was then altered to evaluate performance of several alternative modified assembly lines that would avoid the present operational problems. This paper discusses techniques to develop these models and the benefits of applying simulation in analyzing manufacturing systems.
Traditionally, fixtures have been required to be redesigned and remanufactured for every new product or manufacturing operation. Although this kind of fixturing practice might be economical for mass production, it would cause long lead times and high (fixture) manufacturing costs in small batch production. In today's flexible automation approach to small batch production, reconfigurable fixtures can play an important role in addressing these two concerns.The objective of this research was to develop a reconfigurable fixturing system for robotic assembly. The required features for such a system were in turn set as: modularity, automatic reconfigurability, sensory feedback controllability, and programmability.The modular fixture component designs which were developed include horizontal and vertical locators, a V-block, horizontal and vertical clamps, and a hole type baseplate on which these components can be reconfigured. All the components incorporate a standard sensing scheme. The main features of the developed fixturing system were verified through the manufacturing and testing of a prototype, which was interfaced to a personal computer.
The flexible, automated, high speed assembly of printed circuit (PC) boards offers productivity gains over hard automation. While significant progress has been made to automate the component insertion on PC boards, there still remains a class of components, commonly referred to as ‘odd-form’ components, whose handling has posed significant constraints on the flexibility of automation designs. This paper presents conceptual designs for the storage and feeding of such odd-form components to facilitate their automated assembly.Moreover, the problem is made more difficult because the component storage and feeding system technology has lagged behind the development of flexible assembly systems that it supports. In this paper, different design alternatives for the component storage/feeding system and its integration into an overall flexible assembly workstation are presented and quantitatively analysed. With appropriate software control and sensing techniques, these designs will enable a large variety of components to be assembled on a number of different types of PC boards, facilitating flexible, automated assembly at high speeds.
This paper presents a prototype feature-based fixture planning system for flexible assembly. Fixture process planning prepares the fixture clamping position and orientation for the base part. In a heuristic way, a group of guidelines were generated to help find the feasible solution for automated fixture planning based on the geometries of the workpiece and the nature of the selected fixtures. By representing the geometric and nongeometric properties of fixtures and workpieces in terms of features, a fixture process planning system was implemented using a knowledge-based approach. A rating technique for feasibility evaluation was used to include all the active facts into an optimal solution. Through this analysis, a product can be evaluated for its manufacturability, cost, and cycle time at the product design stage.
Automated assembly lines are subject to unexpected failures, which can cause costly shutdowns. Generally, the recovery process is done “on-line” by human experts or automated error recovery logic controllers embedded in the system. However, these controller codes are programmed based on anticipated error scenarios and, due to the geometrical features of the assembly lines, there may be error cases that belong to the same anticipated type but are present in different positions, each requiring a different way to recover. Therefore, robustness must be assured in the sense of having a common recovery algorithm for similar cases during the recovery sequence.The proposed approach is based on three-dimensional geometric modeling of the assembly line coupled with the genetic programming and multi-level optimization techniques to generate robust error recovery logic in an “off-line” manner. The approach uses genetic programming's flexibility to generate recovery plans in the robot language itself. An assembly line is modeled and from the given error cases an optimum way of error recovery is investigated using multi-level optimization in a “generate and test” fashion. The obtained results showed that with the improved convergence gained by using multi-level optimization, the infrastructure is capable of finding robust error recovery algorithms. It is expected that this approach will require less time for the generation of robust error recovery logic.
Mismatches between sampling time requirements and production cycle times have automated inspection processes and represent a substantial bottleneck in automatic assembly systems (AASs). Consequently, indirect ways must be developed to assess average outgoing quality in order not to slow the entire line down to the speed of the test process. A clear tradeoff exists between the average outgoing quality limit (AOQL) and the production rate in AASs. In this article, these tradeoffs are analyzed with particular emphasis on the new trends in moving the quality upstream into the products and processes. A simultaneous investigation of product quality and optimal buffer designs is made. Modeling and Monte Carlo optimization approaches involving discrete event simulation and stochastic quasigradient methods are used to carry out the analyses.
This paper presents a structured approached for analyzing the effects of test and rework operations on material flow in systems that assemble circuit cards. Stages that effect quality (e.g., vendor preparation of components, pooling, assembly processes, and test and rework) are identified and representative models of each are presented. Each product is treated as a composite of multiple components, each of which is vulnerable to a variety of defects. Test is treated as fallible and rework as imperfect. Several procedures that accomplish rework are considered. Examples demonstrate some fundamental effects of test and rework operations on material flow. Management implications for improving material flow are discussed.
Manufacturers of large products are increasingly considering the use of flexible assembly systems to improve the efficiency of their assembly operations, especially where many different products are assembled on the same assembly line. The assembly of large products usually involves some large component parts. The complications of mating such components with the partially assembled unit must be considered in the design and the operation of the flexible assembly system. In this paper, we present results of a simulation study that was motivated by a major automobile company. The study investigates capacity planning issues (number of machines and buffers) and scheduling strategies to facilitate the component mating process.
Workpiece positioning and constraining is an important factor in manufacturing processes such as machining, welding, bonding, and assembly. Jigs and fixtures are devices employed to position and constrain the workpiece in a desired location to ensure that it is in a state of static equilibrium and that dimensional accuracy is maintained during the manufacturing operation. Traditionally, fixtures have been designed and manufactured as single-purpose devices for specific parts and manufacturing operations. The traditional approach is costly due to the long load time and effort required to design and manufacture special-purpose fixtures, manual set-up and change of fixtures, and overhead cost associated with storing and retrieving a multiplicity of fixtures. Thus, the traditional approach is not suited to the flexible manufacturing environment where production volume is low and product variation is high. If flexible manufacturing systems (FMSs) are to be truly flexible, then the fixturing must also be flexible.This paper presents requirements, design principles, design rules, and a prototype system for flexible fixtures in robotic assembly. Such a fixturing system would employ a number of fixture modules that are set-up, adjusted and changed automatically by the assembly robot without human intervention. The design of the fixture layout is performed interactively on a commercially available computer-aided design (CAD) package. The guidelines for development of a dedicated software program to automatically generate robot program for setting up, adjusting, and dismantling the fixture, using the information retrieved from the CAD database, is presented.
Fixturing configuration design and analysis is a major concern in the development of automated manufacturing process planning systems. Provided here is a powerful tool to generate optimal fixturing locations for prismatic parts in automated assembly. An integrated approach ensures that the generated fixturing scenario secures the workpart ideally with respect to maximum stiffness, resistance to slip, and stability. An optimization model is built on the foundation of kinematic, force, and robotic grasp analyses. Prismatic parts held in a fixture are considered. Objective functions for rotational and translational perturbations are introduced and analyzed. The optimization is constrained by the kinematic feasibility of the fixturing arrangement. The developed model is especially applicable for computer-integrated manufacturing process planning systems.
A system of algorithms is presented for material removal simulation, dimensional error assessment, and automated correction of five-axis numerically controlled (NC) milling tool paths. The methods are based on a spatial partitioning technique that incorporates incremental proximity calculations between milled and design surfaces. Hence, in addition to real-time animated five-axis milling simulation, milling errors are measured and displayed simultaneously. Using intermediate error assessment results, a reduction-of-intersection-volume algorithm is developed to eliminate gouges on the workpiece via tool path corrction. A similar technique is implemented for detection and elimination of unexpected collisions between the tool assembly and the workpiece. These combined algorithms provide efficient, accurate, and automatic assessment and correction of five-axis milling tool paths. Finally, the view dependency typical of previous spatial partitioning based NC simulation methods is overcome by a contour display technique that generates parallel planar contours to represent the workpiece, thus enabling dynamic viewing transformations without reconstruction of the entire data structure.
The Operations Assistant (OA) is an on-line, interactive decision support system designed to help users with the management of a manufacturing facility. It provides information on the current state of the facility and products, along with projections on the fature states of the facility and progress of products. It generates a graphical view of data and the necessary information to make decisions on product entry sequence, work-in-process reduction schemes, labor/ machine/shift assignments, machine maintenance schedules, and capacity/utilization issues. It contains a number of modules which have the capability of suggesting decisions, and these can be exchanged or supplemented for a particular application. The present OA prototype provides two graphical views; the Product View and the Process View. The operating strategy of the line can be modified interactively, and the consequences of the proposed change (as projected by simulation) seen immediately on these views. The user can also ask for suggestions in making operating decisions, using one of several optimization or heuristic algorithms. OA is highly modular and not specific to any one algorithm—other resource management algorithms can be easily added.
The large, potential savings offered by intelligent manufacturing system technology may be achieved only through proper design and implementation of such systems. While current model-based analysis tools are capable of providing accurate estimates of system performance, the ultimate success of a design procedure depends heavily upon the knowledge and experience of the designer. In this paper, a knowledge-based design procedure, which aids the design process and encourages experimentation, is proposed to reduce this dependence. This design procedure utilizes expert systems to explicitly provide design recommendations based on performance results from model-based analysis tools. A prototype intelligent manufacturing system design tool was developed to show both the feasibility and potential of the design procedure.
The demand for better customer service and the impact of data processing have forced management to look more closely at warehousing automation. Automated storage and retrieval systems (AS/RSs) are the focus in the revolution of warehousing automation.This article investigates the feasibility of using the stochastic Petri net technique to model AS/RSs. A Petri net is a graphics-based tool suitable for modeling manufacturing systems, computer systems, biological systems, etc. It can be used to model a system at different levels of abstraction. A Petri net graph of an AS/RS leads to the generation of a reachability table. The reachability table is, in turn, converted to a state table representation of a Markov process model of the AS/RS. The efficiency, control rules, bay assignment, and many other performance issues can then be studied using the Petri net model.This paper introduces Petri nets as alternative tools for the analysis of AS/RSs. To this end, a discussion on Petri nets, analytical approaches, and simulation is provided. The unique features and flexibilities of Petri nets are presented. Some important characteristics of AS/RSs such as concurrency, conflict, and deadlocking can be described and modeled using Petri nets. Possible extensions to this study are also discussed.
The Automated Manufacturing Research Facility is being constructed at the National Bureau of Standards. This small, integrated, flexible manufacturing system will serve as a research test bed to aid in the identification, design, and testing of standards for the automated factory of the future. This paper describes the five layer hierarchical production control model proposed to manage these factories. Included is a discussion of the philosophy behind this model, the functional requirements of each layer within the model, a brief description of the data services needed to support this approach, and an overview of the techniques used to implement existing subsystems.
It has been well documented that companies can attain significant competitive advantages through advanced manufacturing technologies such as flexible manufacturing systems, computer aided design, and robotic systems. Yet, it is equally well known that many companies are reluctant to install these technologies, and those who do frequently are not reaping the advantages the technologies can offer, largely because of the difficulties of implementing these expensive, complex systems.Some of the major difficulties in implementing the automated factory, sometimes referred to as the factory of the future, are addressed here. A generic implementation plan is formulated and the major impediments are described. These include an assessment of the firm's readiness for automation, conducting the “as is” study, and attending to the automation infrastructure and organizational interfaces.
Much has been written about the use of multiattribute decision models to evaluate manufacturing technologies. This article describes an application to a real problem using a recently developed multiattribute decision tool. The case study involves an evaluation of three candidate technologies for filling packaged food containers. A manual baseline filling method is compared with the two technology investment options of automated weigh filling and automated volumetric filling. In food industries, the cost of material overfill is important but is not the only factor that should be considered. A multiattribute decision model is formulated that considers difficult-to-quantify criteria, such as material conversion, information conversion, and strategic activities. For the given decision scenario, when only directly measurable annual cost savings are considered, the weigh filler option is preferred; however, when the additional criteria are considered, volumetric filling is preferred primarily due to its product and equipment flexibility. It is proposed that the methodology described here will enhance the acceptability of applying multiattribute decision making to manufacturing investment problems.