Structured adaptive supervisory control model and software development for a flexible manufacturing system

A three-step approach is proposed to substantially reduce the high cost of development of control software for an FMS on the shop floor. First, by introducing the concept of multiple computing processes into Automata Theory, a finite capacity machine (FCM) is presented as an extended finite machine. An FCM is used to model the control of a flexible manufacturing system, which avoids the state explosion problem. Secondly, a structured adaptive supervisory control (SASC) model meeting the desired control objectives for an FMS is derived from a composed FCM. The SASC model can be constructed systematically. Finally, generic software components for an SASC model are described, which allow the SASC model to be cost-effectively transferred into shop-floor applicable control software.     

Virtual cell formation

The concept of a virtual cellular manufacturing system (VCMS), which operates very much like a traditional cellular manufacturing system (CMS), has attracted considerable attention in recent years. Unlike traditional cellular manufacturing systems, virtual cellular manufacturing systems are most suitable in production environments that experience frequent product mix changes. Whereas, in traditional CMS, the shop floor configuration is fixed, in VCMS the shop floor configuration changes in response to changes in product mix over time. Therefore, the life of a given shop floor configuration continues as long as the product mix remains relatively unchanged. Furthermore, whereas in traditional cellular manufacturing systems, a machine cell occupies a contiguous region of the shop floor, the same is not necessarily true with virtual cells. Virtual manufacturing cells are simply logical cells in which the machines belonging to the same cell need not occupy the same contiguous area. Because cell members are logical instead of physical, machines in a cell can be at any location on the floor. In this paper, we present a methodology for designing virtual manufacturing cells.     

DESIGN OF A KNOWLEDGE-BASED ON-LINE SIMULATION SYSTEM TO CONTROL A MANUFACTURING SHOP FLOOR

In this paper, the design principles of a new knowledge-based on-line simulation (KBOLS) architecture for the purpose of integrating the supervisory decision making process with a shop floor control system is discussed. The requirements for implementing the KBOLS architecture in a computer integrated manufacturing environment are presented. The special features of the KBOLS architecture include a knowledge-based controller capable of interacting with the shop floor and a manufacturing simulator, a shared blackboard data structure for knowledge bases, and a learning module. The KBOLS architecture is implemented in a flexible manufacturing system for analyzing interruptions due to machine breakdowns and rush orders.     

Multi-agent hybrid shop floor control system

A manufacturing system is composed of many activities that can be monitored and controlled at different levels of abstraction. A shop floor can be considered an important level at which to develop a Computer Integrated Manufacturing system (CIM). However, a shop floor is the dynamic environment where unexpected events continuously occur, and impose changes to planned activities. To deal with this problem, the shop floor should adopt an appropriate control system that is responsible for the coordination and control of the manufacturing physical flow and information flow. In this paper, a hybrid control system is described with a shop floor activity methodology called Multi-Layered Task Initiation Diagram (MTD). The architecture of the control model identifies three levels; i.e. the shop floor controller (SFC), the intelligent agent controller (IAC) and the equipment controller (EC). The methodology behind the development of the control system is an intelligent multi-agent paradigm that enables the shop floor control system to be an independent, autonomous, and distributed system, and to achieve an adaptability to change the manufacturing environment.     

Using finite state automata (FSA) for formal modelling of affordances in human-machine cooperative manufacturing systems

Modelling complex systems poses significant challenges on how one represents the system components and interactions among them. In order to provide a systematic approach to represent human participation as a part of a dynamic system, this paper presents a formal automata model of human-machine cooperative systems that incorporates human capabilities with respect to system conditions. Specifically, we propose a control model for human-involved shop floor systems based on discrete event-based systems (DES) and an environmental concept known as an affordance. When modelling human-involved systems where a human operator is considered a crucial system component, it is necessary to analyse the model complexity that increases significantly due to a human's behavioural patterns. From the perspective of the temporal and physical state domains a human operator's behaviour is usually limited by attention and resource constraints. We investigate these limitations and map them into constrained system affordances, and then construct a formal human-machine cooperative model based on the finite state automaton (FSA) model. The proposed model can provide a framework to combine human activities into systems operations in consideration of human's effectivities and system affordances. A detailed application example is provided to illustrate that the proposed model can effectively be applied to manufacturing control systems.     

Effectivity date analysis and scheduling

The impact of design on logistics cannot be ignored, and design for logistics is a new concept similar to design for manufacturing or design for assembly. Engineering change is one of the scenarios that would require logistics support. Change control of a product data management (PDM) system is one of the major approaches for handling engineering changes today. According to principles of configuration management, during the change control workflow, there are three different dates: release date, effective date, and effectivity date utilised for controlling and managing change planning and scheduling. Effective date is the exact date that a released change takes effect to the shop floor workshop. Effectivity date is the expected date that decision makers plan for the change to take effect. In normal situations, multiple disciplines, such as design and development, purchasing, shop floor workshop, quality control, and so on, are involved in making a change decision on when a change is to become effective. In this paper, a linear programming effectivity decision model is proposed to concurrently support changes of design scheduling, and production planning and scheduling when an engineering change occurs. The proposed model succeeded in solving an integration problem of design scheduling, production planning and shop floor scheduling.     

A formal control-theoretic model of a human–automation interactive manufacturing system control

This paper describes a human–automation interactive manufacturing system and presents a formal model for describing and controlling the system. The model presented in this paper considers a system from two perspectives: structural and operational perspectives. Human and an automated controller are considered agents that cooperate to achieve given goals by executing assigned tasks. A human–automation interaction is described with a particular communication model between two agents that exchanges messages. A system control schema and human tasks are modelled separately and then integrated in a formal manner using a modified communicating finite state machine framework. An interface model that coordinates the message exchanges between two agents is then introduced. An automated shop floor control system with a human material handler is modelled with the proposed framework and a simple illustrative example is provided.     

Simulation-based shop floor control: formal model, model generation and control interface

In this paper, a structure and architecture for the rapid realization of a simulation-based real-time shop floor control system for a discrete part manufacturing system is presented. The research focuses on automatic simulation model and execution system generation from a production resource model. An Automatic Execution Model Generator (AEMG) has been designed and implemented for generating a Message-based Part State Graph (MPSG)-based shop level execution model. An Automatic Simulation Model Generator (ASMG) has been designed and implemented for generating an Arena simulation model based on a resource model (MS Access 97) and an MPSG-based shop level execution model. A commercial finite capacity scheduler, Tempo, has been used to provide schedule information for the Arena simulation model. This research has been implemented and tested for six manufacturing systems, including The Pennsylvania State University CIM laboratory.     

虚拟车间动态重构模型的研究

阐述了敏捷制造模式下，车间的功能除了具有常规的生产管理和控制功能外，还具有实现单元动态重构的功能和通过网络对外合作的功能。在分析了虚拟车间动态重构的系统的关键技术的基础上，提出了基于多代理机的虚拟车间动态重构模型，讨论了Ａｇｅｎｔ之间的协商机制。     

INTEGRATED SHOP FLOOR CONTROL USING AUTONOMOUS AGENTS

In this paper, we present a generic framework for controlling the work flow in computer controlled manufacturing systems. Based on a market-like model and a combination of objective and price mechanism, the framework allows heterogeneous job objectives, admits job priorities, recognizes multiple resources types, and allows multiple step negotiation between parts and resources. The framework is designed to accommodate frequent changes in the environment such as machine failures, tool shortages, and process requirement variations. An object-oriented simulation system is built to demonstrate the flexibility and effectiveness of die proposed framework. The results show that the proposed framework provides a foundation for highly adaptive, real time shop floor control.     

Agile manufacturing: Enablers and an implementation framework

Tougher competitive situations have led to increasing attention being paid to customer satisfaction, of which timely and customized services are the key concepts. As the product life cycle becomes shortened, high product quality becomes necessary for survival. Markets become highly diversified and global, and continuous and unexpected change become the key factors for success. The need for a method of rapidly and cost-effectively developing products, production facilities and supporting software, including design, process planning and shop floor control system has led to the concept of agile manufacturing. Agile manufacturing can be defined as the capability to survive and prosper in a competitive environment of continuous and unpredictable change by reacting quickly and effectively to changing markets, driven by customer-designed products and services. This article details the key concepts and enablers of agile manufacturing. The key enablers of agile manufacturing include: (i) virtual enterprise formation tools/metrics; (ii) physically distributed manufacturing architecture and teams; (iii) rapid partnership formation tools/metrics; (iv) concurrent engineering; (v) integrated product/production/business information system; (vi) rapid prototyping tools; and (vii) electronic commerce. A conceptual framework for the development of an agile manufacturing system and future research directions are presented in this paper. This framework takes into account the customization and system integration with the help of business process redesign, legal issues, concurrent engineering, computer-integrated manufacturing, cost management, total quality management and information technology.     

Event-driven multi-agent ubiquitous manufacturing execution platform for shop floor work-in-progress management

It has been reported that radio frequency identification (RFID) technology is applied to manufacturing shop floors for capturing and collecting real-time field data. Real-time information visibility and traceability allows decision makers to make better-informed shop floor decisions. It has been a great challenge to process a huge amount of RFID data into useful information for managerial uses. This paper presents an event-driven shop floor work-in-progress (WIP) management platform for creating a ubiquitous manufacturing (UM) environment. The platform aims to monitor and control dynamic production and material handling through RFID-enabled traceability and visibility of shop floor manufacturing processes. The platform provides facilities to process shop floor real-time RFID events and to aggregate actionable and meaningful operational information to support decision-making activities. An information processing mechanism based on a critical event model is proposed to organise real-time field data in various abstract levels for enterprise decisions. A case study at an air conditioner manufacturing company is used to demonstrate how the proposed platform can benefit its shop floor WIP management by showing how production and logistic operators and their supervisors accomplish their tasks.     

Adaptive and dynamic process planning using neural networks

Although feature-based process planning plays a vital role in automating and integrating design and manufacturing for efficient production, its off-line properties prevent the shop floor controller from rapidly coping with dynamic shop floor status such as unexpected production errors and rush orders. This paper proposes a conceptual framework of the adaptive and dynamic process planning system that can rapidly and dynamically generate the needed process plans based on shop floor status. In particular, the generic schemes for constructing dynamic planning models are suggested. The dynamic planning models are constructed as neural network forms, and then embedded into each process feature in the process plan. The shop floor controller will execute them to determine machine, cutting tools, cutting parameters, tool paths and NC codes just before the associated process feature is machined. The dynamic nature of process planning enables the shop floor controller to increase flexibility and efficiency in unexpected situations.     

A formal structure and implementation of a robotic material handling controller in an automated shop floor environment

This paper proposes a formal structure and framework for a robotic material handling (MH) equipment-level controller in a hierarchical shop floor control architecture. This framework essentially attempts to integrate shop-level process planning with material handling at the workplace. Handling operations are therefore integrated with manufacturing functions in order to ensure 'seamless' material transfer and processing. The responsibilities of the MH equipment controller include: interface with a MH robot; read and interpret handling plans from a database (in the form of an AND/OR graph); plan and schedule MH activities; perform path planning and grasp planning for the required task; generate generic robot-level commands automatically; and convert generic robot-level commands to robot-specific instructions. A MH equipment-level controller has been designed and implemented for a PUMA 560 robot in the CIM lab at the Pennsylvania State University. The proposed controller can work for various situations and job volumes. In addition, by creating the framework described in this paper, extending the concept for other parts and obstacles with various features, coordinating the vision system or the CAD system to the proposed controller, and combining complete path planning and grasp planning systems, a fully operational MH equipment-level controller can be realized.     

Enhancing smart shop floor management with ubiquitous augmented reality

This paper describes a framework for implementing Smart manufacturing Shop floor systems based on the Ubiquitous Augmented Reality technology (SSUAR). The proposed system makes use of data sharing between shop floor resources and a sensor network in order to optimise the production schedules for carrying out projects. The optimisation is performed in real-time and the production scheduling responds to new projects as well as the changing status of resources, such as machines and workers. Ubiquitous augmented reality interface has been developed and utilised as a user interface for the shop floor workers to receive information, instructions and guidance from the experts and manufacturing systems, and to update the systems on task parameters, such as estimated completion times, progress and machine status. A review of related work, methodology and implementation of the proposed system, and a case study are presented in this paper. Using this architecture, real-time scheduling of tasks in the smart shop floor can be achieved. The case study demonstrated the ability of SSUAR to integrate task scheduling with two-way communication between the system and the users.     

Negotiating price/delivery date in a stochastic manufacturing environment

We study a make-to-order manufacturing system consisting of several processing centers that are subject to failures and repairs. Our objective is to build a model that can be used as a tool for negotiating the delivery date and the price of a certain upcoming order. The model takes into account the congestion level of the shop floor at the time the order is placed. Based on the workload of the processing centers, the model splits the order into lots and assigns them to the processing centers so as to determine the order completion time associated with the minimum operating cost. The efficiency of the solution method for the model allows real-time decision-making while negotiating the price and delivery date of the order to be placed. Since the decisions are made based on a snapshot of the congestion level at the shop floor, using this model will reduce the conflict between the marketing and the production activities in manufacturing organizations.     

Fuzzy goal-programming model with an artificial immune system (AIS) approach for a machine tool selection and operation allocation problem in a flexible manufacturing system

Some of the important planning problems that need realistic modelling and a quicker solution, especially in automated manufacturing systems, have recently assumed greater significance. In real-life industrial applications, the existing models considering deterministic situations fail as the true language adopted by foremen and technicians are fuzzy in nature. Thus, to map the situation on the shop floor to arrive at a real-time solution of this kind of tactical planning problem, it is essential to adopt fuzzy-based multi-objective goals so as to express the target desired by the management of business enterprises. This paper presents a fuzzy goal-programming approach to model the machine tool selection and operation allocation problem of flexible manufacturing systems. The model is optimized using an approach based on artificial immune systems and the results of the computational experiments are reported.     

Placement of effective work-in-progress limits in route-specific unit-based pull systems

Unit-based pull systems control the throughput time of orders in a production system by limiting the number of orders on the shop floor. In production systems where orders can follow different routings on the shop floor, route-specific pull systems that control the progress of orders on the shop floor by placing limits on the number of orders in (parts of) a routing, have shown to be effective in controlling throughput times. This is because route-specific pull systems are able to create a balanced distribution of the amount of work on the shop floor, which leads to shorter and more reliable throughput times. The placement of limits on work-in-progress in a route-specific pull system determines to a large extent the workload balancing capability of such a system. This paper shows how the placement of work-in-progress limits affects the workload balancing capability and thereby the throughput time performance of a route-specific unit-based pull system, namely POLCA.     

A function block based approach for increasing adaptability of assembly planning and control

Today's market turbulences cause frequent changes in manufacturing environments. Products diversity, small batch sizes and short life cycles have increased production uncertainties and created a highly dynamic shop floor environment. One essential requirement of such an environment is an adaptive planning and control system that is sufficiently agile to respond to the variety of production requirements and enable easy system reconfiguration at run-time. When developing a product, assembly is a key area that impacts the manufacturing system's responsiveness to the changes. In this research, a framework and a new methodology are introduced to increase the adaptability and autonomy of job-shop assembly process planning and control using function blocks (FBs). A function block is a reusable functional module with an explicit event-driven model, and provides for data flow and finite state automata based control. Event-driven and FB-enabled decision-making is unique in adaptive assembly planning and control. It is explained through an example of a two-robot assembly work cell, where the result of the adaptive planning is wrapped in FBs for execution. The proposed approach has been implemented and simulated using Matlab Simulink in the case study. The simulation demonstrates how this approach would increase the adaptability and responsiveness to changes that may occur regularly in dynamic job-shop assembly operations.