Modelling flexible manufacturing systems using mean-value analysis

Flexible manufacturing systems (FMS) represent an important new development in automated manufacturing of parts with mid-volume demand. In the design and operation of these complex systems, it is useful to have tools that predict their performance under various conditions. This paper describes one such tool called MVAQ, a computer program based on mean-value analysis of queues. Part production rates, machine utilization and average work-in-process sizes are all easily obtained using MVAQ. The reader is advised on when MVAQ should be used for modelling FMSs and how the program can be used. A simple design example is given to illustrate the use of MVAQ. A brief tutorial on the theory behind MVAQ is also included.     

A constraint programming approach to tool allocation and production scheduling in flexible manufacturing systems

This paper presents a constraint programming (CP) methodology to deal with the scheduling of flexible manufacturing systems (FMSs). The proposed approach, which consists of both a model and a search strategy, handles several features found in industrial environments, such as limitations on number of tools in the system, lifetime of tools, as well as tool magazine capacity of machines. In addition, it tackles the problem in a integrated way by considering tool planning and allocation, machine assignment, part routing, and task timing decisions altogether in the approach. The formulation, which is able to take into account a variety of objective functions, has been successfully applied to the solution of test problems of various sizes and degrees of difficulty.     

Cost performance dynamics in lean production leveling

Balancing of production systems is one of the main lean manufacturing principles as it reduces in-process storage and related forms of waste. A dynamic systems approach is proposed to investigate challenges of implementing production leveling and associated costs. A lean cell producing at takt time is modeled using system dynamics. The model captures various lean tools influencing production leveling and their implications. Comparative cost analysis between various leveling implementation policies for stochastic demand with multiple products is conducted. Results showed that determining the most feasible leveling policy is highly dictated by both the cost and limitations of capacity scalability. In addition, delivery sequence plans of different products/parts needed to achieve mix leveling and lot sizes affect the feasible production leveling policy while implementing lean principles. The developed model and insights gained from the results can help lean manufacturing practitioners to better decide when and how to implement production leveling as well as determine both production lots sizes and sequence. They also emphasize the importance of cost analysis as assisting decision support tool in the trade-off required between the benefits of different levels of lean policies and their associated cost.     

A structure-function-control paradigm for knowledge-based modeling and design of manufacturing workcells

This paper discusses the integration of structural, functional and control knowledge in manufacturing workcell modeling, simulation and design. After an overview of applications of semantic and object-oriented data models in the manufacturing domain, issues relating to the control synthesis for manufacturing workcells are presented. In particular, a data model encompassing functional and control features, along with application domain structural knowledge, is developed. This model assists in explicitly representing the control aspects of engineering design within an object-oriented database and supports a task-level, functionality-driven, manufacturing workcell design. Since manufacturing workcells consist of a number of elements interacting in a complex manner, workcell control design is one of the most difficult steps in the workcell design procedure. Message passage, commonly used in object-oriented databases, provides no explicit modeling of the database behavior. Hence, it can not serve as a tool for the design of system control. On the other hand, Petrinets (PN) have proven successful in describing complex interaction among active agents. This paper will explore the incorporation of Petri nets as a basis for describing application control knowledge within a structure-function-control data model.     

PATHSIM,a modular simulator for an automatic tool handling system evaluation in FMS

The automatic movement of tools, as well as parts, within a flexible manufacturing system is now technically possible. The prospective benefits of automatic tool movement include reduced tool inventories, lower manpower requirements, and an enhanced capability for unattended operation. This report describes a tool for investigating the performance of automatic tool handling systems associated with flexible manufacturing systems.This work uses simulation to describe the prospective systems. In particular, a combined network and discrete event model, written in the SLAM simulation language, is used. The model is of a modular construction, to facilitate its use for different system configurations in the future. The trade-offs between the advantages provided by automatic tool handling and the increased system costs because of the associated hardware are outlined. An illustration of the search for a satisfactory trade-off is made by varying several factors in an example FMS and assessing their effects on system performance.     

A comparison of methodologies for efficient part-machine cluster formation

A comparison of two different models for solving machine cluster formation problems in cellular manufacturing is presented in this paper. The models focus on minimizing total part handling operations evaluated as a weighted sum of inter- and intracell moves. The concept used in the evaluation of intercell moves accounts for the difference between the two models. The utilization rate of active workstations has also been incorporated as an important measure and a targeted minimum utilization of 50% is used in this study. Performance of the models have been evaluated by the application of an example problem which has frequently appeared in published literature. The results indicate one model is significantly more attractive as a decision making tool for use in discrete parts manufacturing.     

Simultaneous parameter and tolerance optimization of structures via probability-interval mixed reliability model

Both structural sizes and dimensional tolerances strongly influence the manufacturing cost and the functional performance of a practical product. This paper presents an optimization method to simultaneously find the optimal combination of structural sizes and dimensional tolerances. Based on a probability-interval mixed reliability model, the imprecision of design parameters is modeled as interval uncertainties fluctuating within allowable tolerance bounds. The optimization model is defined as to minimize the total manufacturing cost under mixed reliability index constraints, which are further transformed into their equivalent formulations by using the performance measure approach. The optimization problem is then solved with the sequential approximate programming. Meanwhile, a numerically stable algorithm based on the trust region method is proposed to efficiently update the target performance points (TPPs) and the worst case points (WCPs), which shows better performance than traditional approaches for highly nonlinear problems. Numerical results reveal that reasonable dimensions and tolerances can be suggested for the minimum manufacturing cost and a desirable structural safety.     

TIREDDX: an integrated intelligent defects diagnostic system for tire production and service

Tire manufacturing is a complicated process due to the number of processing variables involved, dealing with more than 80 raw materials undergoing six main processing steps. Accordingly, the quality of a tire and its efficiency in use depend highly on the raw materials quality and the processing variables. Consequently, in order to properly diagnose a defect (quality parameter) in a tire, the manufacturing history data of that particular tire is essential, with the raw materials inspection results, as the starting point, and going through all manufacturing steps, to the final inspection results and customer claims.This study presents an integrated tire defects diagnostic expert system (TIREDDX) that can be applied during production and service. The main objective of developing a diagnostic integrated expert system is to achieve an integrated diagnostic procedure. The developed system is able to diagnose the probable cause(s) of the defect by tracing the acquired quality and production information at the various steps of tire manufacturing processes, starting from the serial number the defected tire. Implementing such a system significantly reduces the time consumed in tire defect diagnosis, increases the consistency of diagnosing decision-making, and better utilizes the company's management information system. Moreover, it can be regarded as an advisory tool to those having much technical experience and as a training tool for less-experienced personnel those who seek guidance and advice.TIREDDX comprises two main modules: manufacturing history databases and diagnostic expert system. The system was developed and implemented in one of the leading truck tires production companies in Egypt, where satisfactory results were achieved.     

Using views for product data exchange

Proprietary data structures complicate data sharing by making it difficult to save a product model from one software tool and load it directly into another tool. Engineers may want, for example, to verify with an analysis tool that all design constraints have been met or to prepare a design for manufacturing. In such cases, software developers must find a means of moving the product model from the proprietary data structures of a CAD system to those of the analysis system and the manufacturing system, with minimal loss of information. A database view, used in conjunction with data exchange standards, can facilitate the sharing of product model data between software tools in design and manufacturing computing environments     

The EOQ repair and waste disposal model with switching costs

This paper addresses the problem of determining the optimal batch sizes for production and recovery in an EOQ (economic order/production quantity) repair and waste disposal model context. This paper assumes that a first shop is manufacturing new products as well as repairing products used by a second shop. The used products can either be stored in the second shop and then be brought back to the first shop in an approach used to reduce inventory costs, or be disposed outside the system. The works available in the literature assumed a general time interval and ignored the very first time interval where no repair runs are performed. This assumption resulted in an over estimation of the average inventory level and subsequently the holding cost. These works also have not accounted for switching costs when alternating between production and recovery runs, which are common when switching among products or jobs in a manufacturing facility. This paper addresses these two limitations. Mathematical models are developed with numerical examples presented and results discussed.     

A hybrid fuzzy MCDM approach to machine tool selection

The selection of the appropriate machine tools for a manufacturing company is one of the important points to achieving high competitiveness in the market. Besides, an appropriate choice of machine tools is very important as it helps to realize full production quickly. Today’s market offers many more choices for machine tool alternatives. There are also many factors one should consider as part of the appropriate machine tool selection process, including productivity, flexibility, compatibility, safety, cost, etc. Consequently evaluation procedures involve several objectives and it is often necessary to compromise among possibly conflicting tangible and intangible factors. For these reasons, multiple criteria decision making (MCDM) has been found to be a useful approach to solve this kind of problem. Most of the MCDM models are basically mathematical and ignore qualitative and often subjective considerations. The use of fuzzy set theory allows incorporating qualitative and partially known information into the decision model. This paper describes a fuzzy technique for order preference by similarity to ideal solution (TOPSIS) based methodology for evaluation and selection of vertical CNC machining centers for a manufacturing company in Istanbul, Turkey. The criteria weights are calculated by using the fuzzy AHP (analytical hierarchy process).     

Advanced Resource Planning

Advanced Resources Planning hits the bottom of what we know as aggregate planning. This approach differs from other approaches in that it explicitly recognizes the stochastic nature of manufacturing systems. Therefore, it is an ideal high-level tuning and planning tool which can be used in various planning environments like MRP, ERP, JIT, Load-Oriented Planning, Theory of Constraints, Finite Scheduling, POLCA systems, and perhaps many more. The main purpose is to set aggregate planning parameters right before diving into any other operational planning decision. In this sense, we opt to offer realistic lead time estimations, lot sizes, utilization levels, customer service levels and quoted delivery times.The underlying approach is a waiting line network, which is heavily adapted in order to make it useful for planning purposes. The main feature is that both input parameters and output parameters are considered as stochastic variables. In this way it allows us to model manufacturing environments in a more realistic and intuitive way, including all kinds of uncertainty and variability. As a consequence, the output of the planning effort is also a stochastic variable: it has an average, a variance and the entire lead time distribution. The latter makes it possible to obtain high customer service levels or to establish realistic delivery times, which can be met with a high probability.This mathematical approach as such is not suited for people operating a manufacturing system. We illustrate the approach with software, named i-CLIPS, and we review some implementations and their results.     

A generic methodology and a digital twin for zero defect manufacturing (ZDM) performance mapping towards design for ZDM

Over recent years, the manufacturing industry has seen constant growth and change. From one side, it has been affected by the fourth industrial revolution (Industry 4.0). From the other side, it has had to enhance its ability to meet higher customer expectations, such as producing more customized products in a shorter time. In the contemporary competitive market of manufacturing, quality is a criterion of primary importance for winning market share. Quality improvement must be coupled with a concern for high performance. One of the most promising concepts for quality control and improvement is called zero defect manufacturing (ZDM), which utilizes the benefits of Industry 4.0 technologies. ZDM imposes the rule that any event in the production process should have a counter-action to mitigate it. In light of this, the current research developed a methodology the manufacturer can use to correctly select or design appropriate ZDM strategies and equipment to implement at each manufacturing stage. This methodology consists of several steps. The first step is to conduct several simulations using a dynamic scheduling tool with specific data sets to develop a digital twin (DT). The data sets are created using the Taguchi design of experiments methodology. The DT model is created for use in predicting the results of the developed scheduling tool without actually using said tool. Using the DT, multiple ZDM parameter-combination sets can be created and plugged into the model. This process generates ZDM performance maps that show the effect of each ZDM strategy at each manufacturing stage under different control parameters. These maps are intended to provide information for comparing different ZDM-oriented equipment to help manufacturers reach a final decision on correct and efficient ZDM implementation or to assist in the design phase of a ZDM strategy implementation.     

Methodology for rapid identification and collection of input data in the simulation of manufacturing systems

Computer simulation is a well-established decision support tool in the manufacturing industry. The rapid development and deployment of simulation models however, are inhibited by factors such as inefficient data collection, lengthy model documentation, and poorly planned experimentation. Typically, more than one third of project time is spent on identification, collection, validation, and analysis of input data. Whilst most research work has been focused on statistical techniques for data analysis, less attention has been paid to the development of systematic approaches to input data gathering. This paper presents a methodology for rapid identification and collection of input data in batch manufacturing environments. A functional module library and a reference data model, both developed using the IDEF (Integrated computer aided manufacturing DEFinition) family of constructs, are the core elements of the methodology. The paper also identifies the major causes behind the inefficient collection of data.     

Intelligent scheduling in the manufacturing environment

This article is intended to present a model for intelligent scheduling in the manufacturing environment. The model combines an object oriented scheduler with a visual event recognizer to create an interactive visual-event intelligent scheduling tool.     

Integrated product and process designenvironment tool for manufacturing T/R modules

We present a decision-making assistant tool for an integrated product and process design environment for manufacturing applications. Specifically, we target microwave modules that use electro-mechanical components and require optimal solutions to reduce cost, improve quality, and gain leverage in time to market the product. This tool will assist the product and process designer to improve their productivity and enable them to cooperate and coordinate their designs through a common design interface. We consider a multiobjective optimization model that determines components and processes for a given conceptual design for microwave modules. This model outputs a set of solutions that the Pareto optimal concerning cost, quality, and other metrics. In addition, we identify system integration issues for manufacturing applications, and propose an architecture that will serve as a building block to our continuing research in virtual manufacturing applications.     

Integrated process planning using tool/process capabilities and heuristic search

CAD–CAM integration has involved either design with standard manufacturing features (feature-based design), or interpretation of a solid model based on a set of predetermined feature patterns (automatic feature recognition). Thus existing approaches are limited in application to predefined features, and also disregard the dynamic nature of the process and tool availability in the manufacturing shop floor. To overcome this problem, we develop a process oriented approach to design interpretation, and model the shape producing capabilities of the tools into tool classes. We then interpret the part by matching regions of it with the tool classes directly. In addition, there could be more than one way in which a part can be interpreted, and to obtain an optimal plan, it is necessary for an integrated computer aided process planning system to examine these alternatives. We develop a systematic search algorithm to generate the different interpretations, and a heuristic approach to sequence operations (set-ups/tools) for the features of the interpretations generated. The heuristic operation sequencing algorithm considers features and their manufacturing constraints (precedences) simultaneously, to optimally allocate set-ups and tools for the various features. The modules within the design interpretation and process planner are linked through an abstracted qualitative model of feature interactions. Such an abstract representation is convenient for geometric reasoning tasks associated with planning and design interpretation.     

Effect of the identification of key machines in the cell formation problem of cellular manufacturing systems

Cellular manufacturing as been viewed as an important manufacturing philosophy that has helped both small and medium sized parts manufacturers increase their manufacturing productivity. In the analysis of a cellular manufacturing problem, identifying the key machines representing the manufacturing cells is an important step in the determination of final part-machine clusters. Three different methods have been proposed in this paper for performing this identification, and a more complete model taking into consideration the sequence of operations exhibited by parts have been developed for minimizing total moves. The total movefs contributed by all parts have been evaluated as a weighted sum of both inter- and intracell moves. A heuristic solution algorithm developed for the model has been operationalized by implementing the associated computer program on an IBM PC compatible microcomputer. The sensitivity of each of the three proposed methods with regard to identifying the key machines and their impact on the selection of final part-machine clusters has been analyzed by solving an example problem. The results obtained show that method 2 outperforms the other two (methods 1 and 3) in determining the best part-machine clusters in cellular manufacturing, thus making it a better decision tool to be used by both small and medium sized parts manufacturers in production planning.     

An integrated modelling framework to support manufacturing system diagnosis for continuous improvement

This paper proposes an integrated modelling framework for the analysis of manufacturing systems that can increase the capacity of modelling tools for rapidly creating a structured database with multiple detail levels and thus obtain key performance indicators (KPIs) that highlight possible areas for improvement. The method combines five important concepts: hierarchical structure, quantitative/qualitative analysis, data modelling, manufacturing database and performance indicators. It enables methods to build a full information model of the manufacturing system, from the shopfloor functional structure to the basic production activities (operations, transport, inspection, etc.). The proposed method is based on a modified IDEF model that stores all kind of quantitative and qualitative information. A computer-based support tool has been developed to connect with the IDEF model, creating automatically a relational database through a set of algorithms. This manufacturing datawarehouse is oriented towards obtaining a rapid global vision of the system through multiple indicators. The developed tool has been provided with different scorecard panels to make use of KPIs to decide the best actions for continuous improvement. To demonstrate and validate both the proposed method and the developed tools, a case study has been carried out for a complex manufacturing system.