Japan is now making plans for the fifth generation of computers which will enable them to establish and dominate a ''knowledge industry''. The authors explain the new knowledge technology and outline America's weak, almost nonexistent response to the Japanese challenge. Contents, abridged: It's not just the second computer revolution, it's the important one. The Japanese fifth general. The nations. The American response. Epilog, or it is hard to predict, especially the future. Index.

Excerpts from The Fifth Generation: artificial intelligence and Japan's computer challenge to the world

Diagnostic expert system applications continue to be the most popular.Synthetic type problem domains (such as design and planning) tend you yield higher impact systems.Rule-based knowledge representations tend you yield higher impact expert system applications.Unstructured interviews are not commonly used now for knowledge acquisition.In the past, structured and unstructured interviews tended to yield systems with equally high impact. Research in Expert Systems (ES) has been one of the longest-running, and most successful areas of ongoing research within the AI field. Since the 1980s, many case studies of ES applications have been published covering a wide range of functional areas and problem domains. These case studies contain an enormous amount of information about how ESs have been developed and how the tools, concepts, and applications have evolved since their inception. This research has painstakingly collected and analysed the content of 311 ES case studies dating from 1984 through 2016. A detailed content analysis was performed on this corpus in order to capture as many details as possible from each case. Further value was added to the study by using an impact scale to try and gauge the impact or success of the resulting application. With such a large sample size, the results are helpful in identifying how ES research has evolved and areas for further research.

Trends in expert system development

Lean supply chain management is a relatively new concept resulting from the integration of lean philosophy into supply chain management. Decision making in a lean supply chain context is challenging because of the complexity, dynamics, and uncertainty inherent to both supply networks and the types of waste (defined as any processes, including use of resources, which do not add value to customers). Efficient knowledge management has been identified as one of the key requirements to achieve integrated support for lean supply chain decisions. This paper proposes a decision-focused knowledge framework including a multi-layer knowledge model (to capture the know-why and know-with together with the know-what and know-how), a knowledge matrix for knowledge elicitation, and a decision tree for the design of the knowledge base. A knowledge system for lean supply chain management (KSLSCM) has been developed using artificial intelligence system shells VisiRule and Flex. The KSLSCM has five core components: a supply ...

A decision-focused knowledge management framework to support collaborative decision making for lean supply chain management

In the manufacturing industry, for example, the pharmaceutical industry, a thorough understanding of the process is necessary in addition to a properly designed monitoring and analysis system (PAT system) to consistently obtain the desired end-product properties. A model-based computer-aided framework including the methods and tools through which the design of monitoring and analysis systems for product quality control can be generated, analyzed and/or validated, has been developed. Two important supporting tools developed as part of the framework are a knowledge base and a model library. The knowledge base provides the necessary information/data during the design of the PAT system while the model library generates additional or missing data needed for design and analysis. Optimization of the PAT system design is achieved in terms of product data analysis time and/or cost of monitoring equipment subject to the maintenance constraints of the desired product quality. The application of the model-based framework is highlighted through a case study involving the operation of a fermentation process.

Model-based Computer Aided Framework for Design of Process Monitoring and Analysis Systems

https://backend.orbit.dtu.dk/ws/files/5034408/Ravendra%20Singh_PEC09-47.pdf

Model-based computer-aided framework for design of process monitoring and analysis systems

Increasing global competition has made many manufacturing companies recognize that competitive manufacturing in terms of low cost and high quality is crucial for success. Real-time process control and production optimization are, however, extremely challenging areas because manufacturing processes are getting ever more complex and involve many different parameters. This is a major problem when building decision support systems especially in electronics manufacturing. Although problem-solving is a knowledge intensive activity undertaken by people on the production floor, it is quite common to have large databases and run blindly feature extraction and data mining methods. Performance of these methods could, however, be drastically increased when combined with knowledge or expertise of the process. This paper describes how defect-related knowledge on an electronic assembly line can be integrated in the decision making process at an operational and organizational level. It focuses in particular on the efficient acquisition of shallow knowledge concerning everyday human interventions on the production lines, as well as on the factory-wide sharing of the resulting information for an improved defect management. Software with dedicated interfaces has been developed using a knowledge representation that supports portability and flexibility of the system. Semi-automatic knowledge acquisition from the production floor and generation of comprehensive reports for the quality department resulted in an improvement of the usability, usage, and usefulness of the decision support system.

Knowledge acquisition for decision support systems on an electronic assembly line

/pdf/production-optimization-on-pcb-assembly-lines-using-discrete-haeuodiowp.pdf

Production optimization on pcb assembly lines using discrete-event simulation

This paper describes how Linguistic Equations, an intelligent method derived from Fuzzy Algorithms, have been used in a decision-helping tool adapted to the specific needs of electronics manufacturing. In our case the company involved in the project, PKC Group, is mainly producing control cards for the telecommunication and automotive industry. In their business, nearly 70 percent of the cost of a product is material cost. Detecting defects and repairing the Printed Circuit Boards is therefore a necessity. With an ever increasing complexity of the products, defects are very likely to occur, no matter how much attention is put into their prevention. The work focused therefore on defect detection during the final testing of the product. The approach is based on experience using intelligent methods such as Fuzzy Logic or Linguistic Equations in fault diagnosis. An intelligent system based on expert knowledge was developed for analyzing test data. This analysis emphasizes localization of the defective components more than possible causes of those defects. Expert knowledge was essential for the development of the system as the number of defects is too low for a data-based approach. According to the first results, the system is successful for new products, even in the ramp-up stage. On the other hand, the underlying methodology provides techniques for tuning the tool parameters when amount of testing data increases. Diagnosis effectiveness can therefore be improved from detection of a functional area towards component level analysis. This report is engineer oriented, which means that it has to stay simple and accessible to people without research background. In the first part of this report, we will give a short description of the different methods used in the project with exemples from the case study. The second part concentrates on what has been implemented in the company, what were the needs and what are the improvements that are expected from this work.

/pdf/defect-localization-on-a-pcb-with-functional-testing-3upiukv8zz.pdf

Defect localization on a PCB with functional testing

Increasing globalization of the economy is imposing tough challenges to manufacturing companies. The ability to produce highly customized products, in order to satisfy market niches, requires the introduction of new features in automation systems. Flexible manufacturing processes must be able to handle unforeseen events, but their complexity makes the supervision and maintenance task difficult to perform by human operators. This paper describes how linguistic equations (LE), an intelligent method derived from Fuzzy Algorithms, has been used in a decision-helping tool for electronic manufacturing. In our case the company involved in the project is mainly producing control cards for the automotive industry. In their business, nearly 70% of the cost of a product is material cost. Detecting defects and repairing the printed circuit boards is therefore a necessity. With an ever increasing complexity of the products, defects are very likely to occur, no matter how much attention is put into their prevention. Therefore, the system described in this paper comes into use only during the final testing of the product and is purely oriented towards the detection and localization of defects. Final control is based on functional testing. Using linguistic equations and expert knowledge, the system is able to analyze that data and successfully detect and trace a defect in a small area of the printed circuit board. If sufficient amount of data is provided, self-tuning and self-learning methods can be used. Diagnosis effectiveness can therefore be improved from detection of a functional area towards component level analysis.

Knowledge-based linguistic equations for defect detection through functional testing of printed circuit boards

Real-time process control and production optimization are extremely challenging areas. Traditional approaches often lack in robustness or reliability when dealing with incomplete, inaccurate, or simply irrelevant data. This is a major problem when building decision support systems especially in electronics manufacturing, where blind feature extraction and data mining methods on large databases are common. Performance of these methods can be drastically increased when combined with knowledge or expertise of the process. This paper describes how defect-related knowledge on an electronic assembly line can be integrated in the decision making process at an operational and organizational level. It focuses in particular on the acquisition of shallow knowledge concerning everyday human interventions on the production lines, as well as on the conceptualization and factory wide sharing of the resulting defect information. Software with dedicated interfaces has been developed for that purpose. Semi-automatic knowledge acquisition from the production floor and generation of comprehensive reports for the quality department resulted in an improvement of the usability, usage, and usefulness of the decision support system.

Sébastien Gebus

Papers

Knowledge acquisition for decision support systems on an electronic assembly line

Production optimization on pcb assembly lines using discrete-event simulation

Defect localization on a PCB with functional testing

Knowledge-based linguistic equations for defect detection through functional testing of printed circuit boards

Defect-related knowledge acquisition for decision support systems in electronics assembly