A model for assessing the layout structural complexity of manufacturing systems

The layout of a manufacturing facility/system not only shapes its material flow pattern and influence transportation and operation cost, but also affects logistics and parts/machine assignment decisions. The layout of manufacturing systems determines its structural complexity by virtue of its design configuration characteristics. This paper introduces a new model and indices for assessing the structural complexity of manufacturing systems layout in the physical domain. Six complexity indices, based on the physical structural characteristics of the layout, have been introduced and formulated. They are layout density, path, cycle, decision points, redundancy distribution and magnitude indices. An overall Layout Complexity Index (LCI) which combines all indices is developed using a novel method based on radar plots which is insensitive to the order of plotting the individual indices. The use of the developed LCI is demonstrated using six typical types of manufacturing systems layouts and relevant guidelines are presented. The developed model and complexity indices help design system layouts for least complexity and compare layout alternatives that meet the specifications, at early design stages. It supports making trade-off decisions regarding manufacturing systems flexibility and complexity and their associated costs.     

Integration of design and manufacturing for structural shape optimization

This paper presents an integrated design and manufacturing approach that supports shape optimization of structural components. The approach starts from a primitive concept stage, where boundary and loading conditions of the structural component are given to the designer. Topology optimization is conducted for an initial structural layout. The discretized structural layout is smoothed using parametric B-Spline surfaces. The B-Spline surfaces are imported into a CAD system to construct parametric solid models for shape optimization. Virtual manufacturing (VM) techniques are employed to ensure that the optimized shape can be manufactured at a reasonable cost. The solid freeform fabrication (SFF) system fabricates physical prototypes of the structure for design verification. Finally, a computer numerical control (CNC) machine is employed to fabricate functional parts as well as mold or die for mass production of the structural component. The main contribution of the paper is incorporating manufacturing into the design process, where manufacturing cost is considered for design. In addition, the overall design process starts from a primitive stage and ends with functional parts. A 3D tracked vehicle roadarm is employed throughout this paper to illustrate the overall design process and various techniques involved.     

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.     

基于RMC的可重构制造系统设备布局优化研究*

为实现多品种变批量生产制造系统阵列式布局的动态重构,提出一种新的设备布局优化方法.建立了可重构制造系统(RMS)设备优化选择数学模型,设计了基于蚁群优化和阶序聚类算法的可重构制造单元(RMC)动态重构算法.以交货期内最小成本为目标,引入系统复杂度和系统响应度从系统能力角度完善了实现RMS重构的约束条件,最终完成了可重构制造系统设备布局优化.最后通过布局实例仿真验证该方法的可行性和有效性.     

Performance analysis of manufacturing systems composed of modular machines using the universal generating function

The modularity and reconfigurability of the building blocks of modern manufacturing systems have to be considered when evaluating their performance. This paper proposes a model for evaluating system availability and expected production rates for manufacturing systems that are composed of unreliable modular machines with multiple functionally parallel production units. These units are treated as independent modules, where the breakdown/stoppage of one unit does not necessitate the failure/stoppage of the whole machine and its production. The considered systems are multi-state manufacturing systems (MSMS) that can handle multiple parts simultaneously, and their structure is that of nonbuffered flow lines allowing paralleling of identical multi-state modular machines (MSMM) in each production stage. In spite of the inherent computational complexity of the proposed analysis, due to the large number of system states, it was made possible by the use of the universal generating function (UGF) technique, which proved efficient for large MSMS. The proposed model was applied to a number of case studies for demonstration and verification. The case studies were based on a family of engine front covers. The results show that machines with a larger number of modules, usually thought of as having lower availability, provided higher overall system availability in the case of machines with multiple spindles. Based on the new analysis and results, it is recommended that system designers favorably consider machines with multiple spindles rather than increasing the number of machines in parallel. These results provide an important support for the use of modular/reconfigurable equipment compared with traditional equipment, in spite of the higher cost.     

Decision making in equipment selection: an integrated approach with AHP and PROMETHEE

Multi-attribute equipment selection is a very important issue for an effective manufacturing system, since the improper equipment selection might cause many problems affecting productivity, precision, flexibility and quality of the products negatively. On the other hand, selecting the best equipment among many alternatives is a multi-criteria decision making (MCDM) problem. In this study, an integrated approach which employs analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluations (PROMETHEE) together, is proposed for the equipment selection problem. The AHP is used to analyze the structure of the equipment selection problem and to determine weights of the criteria, and PROMETHEE method is used to obtain final ranking, and to make a sensitivity analysis by changing the weights. Proposed approach is applied to a problem of selecting milling machines to be purchased in an international company. Company management found the application and results satisfactory and implementable in their equipment selection decisions.     

An integrated approach to machine selection problem using fuzzy SMART-fuzzy weighted axiomatic design

In respond to new market requirements and competitive positioning of manufacturing companies selecting optimal machines that are consistent with manufacturing goals is of crucial importance. As it involves multiple conflicting criteria and inherent ambiguity and vagueness, election of a suitable machine can be regarded as a fuzzy multi-criteria decision making problem. In this study, for the first time in the literature, an integrated approach consisting of fuzzy simple multiattribute rating technique (SMART) approach and fuzzy weighted axiomatic design (FWAD) approach is proposed to determining the optimal continuous fluid bed tea dryer for a privately owned tea plant operating in Turkey. The weights of the evaluation criteria are calculated via fuzzy SMART and then FWAD is utilized to rank competing machine alternatives in terms of their overall performance. In the FWAD application phase, five experts have determined functional requirements (FRs) and have rated alternatives. Therefore, individual fuzzy opinions were required to be aggregated in order to set up a group consensus. A group decision analysis, referred to as the least squares distance method is used to aggregating the ratings of FRs and alternatives. It is concluded that the proposed hybrid methodology is a robust decision support tool for ranking machine alternatives under fuzzy environment and furthermore, it can be exploited for other fuzzy decision making problems, as well.     

Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings,lot splitting and flexible reconfiguration by simulated annealing

This paper presents a novel mixed-integer non-linear programming model for the layout design of a dynamic cellular manufacturing system (DCMS). In a dynamic environment, the product mix and part demands are varying during a multi-period planning horizon. As a result, the best cell configuration for one period may not be efficient for successive periods, and thus it necessitates reconfigurations. Three major and interrelated decisions are involved in the design of a CMS; namely cell formation (CF), group layout (GL) and group scheduling (GS). A novel aspect of this model is concurrently making the CF and GL decisions in a dynamic environment. The proposed model integrating the CF and GL decisions can be used by researchers and practitioners to design GL in practical and dynamic cell formation problems. Another compromising aspect of this model is the utilization of multi-rows layout to locate machines in the cells configured with flexible shapes. Such a DCMS model with an extensive coverage of important manufacturing features has not been proposed before and incorporates several design features including alternate process routings, operation sequence, processing time, production volume of parts, purchasing machine, duplicate machines, machine capacity, lot splitting, intra-cell layout, inter-cell layout, multi-rows layout of equal area facilities and flexible reconfiguration. The objective of the integrated model is to minimize the total costs of intra and inter-cell material handling, machine relocation, purchasing new machines, machine overhead and machine processing. Linearization procedures are used to transform the presented non-linear programming model into a linearized formulation. Two numerical examples taken from the literature are solved by the Lingo software using a branch-and-bound method to illustrate the performance of this model. An efficient simulated annealing (SA) algorithm with elaborately designed solution representation and neighborhood generation is extended to solve the proposed model because of its NP-hardness. It is then tested using several problems with different sizes and settings to verify the computational efficiency of the developed algorithm in comparison with the Lingo software. The obtained results show that the proposed SA is able to find the near-optimal solutions in computational time, approximately 100 times less than Lingo. Also, the computational results show that the proposed model to some extent overcomes common disadvantages in the existing dynamic cell formation models that have not yet considered layout problems.     

A generalized method for the inherent energy performance modeling of machine tools

Machine tools (MTs), as the key equipment of manufacturing systems, have enormous quantities and consume a great amount of energy. However, the diversity of both machines and their energy consumption properties make it difficult to transfer the energy-saving knowledge and services among different MT. To facilitate the initialization configuration of energy-saving services, the inherent energy performance (IEP) is investigated to describe the differences in energy consumption among MTs, and a generalized method for modeling the IEP of MT and its electrical subsystems is proposed. Three key enablers, including generalized experimental design rules, automatic coding, and data processing algorithms, are presented and integrated into a supporting system to reduce the modeling efforts and knowledge requirements. Case studies of an offline manufacturing scenario and an Internet of Things (IoT)-enabled manufacturing scenario were carried out to verify the effectiveness and convenience of the proposed method. The results show that the proposed method can provide essential modeling support for large-scale energy-saving service configurations and energy-efficient MT development.     

Task-driven e-manufacturing resource configurable model

Manufacturing resource configuration (MRC) plays a very important role in an e-Manufacturing system. Higher requirements for optimal configuration under online resource visibility and traceability have led to two main challenges. One is that more features of manufacturing tasks affecting the optimization results should be taken into consideration when establishing the MRC mathematical model for a manufacturing cell. The other is that manufacturing information should be given equal attention as MRC to realize real-time visibility and traceability of the resulting manufacturing cells. This paper presents a comprehensive mathematical model which considers more practical features of manufacturing tasks (e.g. batch volume and alternative processing routes) for manufacturing cell formation. This model adopts a fuzzy clustering method to group the manufacturing tasks and machines. Moreover, it is enabled by a smart equipment model to realize the configurable model of real-time manufacturing information and corresponding visualization and tracing methods. A case study is given to demonstrate the proposed models and methods.     

A review of current research in manufacturing shop design integration

The importance of the manufacturing shop design in the successful operation of a production system is well documented and, as a result, significant work has been devoted to this area. Due to the inherent complexity of the global shop design problem, much research has focused on individual sub-problems, including the layout of manufacturing resources, the design of the shop aisle network, and the selection of the material handling equipment, rather than addressing the integrated problem directly. This paper reviews literature contributions to shop design integration, as well as partial integration schemes such as the design for operation and control of material handling systems. Furthermore, it examines the ability of models and solution approaches for individual sub-problems of shop design to support integration schemes.     

Integrated maintenance and operations decision making with imperfect degradation state observations

In highly flexible and integrated manufacturing systems, such as semiconductor fabs, strong interactions between the equipment condition, operations executed on the various machines and the outgoing product quality necessitate integrated decision making in the domains of maintenance scheduling and production operations. Furthermore, in highly complex manufacturing equipment, the underlying condition is not directly observable and can only be inferred probabilistically from the available sensor readings. In order to deal with interactions between maintenance and production operations in Flexible Manufacturing Systems (FMSs) in which equipment conditions are not perfectly observable, we propose in this paper a decision-making method based on a Partially Observable Markov Decision Processes (POMDP's), yielding an integrated policy in the realms of maintenance scheduling and production sequencing. Optimization was pursued using a metaheuristic method that used the results of discrete-event simulations of the underlying manufacturing system. The new approach is demonstrated in simulations of a generic semiconductor manufacturing cluster tool. The results showed that, regardless of uncertainties in the knowledge of actual equipment conditions, jointly making maintenance and production sequencing decisions consistently outperforms the current practice of making these decisions separately.     

制造系统中的单向环型设备布局设计

提出一种优化建模与虚拟现实技术相结合的求解策略，较好地解决了制造系统中的单向环型设备布局问题．研究该问题的固有特性，提出三条定理，构建了一个启发式算法，并实现了一个沉浸式虚拟布局设计的例子．     

A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment

This paper deals with a problem of reconfigurable manufacturing systems (RMSs) design based on products specifications and reconfigurable machines capabilities. A reconfigurable manufacturing environment includes machines, tools, system layout, etc. Moreover, the machine can be reconfigured to meet the changing needs in terms of capacity and functionality, which means that the same machine can be modified in order to perform different tasks depending on the offered axes of motion in each configuration and the availability of tools. This problem is related to the selection of candidate reconfigurable machines among an available set, which will be then used to carry out a certain product based on the product characteristics. The selection of the machines considers two main objectives respectively the minimization of the total cost (production cost, reconfiguration cost, tool changing cost and tool using cost) and the total completion time. An adapted version of the non- dominated sorting genetic algorithm (NSGA-II) is proposed to solve the problem. To demonstrate the effectiveness of the proposed approach on RMS design problem, a numerical example is presented and the obtained results are discussed with suggested future research.     

车间数字设备集成控制系统的研究

本文分析了网络制造环境下离散制造企业车间数字设备控制模式及其控制网络的拓扑结构;提出了基于EPA(Ethernet for Plant Automation)协议的车间数字设备集成控制系统的体系结构;设计了集成控制网络系统的结点结构和通信协议转换器;利用嵌入式技术实现了车间数字设备与工业以太网间信息的透明传输。     

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.     

Design of a synchronous manufacturing system with just-in-time production (SMS/JITP)

One of the goals of a manufacturing system is to minimize the cost of production. A significant source of manufacturing cost is attributable to material handling. The sources of material handling cost include equipment, labor, work-in-process, and floor space. One approach to reduce material handling cost is through the implementation of synchronous manufacturing with just-in-time production. Although, the concept of a synchronous manufacturing is known, techniques to design and plan such systems are yet to be formalized. In this paper, a quantitative modeling framework for the design and analysis of a synchronous manufacturing system with just-in-time production is presented. The approach is composed of principles and techniques drawn from scheduling, layout planning, material handling, and computer simulation.     

A data-driven generic simulation model for logistics-embedded assembly manufacturing lines

Simulation has been used to evaluate various aspects of manufacturing systems. However, building a simulation model of a manufacturing system is time-consuming and error-prone because of the complexity of the systems. This paper introduces a generic simulation modeling framework to reduce the simulation model build time. The framework consists of layout modeling software and a data-driven generic simulation model. The generic simulation model was developed considering the processing as well as the logistics aspects of assembly manufacturing systems. The framework can be used to quickly develop an integrated simulation model of the production schedule, operation processes and logistics of a system. The framework was validated by developing simulation models of cellular and conveyor manufacturing systems.     

The implementation of neural network for semiconductor PECVD process

In semiconductor manufacturing, the monitoring system has been developed very excellently and can be used for comprehensively collecting the historical data of process information and quality characteristics of equipment. However, due to the high turnover rate of personnel and the great variance in manufacturing process, the previous control technique by using intuition and experience of engineers for manufacturing process parameter settings to achieve good product quality is no longer appropriate. Therefore, this research establishes a quality predictor for analyzing the relationship between manufacturing process parameter setting and final product quality in the plasma-enhanced chemical vapor deposition (PECVD) of semiconductor manufacturing by applying the back-propagation neural network (BPNN) algorithm and Taguchi method. The experimental data are categorized into 500 pieces of training data and 150 pieces of verifying data. The proposed analysis method for using in the PECVD process of semiconductor manufacturing is verified by comparing the predicted film thickness of SiO₂ and the predicted refractive index of silicon dioxide films with the measured data. According to the comparison result, the proposed model has an excellent prediction capability of final product quality and can be applied in process control for related manufacturing fields.     

Genetic algorithms for designing loop layout manufacturing systems

A common layout for flexible manufacturing system is loop network with machines arranged in a cycle and materials transported in only one direction around the cycle. Congestion is a common measure for evaluating a loop layout. This paper investigates the problem of designing loop layout system with both of minsum and minmax congestion measures. A hybrid heuristic of genetic algorithms and neighborhood search is developed for solving such problem and preliminary computational results are reported.