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

In a computer system, a user interacts with a plurality of applications using natural language text provided by an input device. In order to enable the applications for user interaction, rules are registered on a blackboard of an arbiter. The arbiter can be localized, or distributed. Each rule is identified with a particular application, and can include a callback procedure. Each rule includes a left-hand side symbol and a right-hand side string which is a translation of the symbol. A parser parses a natural language input text string using the register rules to generate an evaluation tree. The evaluation tree is expressed in the form of an interpretive language. The evaluation tree is interpreted to produce at least one application request. The application request is generated by the callback procedure associated with a specific one of the rules applied during the parsing of the input text. The request is transmitted to the application identified with the applied rule.

Application user interface

For the past few years, electronics manufacturing may have been the victim of its own success. Unlike in heavier industries, rationalization is a concept that was unknown in the sector until only a few years ago and even now, many companies are struggling with cost-cutting measures. Production systems in electronics manufacturing need to be highly flexible because of a varying and evolving environment. Therefore real-time process control and, possibly as a result, production optimization are extremely challenging areas. Traditional approaches often do not work due to a lack of robustness or reliability. For this reason, a new generation of decision support systems is needed in response to some specific problems. The thesis addresses topics such as design of intelligent interfaces for knowledge acquisition and elicitation, use of that knowledge for improved data analysis and diagnostics, realtime feedback control, self-tuning capabilities, and evaluation of optimization methods in discrete processes. Topics covered therefore include the whole scope of a decision support system, from its design through to the evaluation of its performance as well as interaction capabilities as a vehicle for sharing information. The aim of this research is to streamline the development of a new generation of decision support systems by providing tools and methods for a better integration of knowledge in an evolving environment. The main interest lies not only in improved data analysis, but also in better formalization and use of diagnosis. Case studies presented in this thesis demonstrate the practical feasibility of such an approach.

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Knowledge-based decision support systems for production optimization and quality improvement in the electronics industry

(1) Background: In recent years, many studies regarding the issues of improving the management and effectiveness of the maintenance department of manufacturing companies, in the context Industry 4.0, have been published. This makes it necessary to establish a research gap in the approach to obtaining support in realising management tasks in the maintenance area in the selection of appropriate employees to perform the given activities. (2) Methods: This article uses literature studies and empirical research results from manufacturing companies, in order to determine the approach in supporting the selection of maintenance experts. In the approach, the method used—which is based on rules should there be future any formalisation of the data—is also the Fuzzy Analytic Hierarchy Process (FAHP), which analyses the importance of a given competence, within a manufacturing resource, to undertake repairs. (3) Results: The innovative approach towards the selection of expert workers in a maintenance department is created, in part, in the form of an implemented web-application. The novelty of the “maintenance expert selection map", so-called, is the provision of formal procedures for describing the competence of each maintenance worker and defining the best “state of nature”. (4) Conclusions: In the research that is presented here, the practicality for maintenance managers in the “maintenance expert selection map" was established. This map describes the competence of workers for selecting them for repair work within a given manufacturing resource; the scope of employee training was also determined in this research.

An Approach to Supporting the Selection of Maintenance Experts in the Context of Industry 4.0

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

The author discusses how knowledge acquisition will become a key element to improving the design, production, and testing of complex electronic, mechanical, and hydraulic systems. Specifically, knowledge acquisition will become critical to the use of dependency model systems. The author also discusses a means for automating knowledge acquisition and integrating it into the design process, and presents NASA's CAD/CAE knowledge base development tool (KBDT) as a prototype for demonstrating the concept of automated knowledge acquisition. KBDT's basic structure, its operation, and its integration with NASA's knowledge-based autonomous test engineer (KATE), are described. KBDT's unique combination of voice synthesis, voice recognition, natural language processing, and knowledge acquisition processing components, integrated via a blackboard architecture, necessary for this knowledge engineering application, are outlined. >

G.B. Winter

Papers

An automated knowledge acquisition system for model-based diagnostics