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Journal articles on the topic 'Expert systems (Computer science) – Industrial applications'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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1

Rosenman, Michael A., John S. Gero, Peter J. Hutchinson, and Rivka Oxman. "Expert systems applications in computer-aided design." Computer-Aided Design 18, no. 10 (December 1986): 546–51. http://dx.doi.org/10.1016/0010-4485(86)90043-6.

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2

Rosenman, Michael A., John S. Gero, Peter J. Hutchinson, and Rivka Oxman. "Expert systems applications in computer-aided design." Computer-Aided Design 18, no. 7 (September 1986): 392–93. http://dx.doi.org/10.1016/0010-4485(86)90248-4.

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3

Stevenson, T. H., D. A. Plath, and C. M. Bush. "Using expert systems in industrial marketing." ISA Transactions 30, no. 3 (January 1991): 81–86. http://dx.doi.org/10.1016/0019-0578(91)90029-5.

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4

Furuta, Hitoshi, King-Sun Tu, and James T. P. Yao. "Structural engineering applications of expert systems." Computer-Aided Design 17, no. 9 (November 1985): 410–19. http://dx.doi.org/10.1016/0010-4485(85)90288-x.

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5

BASU, ANUPAM, ARUN K. MAJUMDAR, and SATYABROTO SINHA. "Design of industrial regulators using expert systems approach." International Journal of Systems Science 22, no. 3 (March 1991): 551–77. http://dx.doi.org/10.1080/00207729108910633.

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6

Fedderwitz, Walter, and Thies Wittig. "Real-Time Expert Systems for Industrial Control." Integrated Computer-Aided Engineering 2, no. 3 (July 1, 1995): 187–202. http://dx.doi.org/10.3233/ica-1995-2303.

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7

Cardeñosa, J., F. Alonso, J. Castellanos, and J. Garcia. "The application of deep models in industrial expert systems." Expert Systems with Applications 2, no. 2-3 (January 1991): 187–94. http://dx.doi.org/10.1016/0957-4174(91)90115-u.

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8

Kathawala, Yunus. "Expert Systems: Implications for Operations Management." Industrial Management & Data Systems 90, no. 6 (June 1, 1990): 12–16. http://dx.doi.org/10.1108/02635579010004161.

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Several examples of successful expert systems applications are presented. Examples of expert systems as applied in process planning, operations planning, inventory control, process design, quality control and scheduling are covered, and the performance of these expert systems is described. Expert systems will become an essential part of computer‐integrated manufacturing (CIM) and flexible manufacturing systems (FMS) because they can perform several of the tasks mentioned above.

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9

Ruttkay, Z. "Expert systems in computer-aided design." Computer-Aided Design 21, no. 9 (November 1989): 596. http://dx.doi.org/10.1016/0010-4485(89)90022-5.

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10

Rao, Mohan P., and David M. Miller. "Expert systems applications for productivity analysis." Industrial Management & Data Systems 104, no. 9 (December 2004): 776–85. http://dx.doi.org/10.1108/02635570410567766.

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11

Gero, John. "Expert systems in CAD." Computer-Aided Design 17, no. 9 (November 1985): 396–98. http://dx.doi.org/10.1016/0010-4485(85)90286-6.

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12

Byrd, Terry Anthony. "Expert systems implementation:." Industrial Management & Data Systems 95, no. 10 (December 1995): 3–7. http://dx.doi.org/10.1108/02635579510101447.

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13

Davies, B. J. "Expert systems." International Journal of Advanced Manufacturing Technology 2, no. 1 (February 1987): 1–2. http://dx.doi.org/10.1007/bf02601464.

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14

Davies, B. J., I. L. Darbyshire, and A. J. Wright. "Expert systems in process planning." Computer-Aided Design 18, no. 7 (September 1986): 389. http://dx.doi.org/10.1016/0010-4485(86)90227-7.

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15

Howard, H. C., and D. R. Rehak. "Expert systems and CAD databases." Computer-Aided Design 18, no. 7 (September 1986): 393. http://dx.doi.org/10.1016/0010-4485(86)90249-6.

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16

Kourtz, Peter. "Artificial intelligence: a new tool for forest management." Canadian Journal of Forest Research 20, no. 4 (April 1, 1990): 428–37. http://dx.doi.org/10.1139/x90-060.

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Articicial intelligence is a new science that deals with the representation, automatic acquisition, and use of knowledge. Artificial intelligence programs attempt to emulate human thought processes such as deduction, inference, language, and visual recognition. The goal of artificial intelligence is to make computers more useful for reasoning, planning, acting, and communicating with humans. Development of artificial intelligence applications involves the integration of advanced computer science, psychology, and sometimes robotics. Of the subfields that artificial intelligence can be broken into, the one of most immediate interest to forest management is expert systems. Expert systems involve encoding knowledge usually derived from an expert in a narrow subject area and using this knowledge to mimic his decision making. The knowledge is represented usually in the form of facts and rules, involving symbols such as English words. At the core of these systems is a mechanism that automatically searches for and pieces together the facts and rules necessary to solve a specific problem. Small expert systems can be developed on common microcomputers using existing low-cost commercial expert shells. Shells are general expert systems empty of knowledge. The user merely defines the solution structure and adds the desired knowledge. Larger systems usually require integration with existing forestry data bases and models. Their development requires either the relatively expensive expert system development tool kits or the use of one of the artificial intelligence development languages such as lisp or PROLOG. Large systems are expensive to develop, require a high degree of skill in knowledge engineering and computer science, and can require years of testing and modification before they become operational. Expert systems have a major role in all aspects of Canadian forestry. They can be used in conjunction with conventional process models to add currently lacking expert knowledge or as pure knowledge-based systems to solve problems never before tackled. They can preserve and accumulate forestry knowledge by encoding it. Expert systems allow us to package our forestry knowlege into a transportable and saleable product. They are a means to ensure consistent application of policies and operational procedures. There is a sense of urgency associated with the integration of artificial intelligence tools into Canadian forestry. Canada must awaken to the potential of this technology. Such systems are essential to improve industrial efficiency. A possible spin-off will be a resource knowledge business that can market our forestry knowledge worldwide. If we act decisively, we can easily compete with other countries such as Japan to fill this niche. A consortium of resource companies, provincial resource agencies, universities, and federal government laboratories is required to advance this goal.

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17

Jain, D., and M. L. Mahe. "Combining expert systems and CAD techniques." Computer-Aided Design 21, no. 3 (April 1989): 184. http://dx.doi.org/10.1016/0010-4485(89)90087-0.

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18

Subramoniam, Suresh. "Expert Systems: Guidelines for Managers." Industrial Management & Data Systems 92, no. 4 (April 1992): 23–25. http://dx.doi.org/10.1108/02635579210012269.

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19

Rees, Patricia L. "User Evaluation of Expert Systems." Industrial Management & Data Systems 92, no. 6 (June 1992): 17–23. http://dx.doi.org/10.1108/02635579210015392.

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20

Motiwalla, Luvai F., and Vidyaranya B. Gargeya. "Expert Systems in Service Operations." Industrial Management & Data Systems 92, no. 8 (August 1992): 14–19. http://dx.doi.org/10.1108/02635579210019820.

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21

Rees, Patricia L. "User Participation in Expert Systems." Industrial Management & Data Systems 93, no. 6 (June 1993): 3–7. http://dx.doi.org/10.1108/02635579310040906.

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22

Zeng, L., and H. P. Wang. "A patchboard-based expert-systems model for manufacturing applications." International Journal of Advanced Manufacturing Technology 7, no. 1 (January 1992): 38–43. http://dx.doi.org/10.1007/bf02602949.

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23

Rehak, Daniel R., and H. Craig Howard. "Interfacing expert systems with design databases in integrated CAD systems." Computer-Aided Design 17, no. 9 (November 1985): 443–54. http://dx.doi.org/10.1016/0010-4485(85)90292-1.

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24

Thuluva, Aparna Saisree, Darko Anicic, Sebastian Rudolph, and Malintha Adikari. "Semantic Node-RED for rapid development of interoperable industrial IoT applications." Semantic Web 11, no. 6 (October 29, 2020): 949–75. http://dx.doi.org/10.3233/sw-200405.

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The evolution of IoT has revolutionized industrial automation. Industrial devices at every level such as field devices, control devices, enterprise level devices etc., are connected to the Internet, where they can be accessed easily. It has significantly changed the way applications are developed on the industrial automation systems. It led to the paradigm shift where novel IoT application development tools such as Node-RED can be used to develop complex industrial applications as IoT orchestrations. However, in the current state, these applications are bound strictly to devices from specific vendors and ecosystems. They cannot be re-used with devices from other vendors and platforms, since the applications are not semantically interoperable. For this purpose, it is desirable to use platform-independent, vendor-neutral application templates for common automation tasks. However, in the current state in Node-RED such reusable and interoperable application templates cannot be developed. The interoperability problem at the data level can be addressed in IoT, using Semantic Web (SW) technologies. However, for an industrial engineer or an IoT application developer, SW technologies are not very easy to use. In order to enable efficient use of SW technologies to create interoperable IoT applications, novel IoT tools are required. For this purpose, in this paper we propose a novel semantic extension to the widely used Node-RED tool by introducing semantic definitions such as iot.schema.org semantic models into Node-RED. The tool guides a non-expert in semantic technologies such as a device vendor, a machine builder to configure the semantics of a device consistently. Moreover, it also enables an engineer, IoT application developer to design and develop semantically interoperable IoT applications with minimal effort. Our approach accelerates the application development process by introducing novel semantic application templates called Recipes in Node-RED. Using Recipes, complex application development tasks such as skill matching between Recipes and existing things can be automated. We will present the approach to perform automated skill matching on the Cloud or on the Edge of an automation system. We performed quantitative and qualitative evaluation of our approach to test the feasibility and scalability of the approach in real world scenarios. The results of the evaluation are presented and discussed in the paper.

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25

Radford, A. D., and J. S. Gero. "Towards generative expert systems for architectural detailing." Computer-Aided Design 17, no. 9 (November 1985): 428–35. http://dx.doi.org/10.1016/0010-4485(85)90290-8.

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26

Smith, R. W. "Some logic modelling strategies for expert systems." Computer-Aided Design 18, no. 7 (September 1986): 393. http://dx.doi.org/10.1016/0010-4485(86)90251-4.

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27

Ajmal, A. "Clear and complete review of expert systems." Computer-Aided Design 21, no. 7 (September 1989): 469. http://dx.doi.org/10.1016/0010-4485(89)90135-8.

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28

Parkinson, Edward L., Max L. Hailey, Ching F. Lo, Bruce A. Whitehead, George Z. Shi, and George W. Garrison. "Integration architecture of expert systems, neural networks, hypertext, and multimedia can provide competitive opportunities for industrial applications." Computers & Industrial Engineering 27, no. 1-4 (September 1994): 269–72. http://dx.doi.org/10.1016/0360-8352(94)90287-9.

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29

Stone, Robert W., and David J. Good. "Expert systems in the marketing organization." Industrial Management & Data Systems 95, no. 4 (May 1995): 3–7. http://dx.doi.org/10.1108/02635579510086661.

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30

Martin, C. J. "Expert Systems in a Managerial Context." Industrial Management & Data Systems 85, no. 11/12 (November 1985): 6–8. http://dx.doi.org/10.1108/eb057419.

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31

Rees, Patricia L. "PRACTICAL CONSIDERATIONS ASSOCIATED WITH EXPERT SYSTEMS." Industrial Management & Data Systems 88, no. 1/2 (January 1988): 3–6. http://dx.doi.org/10.1108/eb057498.

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32

Jakob, F., and D. Vernet. "Application of expert systems to process control." Robotics and Computer-Integrated Manufacturing 3, no. 2 (January 1987): 239–43. http://dx.doi.org/10.1016/0736-5845(87)90108-6.

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33

Metaxiotis, K., and John Psarras. "Expert systems in business: applications and future directions for the operations researcher." Industrial Management & Data Systems 103, no. 5 (July 2003): 361–68. http://dx.doi.org/10.1108/02635570310477.

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34

Iwata, K. "Application of expert systems to manufacturing in Japan." International Journal of Advanced Manufacturing Technology 3, no. 3 (July 1988): 23–37. http://dx.doi.org/10.1007/bf02601588.

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35

Pherson, D., H. Fillebeen, and P. Savigné. "Configuring and pricing with expert systems." International Journal of Advanced Manufacturing Technology 9, no. 4 (July 1994): 253–62. http://dx.doi.org/10.1007/bf01751123.

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36

Ritter, Noah, and Jeremy Straub. "Implementation of Hardware-Based Expert Systems and Comparison of Their Performance to Software-Based Expert Systems." Machines 9, no. 12 (December 17, 2021): 361. http://dx.doi.org/10.3390/machines9120361.

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Expert systems are a form of highly understandable artificial intelligence that allow humans to trace the decision-making processes that are used. While they are typically software implemented and use an iterative algorithm for rule-fact network processing, this is not the only possible implementation approach. This paper implements and evaluates the use of hardware-based expert systems. It shows that they work accurately and can be developed to parallel software implementations. It also compares the processing speed of software and hardware-based expert systems, showing that hardware-based systems typically operate two orders of magnitude faster than the software ones. The potential applications that hardware-based expert systems can be used for and the capabilities that they can provide are discussed.

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37

Heygate, Richard. "Expert Systems: Are they Right for your Business?" Industrial Management & Data Systems 86, no. 9/10 (September 1986): 18–20. http://dx.doi.org/10.1108/eb057455.

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38

Maher, Mary Lou. "HI-RISE and beyond: directions for expert systems in design." Computer-Aided Design 17, no. 9 (November 1985): 420–27. http://dx.doi.org/10.1016/0010-4485(85)90289-1.

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39

Polaków, Grzegorz, Jacek Czeczot, and Piotr Laszczyk. "Agent-based approach to continuous process control for enabling parallelization of engineering cycles." Concurrent Engineering 26, no. 3 (December 21, 2017): 287–98. http://dx.doi.org/10.1177/1063293x17746714.

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Modern manufacturing and production systems have growing demands in energy and cost savings, which can be ensured using more advanced control algorithms at the regulatory-level industrial control loops. However, developing such algorithms requires case-dependent approach that involves complex mathematics and expertise in various fields of technology and engineering. Gathering all the needed experts to conduct the control system engineering cycle is nearly impossible for organizational and economic reasons. Thus, in this work, it is proposed to employ an agent-based approach, which is substantially different than the conventional engineering cycles for developing the static control system. The idea is to split the entire control design procedure into smaller tasks of developing the modules (i.e. agents), which encapsulate the expert knowledge (e.g. on sensor failure detection, input signal modelling and estimation). It enables clear division of the competences between the experts and allows for dynamic inclusion of the expert knowledge into the newly designed or already existing system. Therefore, the expertise may be distributed both in location and in time, which stands in contradiction to the static approach based on sequential engineering cycles.

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40

Weck, M., and G. Kiratli. "Applicability of expert systems to flexible manufacturing." Robotics and Computer-Integrated Manufacturing 3, no. 1 (January 1987): 97–103. http://dx.doi.org/10.1016/0736-5845(87)90011-1.

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41

Pham, D. T., and P. T. N. Pham. "Expert systems in mechanical and manufacturing engineering." International Journal of Advanced Manufacturing Technology 3, no. 3 (July 1988): 3–21. http://dx.doi.org/10.1007/bf02601587.

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42

Mack, Daniel L. C., Gautam Biswas, Hamed Khorasgani, Dinkar Mylaraswamy, and Raj Bharadwaj. "Combining expert knowledge and unsupervised learning techniques for anomaly detection in aircraft flight data." at - Automatisierungstechnik 66, no. 4 (April 25, 2018): 291–307. http://dx.doi.org/10.1515/auto-2017-0120.

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AbstractFault detection and isolation schemes are designed to detect the onset of adverse events during operations of complex systems, such as aircraft, power plants, and industrial processes. In this paper, we combine unsupervised learning techniques with expert knowledge to develop an anomaly detection method to find previously undetected faults from a large database of flight operations data. The unsupervised learning technique combined with a feature extraction scheme applied to the clusters labeled as anomalous facilitates expert analysis in characterizing relevant anomalies and faults in flight operations. We present a case study using a large flight operations data set, and discuss results to demonstrate the effectiveness of our approach. Our method is general, and equally applicable to manufacturing processes and other industrial applications.

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43

Kaula, Rajeev, and Leslie C. Lander. "A module‐based conceptual framework for largescale expert systems." Industrial Management & Data Systems 95, no. 2 (March 1995): 15–23. http://dx.doi.org/10.1108/02635579510082511.

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44

Eppinette, Matt, R. Anthony Inman, and Roger Alan Pick. "Expert systems and the implementation of quality customer service." Industrial Management & Data Systems 97, no. 2 (March 1997): 63–68. http://dx.doi.org/10.1108/02635579710161377.

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45

Fagan, Michael J. "Expert systems applied to mechanical engineering design — experience with bearing selection and application program." Computer-Aided Design 19, no. 7 (September 1987): 361–67. http://dx.doi.org/10.1016/0010-4485(87)90036-4.

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46

Pratihar, Dilip Kumar. "Expert systems in manufacturing processes using soft computing." International Journal of Advanced Manufacturing Technology 81, no. 5-8 (May 14, 2015): 887–96. http://dx.doi.org/10.1007/s00170-015-7285-x.

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47

Harris, Portia J. "An expert systems technology approach to maintenance proficiency." Robotics and Computer-Integrated Manufacturing 11, no. 3 (September 1994): 195–99. http://dx.doi.org/10.1016/0736-5845(94)90034-5.

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48

Chakraborty, Shankar, and Kanika Prasad. "A QFD-based expert system for industrial truck selection in manufacturing organizations." Journal of Manufacturing Technology Management 27, no. 6 (July 4, 2016): 800–817. http://dx.doi.org/10.1108/jmtm-02-2016-0020.

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Purpose Availability of accurate quantity of materials, at correct place and at right time is extremely critical for increasing production effectiveness of any manufacturing organization. This can be achieved through employing an appropriate material handling equipment (MHE) capable of performing the desired operation. Therefore, choosing a right MHE from the available options is a key concern for the success, growth and competitiveness of a manufacturing organization. The purpose of this paper is to describe the design and development of an expert system based on quality function deployment (QFD) methodology in Visual Basic 6.0 for selecting the most appropriate industrial truck which is a commonly practiced MHE in any manufacturing organization. Design/methodology/approach A QFD-based approach is adopted to incorporate customers’ needs into the evaluation criteria on the basis of which industrial truck selection is carried out. The applicability of the developed expert system in solving industrial truck selection problems is demonstrated using two illustrative examples. Findings While applying this QFD-based model, CPCD 80x manufactured by Heli is recognized as the most suitable forklift truck for transporting unitized loads within a manufacturing unit with some spatial constraints, and for loading/unloading packages/boxes/cartons and place them at the desired locations in a manufacturing unit, ETV 216 manufactured by Jungheinrich evolves out as the most suitable reach truck. Originality/value Till date, numerous research articles have been published suggesting the applications of various mathematical models, multi-criteria decision-making methods and knowledge-based systems for solving MHE selection problems, and it is intriguing to note that none of the previously adopted methods has proposed a systematic procedure for selection of the evaluation criteria and interrelated the needs of customers with the technical specifications of MHEs while identifying the best alternative for performing a specified operation. These issues can be addressed through application of this developed QFD-based expert system, which can translate customers’ needs into organizational functions that are implementable in the decision-making/selection procedure.

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49

Paton, Robert. "Managing the Expert." Industrial Management & Data Systems 89, no. 4 (April 1989): 20–23. http://dx.doi.org/10.1108/02635578910132800.

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50

Ito, Yoshimi, Hidenori Shinno, and Hideo Saito. "A proposal for CAD/CAM interface with expert systems." Robotics and Computer-Integrated Manufacturing 4, no. 3-4 (January 1988): 491–97. http://dx.doi.org/10.1016/0736-5845(88)90021-x.

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