Academic excellence in manufacturing engineering

Manufacturing Systems are inherently complex and interdisciplinary, and are normally analyzed in a piecewise fashion using experimental techniques which provide relatively little physical insight or theoritical methods brrowed from other disciplines (e.g., structural mechanics, control theory, etc.). For these reasons Manufacturing Engineering is often considered an unscientific and intuitive subject. With the increasing demand for manufacturing systems to operate at higher production rates without human intervention to reduce manufacturing costs, it is becoming increasingly important to develop scientifically based, general, and efficient analysis tools specifically tailored to the complex interdisciplinary problems encountered in Manufacturing Engineering. In addition to having direct practical benefits, such tools would stimulate academic interest in this field and help alleviate current academic and industrial personnel shortages in the manufacturing area.

This paper describes one such analysis tool, the Dynamic Data System (D.D.S.) methodology, which has been developed at the University of Wisconsin. The D.D.S. methodology combines time series and systems analysis concepts in a computer-based modeling strategy for obtaining a physically meaningfully model of a system directly from input and output data in the form of stochastic difference/differential equations. The methodology can be applied to forecasting, control, system identification, characterization, signature analysis, and design. The basic features of the methodology, representative applications to the on-line detection and suppression of chatter in turning and the active compensation for roundness errors in boring, and areas for future development are discussed.     

Structured intelligence for self-organizing manufacturing systems

This paper deals with fuzzy scheduling and path planning problems by genetic algorithms. We have proposed a self-organizing manufacturing system (SOMS) that is composed of a number of autonomous modules. Each module decides output through interaction with other modules, but the module does not share complete information concerning other modules in the SOMS. Therefore, we require structured intelligence as a whole system. In this paper, we consider a manufacturing line composed of machining centres and conveyor units. The manufacturing procedure can be divided into a sequence of three modules: (a) tool locating module, (b) scheduling module, and (c) path planning module. The tool locating problems have been already solved. In this paper, we first solve the scheduling problem as global preplanning. Here we assume that the processing time is not constant, because some delay may occur in the machining centres. We therefore apply fuzzy theory to represent incomplete information abou t the machining time. We solve the fuzzy scheduling problem with a genetic algorithm. After global preplanning, the path planning module transports materials and products. Next, the scheduling module acquires the actual processing time of each machining centre. Based on the processing time, the schedule module generates a fuzzy number for the processing time. We discuss the effectiveness of the proposed method through the computer simulation results.     

操作型商业智能综述

为了为日常工作提供商业智能支持,研究了商业智能自身在发展过程中概念的转变,以及近年商业智能针对企业不同应用层次产生的新分类.研究了操作型商业智能的定义与定位,根据商业智能的通用架构和操作型商业智能的特点提出了通用的操作型商业智能系统的架构.对操作型商业智能组成模块的技术现状进行研究,分别研究了操作型商业智能与企业业务流程融合以及数据加载问题,重点对加快数据加载速度的技术进行了总结与归纳,结果表明了架构在技术上的可行性.     

Natural intelligence in design and manufacturing

This paper describes a hybrid intelligent system to implement and experiment with the “automated factory”. The objective of the project is to develop and test a new method of automating design and manufacturing by utilizing natural intelligence or more specifically, techniques such as fuzzy logic, Fuzzy Associative Memory (FAM), Backpropogation neural networks (BP), and Adaptive Resonance Theory (ART1).     

Hybrid artificial intelligence architecture for diagnosis and decision-making in manufacturing

This paper presents an architecture which combines artificial neural networks (ANNs) and an expert system (ES) into a hybrid, self-improving artificial intelligence (AI) system. The purpose of this project is to explore methods of combining multiple AI technologies into a hybrid intelligent diagnostic and advisory system. ANNs and ESs have different strengths and weaknesses, which can be exploited in such a way that they are complementary to each other: strengths in one system make up for weaknesses in the other, andvice versa. There is, presently, considerable interest in ways to exploit the strengths of these methodologies to produce an intelligent system which is more robust and flexible than one using either technology alone. Any process which involves both data-driven (bottom-up) and concept-driven (top-down) processing is especially well suited to displaying the capabilities of such a hybrid system. The system can take an incoming pattern of signals, as from various points in an automated manufacturing process, and make intelligent process control decisions on the basis of the pattern as preprocessed by the ANNs, with rule-based heuristic help or corroboration from the ES. Patterns of data from the environment which can be classified by either the ES or a human consultant can result in a high-level ANN being created and trained to recognize that pattern on future occurrences. In subsequent cases in which the ANN and the ES fail to agree on a decision concerning the environmental situation, the system can resolve those differences and retrain the networks and/or modify the models of the environment stored in the ES. Work on a hybrid system for perception in machine vision has been funded initially by an Oak Ridge National Laboratory seed grant, and most of the system components are operating presently in a parallel distributed computer environment.     

Swarm intelligence based techniques for digital filter design

This paper deals with the problem of digital IIR filter design. Two novel modifications are proposed to Particle Swarm Optimization and validated through novel application for design of IIR filter. First modification is based on quantum mechanics and proved to yield a better performance. The second modification is to take care of time dependency character of the constriction factor. Extensive simulation results validate the superior performance of proposed algorithms.     

Integration and control of intelligence in distributed manufacturing

The area of intelligent systems has generated a considerable amount of interest—occasionally verging on controversy—within both the research community and the industrial sector. This paper aims to present a unified framework for integrating the methods and techniques related to intelligent systems in the context of design and control of modern manufacturing systems. Particular emphasis is placed on the methodologies relevant to distributed processing over the Internet. Following presentation of a spectrum of intelligent techniques, a framework for integrated analysis of these techniques at different levels in the context of intelligent manufacturing systems is discussed. Integration of methods of artificial intelligence is investigated primarily along two dimensions: the manufacturing product life-cycle dimension, and the organizational complexity dimension. It is shown that at different stages of the product life-cycle, different intelligent and knowledge-oriented techniques are used, mainly because of the varied levels of complexity associated with those stages. Distribution of the system architecture or system control is the most important factor in terms of demanding the use of the most up-to-date distributed intelligence technologies. A tool set for web-enabled design of distributed intelligent systems is presented. Finally, the issue of intelligence control is addressed. It is argued that the dominant criterion according to which the level of intelligence is selected in technological tasks is the required precision of the resulting operation, related to the degree of generalization required by the particular task. The control of knowledge in higher-level tasks has to be executed with a strong involvement of the human component in the feedback loop. In order to facilitate the human intervention, there is a need for readily available, user-transparent computing and telecommunications infrastructure. In its final part, the paper discusses currently emerging ubiquitous systems, which combine this type of infrastructure with new intelligent control systems based on a multi-sensory perception of the state of the controlled process and its environment to give us tools to manage information in a way that would be most natural and easy for the human operator.     

The role of users and customers in digital innovation: Insights from B2B manufacturing firms

Diffusion of digital technologies into the manufacturing industry has created new opportunities for innovation that firms must address to remain competitive. We investigate the role of customer and user knowledge in the digital innovation processes of three global B2B manufacturing companies. We find that the B2B manufacturing industry's characteristics influence how users and customers may be leveraged. Customers making the purchasing decisions are considered for knowledge about short-term changes in market needs, while users working directly with the products provide long-term guidance for digital innovation. We identify practices for acquiring, distributing, and using customer and user knowledge for digital innovation.     

Emergence of intelligence in next-generation manufacturing systems

In the paper we propose a fundamental shift from the present manufacturing concepts and problem solving approaches towards new manufacturing paradigms involving phenomena such as emergence, intelligence, non-determinism, complexity, self-organization, bottom-up organization, and coexistence with the ecosystem. In the first part of the paper we study the characteristics of the past and the present manufacturing concepts and the problems they caused. According to the analogy with the terms in cognitive psychology four types of problems occurring in complex manufacturing systems are identified. Then, appropriateness of various intelligent systems for solving of these four types of problems is analyzed. In the second part of the paper, we study two completely different problems. These two problems are (1) identification of system in metal forming industry and (2) autonomous robot system in manufacturing environment. A genetic-based approach that imitates integration of living cells into tissues, organs, and organisms is used. The paper clearly shows how the state of the stable global order (i.e., the intelligence) of the overall system gradually emerges as a result of low-level interactions between entities of which the system consists and the environment.     

Manufacturing intelligence to forecast and reduce semiconductor cycle time

Semiconductor manufacturing is one of the most complicated production processes with the challenges of dynamic job arrival, job re-circulation, shifting bottlenecks, and lengthy fabrication process. Owing to the lengthy wafer fabrication process, work in process (WIP) usually affects the cycle time and throughput in the semiconductor fabrication. As the applications of semiconductor have reached the era of consumer electronics, time to market has played an increasingly critical role in maintaining a competitive advantage for a semiconductor company. Many past studies have explored how to reduce the time of scheduling and dispatching in the production cycle. Focusing on real settings, this study aims to develop a manufacturing intelligence approach by integrating Gauss-Newton regression method and back-propagation neural network as basic model to forecast the cycle time of the production line, where WIP, capacity, utilization, average layers, and throughput are rendered as input factors for indentifying effective rules to control the levels of the corresponding factors as well as reduce the cycle time. Additionally, it develops an adaptive model for rapid response to change of production line status. To evaluate the validity of this approach, we conducted an empirical study on the demand change and production dynamics in a semiconductor foundry in Hsinchu Science Park. The approach proved to be successful in improving forecast accuracy and realigning the desired levels of throughput in production lines to reduce the cycle time.     

Discrete Walsh operational matrices for analysis and optimal control of linear digital systems

By the introduction of the shift transformation matrix, direct product matrix and summation matrix of the discrete Walsh series, the analysis of time-varying digital control systems is facilitated and the approximate solution of time-invariant digital optimal control problems is achieved of this study. The design algorithms of digital optimal control are based on the discrete variational principle combined with the idea of penalty functions to obtain the conveniently computational formulations for evaluating the optimal control and trajectory. Three examples are illustrated by using the discrete Walsh approach.