Modular manufacturing

This paper discusses requirements to be satisfied by future manufacturing systems and proposes a new concept of modular manufacturing to integrate intelligent and complex machines. In large-scale systems such as manufacturing systems, modularization is indispensable for clarifying logical structure and assuring a high degree of ease of construction. The parts, products and manufacturing equipments as well as the design and operating activities themselves are all described in units called modules. A manufacturing system is constructed and operated by combining these in building-block style. The creation of this manufacturing system relies on construction and operating systems that enable design and simulation in the virtual world, and production and control in the real world, in a unified approach. Hardware modules and software modules are compiled flexibly and hierarchically to fulfil specified tasks. A system in which modular manufacturing as a concept of system integration is applied to manufacturing robots is called a modular robot system. The robots are embedded in manufacturing systems as the highest application of model-based robotics.     

Reliability-based performance indicator for a manufacturing network with multiple production lines in parallel

This paper addresses the system reliability evaluation for a manufacturing system with multiple production lines in parallel, where the system reliability is the possibility that a manufacturing system can satisfy demand. In the manufacturing system, the capacity of each machine is stochastic (i.e., multi-state) due to failure, partial failure, or maintenance. Thus, the manufacturing system can be constructed as a manufacturing network in terms of stochastic-flow network model. With considering reworking actions, we propose a graphical-based methodology to transform the manufacturing system into a manufacturing network. Thereafter, the manufacturing network is decomposed into general manufacturing paths and reworking paths. A simple algorithm is subsequently developed to generate the minimal capacity vectors that machines should provide to satisfy demand. The system reliability is derived in terms of such capacity vectors afterwards.     

Reconfigurable and transportable container-integrated production system

In this paper, the concept and the prototype realization of a novel reconfigurable small-footprint manufacturing system in a transportable container is presented. The containerized format enables transportation of the system to provide on-site manufacturing, enabling the benefits of localized service delivery without duplication of equipment at multiple locations.Three industrial product use cases with varying manufacturing and performance requirements were analysed. All of the use cases demanded highly customized products with high quality in low production volumes. Based on their requirements, a general system specification was derived and used to develop a concept for the container-integrated factory.A reconfigurable, modular manufacturing system is integral to the overall container concept. Production equipment was integrated in the form of interchangeable process modules, which can be quickly connected by standard utility supply and control interfaces. A modular and self-configuring control system provides assisted production workflow programming, while a modular process chain combining Additive Manufacturing, CNC milling, precision assembly and cleaning processes has been developed.A prototype of the container-integrated factory with reconfigurable process modules and control system has been established, with full functionality and feasibility of the system demonstrated.     

Robust manufacturing system design using multi objective genetic algorithms,Petri nets and Bayesian uncertainty representation

Decisions involving robust manufacturing system configuration design are often costly and involve long term allocation of resources. These decisions typically remain fixed for future planning horizons and failure to design a robust manufacturing system configuration can lead to high production and inventory costs, and lost sales costs. The designers need to find optimal design configurations by evaluating multiple decision variables (such as makespan and WIP) and considering different forms of manufacturing uncertainties (such as uncertainties in processing times and product demand). This paper presents a novel approach using multi objective genetic algorithms (GA), Petri nets and Bayesian model averaging (BMA) for robust design of manufacturing systems. The proposed approach is demonstrated on a manufacturing system configuration design problem to find optimal number of machines in different manufacturing cells for a manufacturing system producing multiple products. The objective function aims at minimizing makespan, mean WIP and number of machines, while considering uncertainties in processing times, equipment failure and repairs, and product demand. The integrated multi objective GA and Petri net based modeling framework coupled with Bayesian methods of uncertainty representation provides a single tool to design, analyze and simulate candidate models while considering distribution model and parameter uncertainties.     

Modular supervisory control for multi-floor manufacturing processes

Due to space availability limitations and high land costs, there is an increasing development of multi-floor manufacturing (MFM) systems in urban and industrial areas. The problem of coordination in a multi-floor manufacturing process, in the Ramadge Wonham framework, is introduced. The manufacturing chain of each floor and the elevator system are modeled in the form of finite deterministic automata. The models of the multi-floor manufacturing process are parametric with respect to the number of floors and the number of manufacturing machines on each floor. The coordination desired performance is formulated in the form of desired regular languages in analytic forms. The languages are realized by appropriate supervisors in the form of finite deterministic automata. The models of the supervisors are also parametric with respect to the number of floors and the number of manufacturing machines on each floor. The total control of the coordination of the multi-floor manufacturing process is accomplished via a modular supervisory control architecture. The complexity of the supervisors as well as the complexity of the total modular supervisory architecture are determined in analytic forms with respect to the number of floors and the number of manufacturing machines on each floor. The special case of a two floor manufacturing process is presented as an illustrative example.     

Machine number,priority rule,and due date determination in flexible manufacturing systems using artificial neural networks

When there is a production system with excess capacity, i.e. more capacity than the demand for the foreseeable future, upper management might consider utilizing only a portion of the available capacity by decreasing the number of workers or halting production on some of the machines/production lines, etc. while preserving the flexibility of the production system to satisfy demand spikes. To achieve this flexibility, upper management might be willing to attain some pre-determined/desired performance values in a production system having identical parallel machines in each work center. In this study, we propose a framework that utilizes parallel neural networks to make decisions on the availability of resources, due date assignments for incoming orders, and dispatching rules for scheduling. This framework is applied to a flexible manufacturing system with work centers having parallel identical machines. The artificial neural networks were able to satisfactorily capture the underlying relationship between the design and control parameters of a manufacturing system and the resulting performance targets.     

Reliability evaluation for a manufacturing network with multiple production lines

This paper focuses on performance evaluation of a manufacturing system with multiple production lines based on the network-analysis perspective. Due to failure, partial failure, or maintenance, the capacity of each machine is stochastic (i.e., multi-state). Hence, the manufacturing system can be constructed as a stochastic-flow network, named manufacturing network herein. This paper intends to measure the probability that the manufacturing network can satisfy customers’ orders. Such a probability is referred to as the system reliability. A graphical representation is first proposed to transform a manufacturing system into a manufacturing network. Thereafter, we decompose the manufacturing network into general processing paths and reworking paths. Three algorithms are subsequently developed for different scenarios and multiple production lines to generate the minimal capacity vectors that machines should provide to satisfy demand. The system reliability can be derived in terms of such capacity vectors afterwards.     

Reliability evaluation for a manufacturing network with multiple production lines

This paper focuses on performance evaluation of a manufacturing system with multiple production lines based on the network-analysis perspective. Due to failure, partial failure, or maintenance, the capacity of each machine is stochastic (i.e., multi-state). Hence, the manufacturing system can be constructed as a stochastic-flow network, named manufacturing network herein. This paper intends to measure the probability that the manufacturing network can satisfy customers’ orders. Such a probability is referred to as the system reliability. A graphical representation is first proposed to transform a manufacturing system into a manufacturing network. Thereafter, we decompose the manufacturing network into general processing paths and reworking paths. Three algorithms are subsequently developed for different scenarios and multiple production lines to generate the minimal capacity vectors that machines should provide to satisfy demand. The system reliability can be derived in terms of such capacity vectors afterwards.     

Integrating manufacturing resources planning (MRP II) with flexible manufacturing systems (FMS)

This paper will describe the gap that exists between the Manufacturing Resources Planning (MRP II) system and the Flexible Manufacturing System (FMS) and discuss several approaches for closing the gap. Considerable effort has gone into developing and integrating the various modules in the MRP II system. The modules for business planning, production planning, master production scheduling, material requirement planning, capacity requirements planning, and shop floor control have been integrated into a comprehensive computer package for planning and controlling manufacturing. At the same time, a similar effort has been underway to develop and integrate the several subsystems of a FMS. In particular, a number of computer control systems have been developed to automatically plan and control the operations of NC machines with automated tool changers, automated material handling, and automated test and inspection equipment. Unfortunately these two efforts have proceeded relatively independently with little interaction between the two groups. If computer integrated manufacturing (CIM) is to become a reality, the resulting gap must be closed, and closed rapidly.     

Modular finite state machines: Development and application to reconfigurable manufacturing cell controller generation

This paper presents a new framework developed for logic control of manufacturing systems based on finite state machines (FSMs) and a method to automatically generate the control logic for manufacturing cell controllers using this framework. The framework is modular; FSMs are encapsulated into modules with well-defined communication between them. Events in the finite state machines are of two types, triggers and responses, allowing FSMs to react to events and to force events to occur. The automatic generation method is based on a library of predefined modules. Its application to and implementation on a manufacturing testbed is described.     

Hidden maintenance opportunities in discrete and complex production lines

In manufacturing systems, many maintenance tasks require equipment to be stopped in order to safely perform them. However, such stoppage cannot last for too long since it might directly result in short-term production losses. In this paper, we investigate how long we can strategically shut down equipment for maintenance during scheduled operations without affecting system throughput. Using the concept of maintenance opportunity windows (MOWs), we estimate such time intervals for various system configurations. Simulations are used to deal with uncertainties in production lines, such as random machine failures, starvations, blockages, etc. Moreover, the proposed MOW algorithms are demonstrated through simulations and real case studies in an automotive assembly plant.     

Modular machine tools: Design and barriers to industrial implementation

The Reconfigurable Manufacturing System (RMS) paradigm has been developed to address challenges in the design of manufacturing systems and equipment that will meet the demands of modern manufacturing. This research involved the development of Modular Reconfigurable Machines (MRMs); as an emerging technology in reconfigurable manufacturing. MRMs are mechanically modular machines. The modularity permits the kinematic architecture and processing functions of the machine to be reconfigured to meet changing production requirements. This paper will focus on aspects of the mechanical design and the development of a control system that supported the modularity and reconfigurability of the mechanical platform. A modular electronic system is presented that is characterized by a plug and play approach to control scalability. This is complemented by a software architecture that has been developed with a focus on hardware abstraction for the management of an augmented mechanical and electronic architecture. The implications of MRMs for RMSs are discussed and key inhibitors to industrial implementation are identified.     

On shift register realization of sequential machines

This paper deals with the modular realization of sequential machines using shift registers as the modules. For a given minimal machine, the minimum number of modules required for its realization has been determined. It is well known that the number of modules required to realize a given machine can sometimes be reduced by realizing a non-minimal machine equivalent to the given minimal one, i.e. by assigning multiple codes to the states of the given minimal machine. This case is dealt with in the second part of the paper.     

Integrating multiple parallel programming paradigms in a dataflow-based software environment

By viewing different parallel programming paradigms as essentially heterogeneous approaches in mapping ‘real-world’ problems to parallel systems, the authors discuss methodologies in integrating multiple programming models on a massively parallel system such as Connection Machine CM5. Using a dataflow based integration model built in a visualization software AVS, the authors describe a simple, effective and modular way to couple sequential, data-parallel and explicit message-passing modules into an integrated parallel programming environment on a CM5. A case study in the area of numerical advection modeling is given to demonstrate the integration of data-parallel and message-passing modules in the proposed multi-paradigm programming environment.     

Intelligent product and mechatronic software components enabling mass customisation in advanced production systems

Up to the present time, the control software design of production systems has been developed to produce a certain number of goods, in a centralised manner and through a case-by-case, timely and costly process. Therefore, the current control design approaches hinder factories in their pursuit to acquire the essential capabilities needed in order to survive in this customer-driven and highly competitive market. Some of these vital production competencies include mass customisation, fault tolerance reconfigurability, handling complexity, scalability and agility. The intention of this research is to propose a uniform architecture for control software design of collaborative manufacturing systems. It introduces software components named as modular, intelligent, and real-time agents (MIRAs) that represent both intelligent products as clients (C-MIRA) and machines or robots as operators (O-MIRAs) in a production system. C-MIRAs are in constant interaction with customers and operators through human machine interfaces, and are responsible for transforming products from concepts up to full realisation of them with the least possible human intervention. This architecture is built upon the IEC 61499 standard which is recognised for facilitating the distributed control design of automation systems; however, it also takes into account the intelligent product concept and envisages the machines’ control to be composed of a set of modular software components with standardised interfaces. This approach makes the software components intuitive and easy to install, to create the desired behaviour for collaborative manufacturing systems and ultimately paves the way towards mass customisation. A simplified food production case study, whose control is synthesised using the proposed approach, is chosen as an illustrative example for the proposed methodology.     

The utilization of stand-alone microprocessor units to control energy intensive systems

Most energy intensive process control systems utilize an oversize dedicated computer system. The utilization of stand-alone microprocessor units to control energy intensive systems, reduces costs, facilitates rapid troubleshooting and provides a means to initially inexpensively monitor the operations of these systems to provide control design data. A manufacturing company inquired as to the possibility of reducing their operational costs utilizing computers. This resulted in the design of a multiple, stand-alone and modular Z8 microcomputer configuration to control their steam and compressed air production.     

Novel Double Modular Redundancy Based Fault-Tolerant FIR Filter for Image Denoising

In signal processing and communication systems, digital filters are widely employed. In some circumstances, the reliability of those systems is crucial, necessitating the use of fault tolerant filter implementations. Many strategies have been presented throughout the years to achieve fault tolerance by utilising the structure and properties of the filters. As technology advances, more complicated systems with several filters become possible. Some of the filters in those complicated systems frequently function in parallel, for example, by applying the same filter to various input signals. Recently, a simple strategy for achieving fault tolerance that takes advantage of the availability of parallel filters was given. Many fault-tolerant ways that take advantage of the filter’s structure and properties have been proposed throughout the years. The primary idea is to use structured authentication scan chains to study the internal states of finite impulse response (FIR) components in order to detect and recover the exact state of faulty modules through the state of non-faulty modules. Finally, a simple solution of Double modular redundancy (DMR) based fault tolerance was developed that takes advantage of the availability of parallel filters for image denoising. This approach is expanded in this short to display how parallel filters can be protected using error correction codes (ECCs) in which each filter is comparable to a bit in a standard ECC. “Advanced error recovery for parallel systems,” the suggested technique, can find and eliminate hidden defects in FIR modules, and also restore the system from multiple failures impacting two FIR modules. From the implementation, Xilinx ISE 14.7 was found to have given significant error reduction capability in the fault calculations and reduction in the area which reduces the cost of implementation. Faults were introduced in all the outputs of the functional filters and found that the fault in every output is corrected.     

Modular Petri Net based modeling,analysis, synthesis and performance evaluation of random topology dedicated production systems

Ordinary t-timed modular Petri Nets are used for modeling, analysis, synthesis and performance evaluation of random topology dedicated production systems. Each system is first decomposed into production line, assembly, disassembly and parallel machine modules followed by derivation of their modular Petri Net models. Two sets of modules, generic and generalized respectively, are derived corresponding to the simplest and most general cases. Overall system Petri Net model is obtained via synthesis of the individual modules satisfying system features (production rates, buffer capacities, machine expected up, down or idle times). Detailed mathematical expressions are derived for modules P-invariants (and T-invariants when exist); they are further generalized for a random topology and complexity dedicated production system. Total number of the individual Petri Net module nodes as well as of the combined system Petri Net model is also calculated. Results show the applicability of the proposed methodology and justify its modeling power and generality.     

Performability of systems based on renewal process models

A system of N statistically identical machines is modeled using renewal theory through a unique architecture and state space. The model is very useful in evaluating flexible manufacturing systems (FMS) in particular. Each machine consists of multiple part types that are subject to individual failure. A multiple stage repair process composed of integrable sojourn time distributions requires access to spare parts to repair down machines. A performability measure is used to gauge system effectiveness for this partially degradable system, and an example is given     

Reliability evaluation for an intermittent production system with stochastic number of normal machines

In an intermittent production system (IPS), a number of normal machines in a workstation may present multiple levels owing to maintenance, possibility of failure, etc. It means that the number of machines in each workstation is stochastic. This paper proposes a key performance index (KPI), which reflects the probability that an IPS can complete demand d within time constraint T. Such a probability is defined as system reliability. The IPS is modeled as a stochastic network, in which each arc is regarded as a workstation with stochastic number of normal machines, and each node is represented as a buffer. The concept of minimal machine vector (MMV), which indicates the minimal capacity required at each workstation to satisfy the demand and time constraints, is presented for evaluating the system reliability. In particular, a novel algorithm based on depth-first search is proposed to derive all MMVs. This algorithm avoids searching for unnecessary child nodes, and thus increases efficiency. Two practical examples, a printed circuit board and a footwear manufacturing systems, are used to illustrate the proposed algorithm. Such a KPI can provide information to production managers to understand the probability that an order can be completed on time.