A cellular similarity coefficient algorithm for the design of manufacturing cells

The development and implementation of an integrated system for computer-aided process planning and cellular manufacturing design is described. A coding scheme is proposed for classifying parts according to the manufacturing processes required to produce them. The routeing data and the classification codes obtained from computer-aided process planning are then stored in a database. A heuristic algorithm has also been developed for designing manufacturing cells using the classification data stored in the process planning database. This algorithm is based on a new concept developed in this study called cellular similarity coefficient which considers the similarity between machine cells rather than individual machines, as in the case of other similarity coefficients.     

On-line optimization of quality in a manufacturing system

One of the major obstacles contributing to the cost, time and efficiency of improving the quality output of manufacturing systems is the propagation of defectives or errors through the system. Design of Experiments (DoE), the response surface plot and a Neural Network Metamodel (NNM) can be used automatically to detect the interrelationship of the system without the need for complex analytical tools and costly intervention. A case study is conducted here to demonstrate the capability of DoE, the response surface plot and NNM in building a decisionsupport model for achieving six-sigma quality for a manufacturing system with a significant shift in the mean number of defectives produced. The case study is based on a discrete event simulation model of an actual manufacturing system. A response surface plot is used as an off-line decision support tool. Alternatively, a grid search method implemented on the NNM can be used as an on-line decision support tool in the manufacturing system with a real-time database system.     

Quality and reliability information integration for design evaluation of fixture system reliability

Quality and reliability are two important factors in manufacturing‐system design. However, the analysis and optimization of manufacturing‐system reliability and product quality are normally conducted separately in practice. There is no general framework to integrate these two important factors, quantitatively analyze the interactions between them, and further study their integrated effects on the manufacturing‐system performance. In this paper, the QR‐Co‐Effect of product/part quality and manufacturing‐system component reliability is investigated in an assembly fixture system. The concept, model and analysis of QR‐Co‐Effect are addressed in this paper. Copyright © 2001 John Wiley & Sons, Ltd.     

A DSS approach to developing systems to support production planning and control in smaller companies

Smaller Manufacturing Enterprises (SMEs) would benefit from manufacturing decision support systems (DSSs) in which the links between customer orders and manufacturing operations were maintained throughout the production planning process. However, systems with this characteristic are not available at prices that most SMEs can afford. This paper describes how a prototype DSS with this feature was developed and validated using data from a manufacturer of fencing materials. A hybrid relational database/object-oriented approach to modelling the manufacturing process is outlined. Static and slowly changing data about the manufacturing system were stored on a relational database, while more dynamic production planning information was built into an object model. The system made use of a 'Bill of Production' for each manufactured item that contained both materials and operation information, and was constructed at the time it was required from information held on the database.     

Quality control information systems

As competition for world markets becomes more intense and as greater demands are placed on human and natural resources, manufacturers face a formidable challenge — to produce cost competitive products of the highest possible quality. Nowadays the quality control (quality assurance) department can spend incredibly large amounts of time just collecting the test data they need in order to do their job. Collating and analysing the data is similarly very demanding in terms of how much human time can be expended. Simplification and automation of the tasks of data collection, collation and analysis is possible with a quality control information system. A microprocessor can form the basis of a quality control information system if application to only a very small area of a manufacturing operation is desired. However, far greater benefits can be derived if a real-time minicomputer is used instead of a microprocessor. This is because quality control data can then be collected across the entire factory floor. Statistical quality control techniques can subsequently be applied to examine the effect of process conditions in some part of the factor on product malquality exhibited in any other part of the factory. ‘What-if’ type analyses can also be conducted factory wide. A quality control information system should be capable of (a) identifying the product quality problems (b) determining the causes of these product quality problems (c) helping to eliminate the causes (d) monitoring the altered process. These system functions enable the quality control department to progress from a ‘fix-the-product’ mode to a ‘fix-the-process’ mode and thereby concentrate on making the product right in the first place, by achieving better control of the manufacturing process. Application software packages are now available which have been purpose-designed for quality control information systems. One such example is the Quality Decision Management (QDM) software package from Hewlett-Packard, which runs on an industrial HP 1000 real-time minicomputer. On the data entry side a quality control information system should be capable of manual input from menu-driven terminals and bar-code wands. Automatic input of test data is also vitally important. This enables such devices as analysers, test instruments, data acquisition units. ATE and other computers to be interfaced to gather test data with no manual intervention. All collected data should be held in a database. The QDM database can be searched to generate a wide range of reports in tabular or colour graphic form, the latter include histograms, scattergrams, control charts (X-bar, sigma), P-charts and Pareto diagrams. The reports enable crucial quality control decisions to be taken. Quality control information systems can be linked to higher level production control computers to form a computer integrated manufacturing (CIM) network. Free movement of data around a CIM network offers enormous flexibility and efficiency in the overall manufacturing process, together with considerable savings in manpower. Costs incurred in the creation of a quality control information system can be easily won back by virtue of the significant savings that such a system brings about.     

A knowledge-based decision support system for locating a manufacturing facility

This paper examines the design and implementation of a knowledge-based decision support system (KBDSS) in the facility location domain. The KBDSS for locating a manufacturing facility is intended to support facilities planners in improving the quality of strategic decision-making in facility location. A survey of past location studies was conducted to identify the major considerations of location analysts and to develop a hierarchy of factors for locating a manufacturing facility in the USA. Location evaluation is accomplished via a location model that is formulated as a linear additive multiattribute utility model. The microcomputer-based DSS developed integrates an object-oriented expert system with other modeling tools, such as a database management system and a C-based interactive graphical user interface. The system incorporates subjective, judgemental evaluations from management with objective, factual location data to recommend the best location for a new manufacturing facility. The system also makes transparent the justification for its recommendations by providing appropriate textual and graphical explanation facilities.     

Configuration of cellular manufacturing systems using association rule induction

A cell-formation approach based on association rule induction is developed to find the effective configurations for cellular manufacturing systems. To gain the benefits of flexibility and efficiency, the manufacturing system is decomposed into several manageable subsystems by categorizing similar parts into part families and disparate machines into cells. It is advantageous to find the important associations among machines such that the occurrence of some machines in a machine cell will cause the occurrence of other machines in the same cell. Relationships among machines can be found from the process database by inducting association rules. By applying association rules to cell-formation problems, certain sets of machines (machine groups) that frequently process some parts together can be inducted. A data-mining technique referred to as association rule induction is used herein to find the association rules among machines from the process database. Seventeen data sets of various size and complexity were used to evaluate the effectiveness of the proposed cell-formation algorithm based on association rule induction. The performance of the proposed approach is compared with several existing techniques. From the computational results, the proposed approach shows its ability to find quality solutions.     

复杂型材智能加工制造系统的设计

目的 实现新一代信息技术背景下传统铝门窗幕墙型材加工行业的转型升级,以应对复杂型材加工制造存在的成本高、工序繁多等诸多挑战。方法 根据型材加工工艺流程及该行业定制化生产的特点,提出一种涵盖网上下单、订单自动处理、机床智能加工生产的复杂型材智能加工制造系统架构,重点针对自主开发的门窗幕墙型材一站式加工智能机床,研发出一套复杂型材智能加工制造系统。结果 研究了加工信息数字化模型、工艺数据库等关键技术。通过工艺数据库的构建,实现了自动编程系统的搭建。结合Web Service与XML技术,研发出订单自助处理系统、机床智能操作管理系统及其与ERP系统的集成互连,打通了生产各环节之间的技术壁垒,形成了复杂型材一体化加工工艺。结论 实际测试表明,经复杂型材智能加工制造系统一体化制造的复杂型材从接受订单到产品加工完成只需40分钟,大幅提高了生产效率和产品质量,降低了加工成本。本研究为复杂型材智能加工及其他传统制造行业的转型升级提供了有益的借鉴和参考。     

The impact of performance and practice factors on UK manufacturing companies' survival

This paper investigates the effects of manufacturing performance variables and practice factors at the firm level on UK manufacturing companies' survival performance in the last two decades. Statistic tests and econometric modelling have been employed on a longitudinal UK manufacturing database. The statistic tests generate different survival factors for individual sectors and the whole database. Model results support contentions that, in general, technology usage, total factor productivity, quality and cost reduction were important survival factors and, in addition, so were workforce flexibility, innovation and product development process in specific contexts. Furthermore, the results suggest that the probit and logit models can be reasonably robust predicting tools for analysing UK manufacturing companies' survival.     

Integrating artificial intelligence into on‐line statistical process control

Statistical process control (SPC) is one of the most effective tools of total quality management, the main function of which is to monitor and minimize process variations. Typically, SPC applications involve three major tasks in sequence: (1) monitoring the process, (2) diagnosing the deviated process and (3) taking corrective action. With the movement towards a computer integrated manufacturing environment, computer based applications need to be developed to implement the various SPC tasks automatically. However, the pertinent literature shows that nearly all the researches in this field have only focussed on the automation of monitoring the process. The remaining two tasks still need to be carried out by quality practitioners. This project aims to apply a hybrid artificial intelligence technique in building a real time SPC system, in which an artificial neural network based control chart monitoring sub‐system and an expert system based control chart alarm interpretation sub‐system are integrated for automatically implementing the SPC tasks comprehensively. This system was designed to provide the quality practitioner with three kinds of information related to the current status of the process: (1) status of the process (in‐control or out‐of‐control). If out‐of‐control, an alarm will be signaled, (2) plausible causes for the out‐of‐control situation and (3) effective actions against the out‐of‐control situation. An example is provided to demonstrate that hybrid intelligence can be usefully applied for solving the problems in a real time SPC system. Copyright © 2003 John Wiley & Sons, Ltd.     

Laserstrahlbasiertes Reparaturlöten in der Elektronik‐Produktion

Laser based rework in electronic production A production of electronic boards with a zero defect‐rate is not attainable still in today’s electronic manufacturing. Boards with defect solder joints or defect components will often be recirculated to a manual rework process, in which the quality and the economical load are depending on quality of personal and system. This paper presents possibilities for the automation of assembly and soldering processes using a diode laser system. Main facts are the advantages of laser systems for rework processes, possibilities of system technical realization and industrial feasibility. In process technical point of view influences of quality after rework processes will be discussed.     

Performance measurement for a manufacturing system based on quality,cost and time

Product quality, manufacturing cost and manufacturing time are three of the major concerns in a manufacturing system. This study proposes a product capability index to evaluate the quality of a multi-process product and addresses the relationship between process yield and the product capability index. In addition, this paper develops a time–cost index to assess manufacturing cost and manufacturing time. The quality index and time–cost index are simultaneously used to monitor product quality and manufacturing time on a quality and time–cost analysis chart. The quality and time–cost analysis chart, providing accurate information on the quality status and time–cost status for each process, helps to initiate, effectively and efficiently, quality improvement plans to elevate product quality and manufacturing time and manufacturing cost control for a manufacturing system.     

Feature-driven,process-based approach to the integration of CAD /CAM in wireframe models

This paper presents a structure for an integrated CAD/CAM system in wireframe models than can be simultaneously used for product design and manufacture of prismatic components. A feature-driven, process-based design methodology, which derives its basis from traditional geometry-based design, feature-based design, deterministic and expert heuristic manufacturing knowledge, is proposed. This methodology provides an integrated modelling environment for automation of downstream manufacturing applications such as process planning and NC code generation. Representation of features in the design database is not limited to geometry alone but includes material, tooling and machine specifications and is based on a process-to-geometry mapping. Research shows that informational completeness of a CAD database depends on application; and for manufacturing tasks, the vocabulary of the process describing the means of making a part should be mapped directly to the feature-based geometry. This effort answers key issues in integrating CAD and CAM in the most widely used wireframe modelling systems.     

Development of a financial management system in a CIMS environment

This research is based on the Anshan Project, a cool strip millwork computer integrated manufacturing system (LZ-CIMS). The study describes the design and completion of a LZ-CIMS financial management system that was developed using an Oracle database with client/server architecture and Developer/2000. During the development of the system, the study explores the use of both software engineering and database theory and Oracle RDBMS to discuss the integrating methods of financial information.     

Taxonomy of the manufacturing strategies of large Spanish industrial companies

This work presents a classification of the manufacturing strategies of large Spanish industrial companies in Spain. For this purpose the various international empirical studies on alternative manufacturing strategies have been taken as a reference, reviewed and compared. The database used in this study is made up of information on 114 factories and has been designed using the information from a survey mailed to the industrial companies which have production plants in Spain and which in the financial year of 1994 (reference date of this research) employed over 200 workers. Cluster analysis methodology has been used and the classification criterion taken was the competitive advantages or strengths of each factory concerning the five competitive priorities in manufacturing: efficiency, flexibility, quality, delivery and customer service. Finally, this work identifies a taxonomy of the manufacturing strategies of a sample of the largest industrial companies in Spain, differentiating three alternative production strategies: flexible marketoriented manufacturers, low-cost quality manufacturers and delivery-based manufacturers. Large industrial companies analysed compete in manufacturing, basically following a choice of three criteria: flexibility, quality or delivery. The companies belonging to each of the three categories identified maintain common and lasting manufacturing objectives and policies.     

Mission reliability evaluation based on operational quality data for multistate manufacturing systems

Modern and intelligent manufacturing systems have a prominent multistate feature. However, previous studies of reliability analysis of multistate manufacturing systems mostly focused on the basic reliability of manufacturing systems but disregarded their operating characteristics, which has hindered the development of Prognostics and Health Management technique for intelligent manufacturing systems. Therefore, an evaluation approach of mission reliability for multistate manufacturing systems based on operational quality data is proposed in this paper. First, from the systematic viewpoint of the composition and operational principle of the manufacturing system, the relationship among production task execution state, production equipment degradation state, and produced product quality state is expounded, and the connotation of the mission reliability of multistate manufacturing systems is defined. Second, an extended state task network (ESTN) is presented to organise operational quality data by considering the quality state of work in process (WIP). Third, a fusion model of operational quality data for manufacturing systems is established with the aid of the ESTN, and an operational quality data-oriented evaluation method of mission reliability is been put forward. Finally, a case study of a manufacturing system for a cylinder head is conducted to verify the proposed approach.     

Towards zero-defect manufacturing (ZDM)—a data mining approach

The quality of a product is dependent on both facilities/equipment and manufacturing processes. Any error or disorder in facilities and processes can cause a catastrophic failure. To avoid such failures, a zero- defect manufacturing (ZDM) system is necessary in order to increase the reliability and safety of manufacturing systems and reach zero-defect quality of products. One of the major challenges for ZDM is the analysis of massive raw datasets. This type of analysis needs an automated and self-organized decision making system. Data mining (DM) is an effective methodology for discovering interesting knowledge within a huge datasets. It plays an important role in developing a ZDM system. The paper presents a general framework of ZDM and explains how to apply DM approaches to manufacture the products with zero-defect. This paper also discusses 3 ongoing projects demonstrating the practice of using DM approaches for reaching the goal of ZDM.     

Optimal process parameter determination for computer‐aided manufacturing

Producing high‐quality products at low cost is one of the key factors to survival for manufacturing sectors in today's intense global competition environment. One way to gain competitiveness is to integrate product design and process planning into one activity. This study attempts to determine optimal process parameters for a manufacturing process under given design parameters. The process parameters to be determined in this study include process means and process tolerances for particular manufacturing process sequences. The problem is formulated in constrained non‐linear optimization, considering both quality‐ and manufacturing‐related costs. The proposed application evaluates alternative product designs and process sequences so that the best associated process parameters can be determined during the early stages of design and planning. This makes the link between CAD and CAM systems more useful and effective. As a result, optimal integration of product design and process planning with minimal production costs and maximal product quality is possible. Copyright © 1999 John Wiley & Sons, Ltd.     

制造过程中质量控制系统的因果追溯性研究

针对制造过程质量控制系统可追溯性存在的问题,在质量控制系统中引入因果追溯性的概念,结合离散事件分析系统——DEVS的特点研究了基于DEVS的制造过程质量控制系统及其因果追溯性;按照T-DEVS的描述及其基本规则构建了制造过程的质量控制系统,并就此系统的因果追溯性进行阐述和定义.论文提供一种建立因果追溯树的方法,进而实现制造过程质量控制系统的因果追溯.     

Empirical relationships between some manufacturing practices and performance

Intense global competition, rapid technological changes, advances in manufacturing and information technology and discerning customers are forcing manufacturers to adopt manufacturing practices and competitive priorities that enable them to deliver high quality products in a short period of time. Identifying manufacturers’ competitive priorities and effective manufacturing practices has long been considered one of the key elements in manufacturing strategy research. This paper presents the results of a study conducted to identify some of the effective manufacturing practices that have a significant influence on manufacturing performance. This study also identifies the main competitive objectives of manufacturing industries that participated in the study. The results reported in this paper are based on data collected from a survey using a standard questionnaire administered to 1000 manufacturers in Australia. Evidence indicates that product quality and reliability are the main competitive factors for manufacturers and price has become surprisingly a relatively less important factor. Results show that simultaneous pursuit of advanced quality practices can neutralize the potential negative impacts of manufacturing difficulties and significantly improve product quality and manufacturing performance. Failure mode and effect analysis (FMEA) is shown to be an important tool for improving product quality and on time delivery performance. FMEA practice driven by the intention to improve customer satisfaction is more effective than that practised to fulfil customer requirements. Effective supplier relationships are shown to contribute positively to the manufacturing performance. The results also suggest that maintaining a supplier rating system and product data management and regularly updating them with field failure and warranty data are important manufacturing practices.