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Journal articles on the topic 'Engineering design reasoning'

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Published: 10 December 2022

Last updated: 28 January 2023

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1

Howard, H. Craig, Jenmu Wang, Francois Daube, and Taufiq Rafiq. "Applying design-dependent knowledge in structural engineering design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 3, no. 2 (May 1989): 111–23. http://dx.doi.org/10.1017/s0890060400001141.

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Recent knowledge-based expert systems for structural engineering design have focused on design-independent knowledge (abstract reasoning rules for designing), and while great strides have been made in that area, there is still a significant need to develop systems to take advantage of the wealth of knowledge contained in every substantial structural design. On the other hand, previous database-oriented design efforts have focused primarily on knowledge-poor databases of solutions, in which the traditional engineering handbook of solutions has simply been replaced by digital data. The challenge is to find a way to capture and apply the kind of case-based, design-dependent knowledge that structural engineers have traditionally used. The long-term results will be better structural designs and better structural designers. This paper discusses the character of the design-dependent knowledge in a structural engineering context, describes two initial applications of case-based reasoning to component design, and presents a general paradigm for a knowledge-based design system integrating rule-based and case-based reasoning.

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Mayes, Robert, Bryon Gallant, and Emma Fettes. "Interdisciplinary STEM through Engineering Design-based Reasoning." International Journal of Engineering Pedagogy (iJEP) 8, no. 3 (May 28, 2018): 60. http://dx.doi.org/10.3991/ijep.v8i3.8026.

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Interdisciplinary STEM programs are in demand for United States middle schools (ages 11 to 13 years) and high schools (ages 14 to 18). The Real STEM Project collaborated with 12 schools to develop and implement such programs. We open with a description of the project, including the 21st century STEM reasoning abilities that were proposed as learning outcomes for the STEM programs. We then focus on one of the five reasoning abilities, engineering design-based reasoning, since engineering often serves as a driver for STEM programs. An exemplar of engineering design as a driver for STEM from one of the participating teacher’s classrooms is provided, and a summary of teaching practices supporting interdisciplinary STEM is drawn from the example.

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Stéphane, Negny, and Le Lann Jean Marc. "Case-based reasoning for chemical engineering design." Chemical Engineering Research and Design 86, no. 6 (June 2008): 648–58. http://dx.doi.org/10.1016/j.cherd.2008.02.011.

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4

Kannapan, Srikanth M., and Kurt M. Marshek. "Design synthetic reasoning." Mechanism and Machine Theory 26, no. 7 (January 1991): 711–39. http://dx.doi.org/10.1016/0094-114x(91)90033-z.

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5

Howard, Thomas J., and Mogens Myrup Andreasen. "Mind-sets of functional reasoning in engineering design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27, no. 3 (July 24, 2013): 233–40. http://dx.doi.org/10.1017/s0890060413000231.

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AbstractThe concept of a function is of great importance in design. This paper describes from theory how designers should reason about functions when designing. This paper introduces the link model, showing how functions and properties link the product and its use, to the perceived value of the product. The important and useful distinction between functions and properties is made along with the distinction between “wirk functions,” which is what the product does when operating, and “use functions,” which is what the product is used for. The paper makes a novel contribution beyond previous literature, showing that not only is a product's behavior or mode of action designed but also the use activity of the end user. Based on the theoretical perspective unfolded, the authors offer nine mind-sets for both design practitioners and researchers to consider when reasoning about functions.

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Nakatani, Yoshio, Makoto Tsukiyama, and Toyoo Fukuda. "Engineering design support framework by case-based reasoning." ISA Transactions 31, no. 2 (January 1992): 165–80. http://dx.doi.org/10.1016/0019-0578(92)90037-j.

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7

Sham, S. H. R. "Nonmonotonic Reasoning in Design." Journal of Computing in Civil Engineering 7, no. 1 (January 1993): 36–53. http://dx.doi.org/10.1061/(asce)0887-3801(1993)7:1(36).

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8

Shiva Kumar, H., and C. S. Krishnamoorthy. "A framework for case-based reasoning in engineering design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 9, no. 3 (June 1995): 161–82. http://dx.doi.org/10.1017/s0890060400002419.

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AbstractAlthough the case-based reasoning (CBR) process is domain dependent, certain aspects of it can readily be captured into a generic framework which in turn can be applied to various engineering domains. One such exercise that has been carried out is described here. In this paper, we present the notion that CBR can be formalized and applied in a specialized framework in an integrated knowledge-based environment. We first analyze the CBR process to abstract the steps involved in the development of a CBR system. We then propose a framework in which most of these steps are formalized so that they can be applied in a domain-independent manner. The salient features of this framework, called CASETOOL (CASE-based reasoning TOOL-kit), are then described. The highlight of this approach is the use of a concept called design criticism in the CBR process. The versatility of the tool is demonstrated through an application from the bridge engineering domain.

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9

Purvis, Lisa, and Pearl Pu. "COMPOSER: A case-based reasoning system for engineering design." Robotica 16, no. 3 (May 1998): 285–95. http://dx.doi.org/10.1017/s0263574798000368.

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The frequent use of past experience by human engineers when solving new problems has led to an interest in the use of case based reasoning (CBR) to help automate engineering design. In engineering design it often occurs that many past exp eriences must be combined to solve a new problem, and thus the process of case based adaptation must efficiently and systematically combine information from many sources. We have developed a constraint based methodology for case combination that allows its application across a wide range of problems. We have shown that our approach provides an efficient adaptation methodology that ensures convergence upon a solution if one exists, provides a uniform representation of cases, and is generalizable beyond just one domain. Our technique is implemented in a case based reasoning system called COMPOSER, which ha s been tested in two design domains: assembly sequence design and configuration design.

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10

QIN, XIAOLI, and WILLIAM C. REGLI. "A study in applying case-based reasoning to engineering design: Mechanical bearing design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17, no. 3 (August 2003): 235–52. http://dx.doi.org/10.1017/s0890060403173064.

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Case-based reasoning (CBR) is a promising methodology for solving many complex engineering design problems. CBR employs past problem-solving experiences when solving new problems. This paper presents a case study of how to apply CBR to a specific engineering problem: mechanical bearing design. A system is developed that retrieves previous design cases from a case repository and uses adaptation techniques to modify them to satisfy the current problem requirements. The approach combines both parametric and constraint satisfaction adaptations. Parametric adaptation considers not only parameter substitution but also the interrelationships between the problem definition and its solution. Constraint satisfaction provides a method to globally check the design requirements to assess case adaptability. Currently, our system has been implemented and tested in the domain of rolling bearings. This work serves as a template for application of CBR techniques to realistic engineering problems.

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AL-SHIHABI, TALAL, and IBRAHIM ZEID. "A design-plan-oriented methodology for applying case-based adaptation to engineering design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12, no. 5 (November 1998): 463–78. http://dx.doi.org/10.1017/s0890060498125052.

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Adaptation of design cases is usually the most challenging part in building any case-based reasoning design system. The success of the adaptation process in finding a solution for a new design problem determines the success of the entire case-based reasoning (CBR) system. The techniques used for generating design solutions have many common aspects among the various engineering design classes that make them amenable to be captured in a generic framework for an acceptable level of abstraction. This paper proposes a design-plan-oriented methodology for adapting design cases to produce a solution to a new design problem in the domain of engineering design. The proposed methodology uses multicase adaptation and case built-in adaptation knowledge to produce a design plan for a new design problem. We first define the model of case representation to work with the proposed methodology. We then define the overall structure of the procedural framework of this methodology and its subprocesses. The system is then demonstrated through an application from the structural engineering domain.

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12

Kannapan, Srikanth M., and Kurt M. Marshek. "Design synthetic reasoning: A methodology for mechanical design." Research in Engineering Design 2, no. 4 (December 1991): 221–38. http://dx.doi.org/10.1007/bf01579219.

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13

Veerkamp, Paul, and Paul Ten Hagen. "Qualitative reasoning about design objects." Robotics and Computer-Integrated Manufacturing 10, no. 1-2 (January 1993): 33–39. http://dx.doi.org/10.1016/0736-5845(93)90023-d.

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14

Sivard, Gunilla, Lars Lindberg, and Erik Agerman. "Customer-Based Design with Constraint Reasoning." CIRP Annals - Manufacturing Technology 42, no. 1 (January 1993): 139–42. http://dx.doi.org/10.1016/s0007-8506(07)62411-1.

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15

Moore, Tamara J., Emilie A. Siverling, and Kerrie A. Douglas. "Evidence‐based reasoning: Eliciting STEM content during engineering design justifications." School Science and Mathematics 119, no. 8 (December 2019): 1–3. http://dx.doi.org/10.1111/ssm.12378.

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16

Letier, Emmanuel, and Axel van Lamsweerde. "Reasoning about partial goal satisfaction for requirements and design engineering." ACM SIGSOFT Software Engineering Notes 29, no. 6 (November 2004): 53–62. http://dx.doi.org/10.1145/1041685.1029905.

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17

Welch, Richard V., and John R. Dixon. "Guiding conceptual design through behavioral reasoning." Research in Engineering Design 6, no. 3 (September 1994): 169–88. http://dx.doi.org/10.1007/bf01607277.

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18

Demoly, Frédéric, Kyoung-Yun Kim, and Imre Horváth. "Ontological engineering for supporting semantic reasoning in design: deriving models based on ontologies for supporting engineering design." Journal of Engineering Design 30, no. 10-12 (November 5, 2019): 405–16. http://dx.doi.org/10.1080/09544828.2019.1633626.

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19

Grabska, E., and G. Ślusarczyk. "Knowledge and reasoning in design systems." Automation in Construction 20, no. 7 (November 2011): 927–34. http://dx.doi.org/10.1016/j.autcon.2011.03.009.

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20

Subramanyam, S., and S. C. Y. Lu. "Computer-Aided Simultaneous Engineering for Components Manufactured in Small and Medium Lot-Sizes." Journal of Engineering for Industry 113, no. 4 (November 1, 1991): 450–64. http://dx.doi.org/10.1115/1.2899722.

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This paper presents a methodology for the simultaneous product and process design of components manufactured in small and medium lot-sizes. A key aspect of this methodology is to ensure that such components are manufacturable for the lowest possible cost in specially designed manufacturing facilities such as manufacturing cells. A computer-based design environment is proposed to cooperatively assist product designers in performing this task. The design environment is based on the model-based reasoning concept. Explicit product and manufacturing facility models are presented to support the reasoning process. These models are based on a combination of feature-based, geometric and process performance models. The reasoning mechanism is based on the Multiple Cooperative Knowledge Sources (MCKS) paradigm with explicit separation of domain and control knowledge. Domain knowledge sources deal with the product and process refinement activities while control knowledge sources deal with managing the concurrency between these two activities. The framework of the design environment is described in detail and justifications are provided for the modeling and reasoning mechanisms chosen. Preliminary results obtained from an implementation of the design environment for the simultaneous product and process design of Bearing Cages are presented. The paper concludes by summarizing the main contributions of this research and presents directions for future research work.

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21

Yang, Yan Hua, Zu Ping Zhu, and Li Gang Yao. "Design Modeling and Solving Implementation for Product Conceptual Design in Quality Function Deployment." Advanced Materials Research 279 (July 2011): 394–99. http://dx.doi.org/10.4028/www.scientific.net/amr.279.394.

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Considering the process of research in conceptual design, a framework model of Function-Behaviour-structure was put forward, and QFD was an effective tool for function conversion. Then a mixed solving technology combining case-based reasoning and behaviour solving reasoning was discussed to achieve design mapping. Finally, an automatic synthesis mapping system was developed and the conceptual design of clam collector was given as an example. Application results demonstrated that the proposed method was feasible.

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22

Pan, Zhi Yi, X. Y. Cao, Y. Wu, and Shun De Gao. "A Product Design Method Based on Integrated Geometric and Rule-Based Reasoning." Materials Science Forum 628-629 (August 2009): 7–12. http://dx.doi.org/10.4028/www.scientific.net/msf.628-629.7.

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Though rule-based reasoning system is easy to create and convenient to extend, it is not flexible because of the demand for precise matching. Geometric reasoning system can obtain engineering semantic from CAD model and connect geometric topology with design knowledge, but it can not drive product design directly. Utilizing the complementary of rule-based reasoning and geometric reasoning, a product design method based on integrated geometric and rule-based reasoning is presented. The method can standardize the expression of design scheme which is from geometric reasoning, and dock seamlessly design scheme and input condition in rule-based reasoning system. Furthermore, the result of rule-based reasoning can drive product design directly. The operation of human-computer interaction and designer’s experience needed are decreased greatly. Digital target in locater is significant to harmonize aircraft tie-in. there are different types in it, and the amount of it is huge, so its design process is complicated and fussy. On the basis of the method, a rapid design software package for locator digital target design is developed and has been applied in aircraft assembly tooling design. The application has proved the method can improve design efficiency, standardization and intelligence.

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23

M. H. Gedig and S. F. Stiemer. "Computer Application to Study Engineering Projects at the Early Stages of Development." Electronic Journal of Structural Engineering 3 (January 1, 2003): 43–66. http://dx.doi.org/10.56748/ejse.330.

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This paper describes a computer software application, the Qualitative Engineering System (QES), which the engineer can use to perform qualitative and semi-quantitative analysis of preliminary engineering designs. In engineering practice, many situations arise in which the engineer wishes to perform a logical, objective comparison between conceptual or preliminary design options. Although there exist many applications which can be used to perform detailed numerical analysis to justify detailed final designs, relatively fewuseful programs are available to validate designs at the preliminary stages. The early stages of design are characterized by higher levels of uncertainty than the latter stages. Established qualitative and semiquantitative reasoning techniques may be used to detail with uncertainty and incomplete information in a sound, logical manner. The QES application utilizes a unified framework, which is used to implement a number of qualitative and semi-quantitative reasoning techniques. This paper gives an explanation of qualitative and semi-quantitative analysis in the context of the QES application. In addition, the paper gives some practical examples of how the QES program can be used in the engineering environment

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24

VAN LANGEN, PIETER H. G., and FRANCES M. T. BRAZIER. "Design space exploration revisited." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 20, no. 2 (March 10, 2006): 113–19. http://dx.doi.org/10.1017/s0890060406060100.

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Design involves reasoning about descriptions of design artifacts, reasoning about design requirements, and reasoning about design process objectives (such as keeping to deadlines and available budget). Reasoning about these three aspects occurs during exploration, generation, and evaluation of partial design descriptions. Design space exploration involves exploration in all three related spaces: the space of partial descriptions of design artifacts, the space of design requirements, and the space of design process objectives. These spaces are vast. Explicit representation of the relations between elements in these three spaces provides the additional information needed to understand and reuse descriptions of partial design process traces, and to guide design exploration. In their Keynote Article, Woodbury and Burrow describe one of these spaces, namely, the space of design object descriptions, as a network of partial and intentional descriptions of design artifacts. The links between partial descriptions represent paths in design processes. Making the information compiled in these paths of exploration explicit, as proposed in this paper, extends the approach described by Woodbury and Burrow, increasing options for accessibility.

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25

Gorman, Michael E. "Implicit knowledge in engineering judgment and scientific reasoning." Behavioral and Brain Sciences 22, no. 5 (October 1999): 767–68. http://dx.doi.org/10.1017/s0140525x9936218x.

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Dienes & Perner's theoretical framework should be applicable to two related areas: technological innovation and the psychology of scientific reasoning. For the former, this commentary focuses on the example of nuclear weapon design, and on the decision to launch the space shuttle Challenger. For the latter, this commentary focuses on Klayman and Ha's positive test heuristic and the invention of the telephone.

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Lin, Feng‐Tyan, and Hung‐Hsiang Wang. "An abductive propositional logic for design reasoning." Journal of the Chinese Institute of Engineers 24, no. 5 (July 2001): 569–79. http://dx.doi.org/10.1080/02533839.2001.9670653.

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27

Shpitalni, M., and H. Lipson. "Automatic Reasoning for Design under Geometrical Constraints." CIRP Annals 46, no. 1 (1997): 85–88. http://dx.doi.org/10.1016/s0007-8506(07)60781-1.

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28

Chinn, S. J., and G. R. Madey. "Temporal representation and reasoning for workflow in engineering design change review." IEEE Transactions on Engineering Management 47, no. 4 (2000): 485–92. http://dx.doi.org/10.1109/17.895343.

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29

Mendes, José Ricardo P., Celso K. Morooka, and Ivan R. Guilherme. "Case-based reasoning in offshore well design." Journal of Petroleum Science and Engineering 40, no. 1-2 (October 2003): 47–60. http://dx.doi.org/10.1016/s0920-4105(03)00083-4.

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30

Martin, R. R., H. Suzuki, and P. A. C. Varley. "Labeling Engineering Line Drawings Using Depth Reasoning." Journal of Computing and Information Science in Engineering 5, no. 2 (February 21, 2005): 158–67. http://dx.doi.org/10.1115/1.1891045.

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Automatic creation of B-rep models of engineering objects from freehand sketches would benefit designers. One step aims to take a line drawing (with hidden lines removed), and from it deduce an initial three-dimensional (3D) geometric realization of the visible part of the object, including junction and line labels, and depth coordinates. Most methods for producing this frontal geometry use line labeling, which takes little or no account of geometry. Thus, the line labels produced can be unreliable. Our alternative approach inflates a drawing to produce provisional depth coordinates, and from these makes deductions about line labels. Assuming many edges in the drawing are parallel to one of three main orthogonal directions, we first attempt to identify groups of parallel lines aligned with the three major axes of the object. From these, we create and solve a linear system of equations relating vertex coordinates, in the coordinate system of the major axes. We then inflate the drawing in a coordinate system based on the plane of the drawing and depth perpendicular to it. Finally, we use this geometry to identify which lines in the drawing correspond to convex, concave, or occluding edges. We discuss alternative realizations of some of the concepts, how to cope with nonisometric-projection drawings, and how to combine this approach with other labeling techniques to gain the benefits of each. We test our approach using sample drawings chosen to be representative of engineering objects. These highlight difficulties often overlooked in previous papers on line labeling. Our new approach has significant benefits.

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31

Zhao, F., and M. L. Maher. "Using analogical reasoning to design buildings." Engineering with Computers 4, no. 3 (September 1988): 107–19. http://dx.doi.org/10.1007/bf01199293.

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32

Woelfel, Christian, Jens Krzywinski, and Frank Drechsel. "Knowing, reasoning and visualizing in industrial design." Knowledge Engineering Review 28, no. 3 (July 26, 2013): 287–302. http://dx.doi.org/10.1017/s0269888913000258.

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AbstractIndustrial design processes can be described as human design problem solving, incorporating the acquisition, evaluation, production and transfer of specific knowledge. In this paper, we will describe the connection and interaction between visualization and reasoning during different stages of the design process. Thereby we focus on three early stages of this process: clarifying the task, concepting and designing an overall solution.This paper provides a rather general description of design processes and more detailed remarks on design knowledge and design actions. It specifically focuses on design concepts as visual key elements in industrial design processes. We will address the importance of externalization and visualization as means for thinking and knowledge generation and transfer in industrial design in general.The design process is described as an interplay of the parallel and iterative developments of three domains: knowledge, concept and design. In contrast to linear schemes, this paper proposes a design process scheme focusing on iterative circles and parallel processing possibilities. Industrial design knowledge will be described and compared to relevant knowledge in other disciplines, in particular, engineering design knowledge. We will describe the strong link between the designer's individual biographies, design knowledge and the outcome of design processes.Design concepts will be discussed as extremely compact representations of core characteristics of the artifacts to be designed, serving as a guide to the design process.Design actions as described in this paper are characterized by the simultaneous occurrence of thought and externalization processes. Different kinds of visualization are discussed in regard of their role in reasoning during industrial design processes.This paper concludes by sketching two perspectives. One addresses the need for interdisciplinary research on new visualization tools with regard to human reasoning in design processes. The second one gives an impression of how visualization tools and methods of industrial design can supplement other disciplines.

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D'Amelio, Valentina, Magdalena K. Chmarra, and Tetsuo Tomiyama. "A method to reduce ambiguities of qualitative reasoning for conceptual design applications." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27, no. 1 (January 15, 2013): 19–35. http://dx.doi.org/10.1017/s0890060412000364.

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AbstractQualitative reasoning can generate ambiguous behaviors due to the lack of quantitative information. Despite many different research results focusing on ambiguities reduction, fundamentally it is impossible to totally remove ambiguities with only qualitative methods and to guarantee the consistency of results. This prevents the wide use of qualitative reasoning techniques in practical situations, particularly in conceptual design, where qualitative reasoning is considered intrinsically useful. To improve this situation, this paper initially investigates the origin of ambiguities in qualitative reasoning. Then it proposes a method based on intelligent interventions of the user who is able to detect ambiguities, to prioritize interventions on these ambiguities, and to reduce ambiguities based on the least commitment strategy. This interaction method breaks through the limit of qualitative reasoning in practical applications to conceptual design. The method was implemented as a new feature in a software tool called the Knowledge Intensive Engineering Framework in order to be tested and used for a printer design.

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Wang, Bin, Jian Jun Chen, and Jie Tao. "Overview of Intelligent Design on Mold." Advanced Materials Research 459 (January 2012): 394–97. http://dx.doi.org/10.4028/www.scientific.net/amr.459.394.

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Applying Artificial Intelligence technology on mold design can help realize the design automation of mold. Because knowledge-based engineering is an effective intelligent design method, the paper systematically introduced the application and development of knowledge representation, knowledge reasoning, knowledge acquisition and other key technologies of knowledge engineering technology used in mold design field. Finally, the development tendency of mold design based on artificial intelligence was analyzed in detail

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Zhao, Yan Wei, F. Zhang, M. Y. Zhang, Jian Chen, and N. Su. "Extension Case-Based Reasoning for Product Configuration Design." Advanced Materials Research 69-70 (May 2009): 616–20. http://dx.doi.org/10.4028/www.scientific.net/amr.69-70.616.

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The interface was regarded as standard and not considered in traditional configuration design, which made it difficult to apply to the existence product configuration. The paper proposes an extension case-based reasoning for product configuration design. With matter-elements, reasoning model of Extension Case-Based Reasoning (ECBR) is established, and its corresponding algorithm is proposed. During the configuration design, the solution space of configuration schemes is obtained by the similarity calculation, and then the overall evaluation of similarity and compatible degrees is adopted to form the final configuration scheme. A prototype system of reducer configuration design is successfully developed according to the method, and it proves the proposed method that is feasible and effective.

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Liu, Kaidi, Yancang Li, and Rong Cui. "Design of a Computable Approximate Reasoning Logic System for AI." Mathematics 10, no. 9 (April 25, 2022): 1447. http://dx.doi.org/10.3390/math10091447.

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The fuzzy logic reasoning based on the “If... then...” rule is not the inaccurate reasoning of AI against ambiguity because fuzzy reasoning is antilogical. In order to solve this problem, a redundancy theory for discriminative weight filtering containing six theorems and one M(1,2,3) model was proposed and the approximate reasoning process was shown, the system logic of AI handling ambiguity as an extension of the classical logic system was proposed. The system is a generalized dynamic logic system characterized by machine learning, which is the practical-application logic system of AI, and can effectively deal with practical problems including conflict, noise, emergencies and various unknown uncertainties. It is characterized by combining approximate reasoning and computing for specific data conversion through machine learning. Its core is data and calculations and the condition is “sufficient” high-quality training data. The innovation is that we proposed a discriminative weight filtering redundancy theory and designed a computable approximate reasoning logic system that combines approximate reasoning and calculation through machine learning to convert specific data. It is a general logic system for AI to deal with uncertainty. The study has significance in theory and practice for AI and logical reasoning research.

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Liu, Kaidi, Yancang Li, and Rong Cui. "Design of a Computable Approximate Reasoning Logic System for AI." Mathematics 10, no. 9 (April 25, 2022): 1447. http://dx.doi.org/10.3390/math10091447.

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The fuzzy logic reasoning based on the “If... then...” rule is not the inaccurate reasoning of AI against ambiguity because fuzzy reasoning is antilogical. In order to solve this problem, a redundancy theory for discriminative weight filtering containing six theorems and one M(1,2,3) model was proposed and the approximate reasoning process was shown, the system logic of AI handling ambiguity as an extension of the classical logic system was proposed. The system is a generalized dynamic logic system characterized by machine learning, which is the practical-application logic system of AI, and can effectively deal with practical problems including conflict, noise, emergencies and various unknown uncertainties. It is characterized by combining approximate reasoning and computing for specific data conversion through machine learning. Its core is data and calculations and the condition is “sufficient” high-quality training data. The innovation is that we proposed a discriminative weight filtering redundancy theory and designed a computable approximate reasoning logic system that combines approximate reasoning and calculation through machine learning to convert specific data. It is a general logic system for AI to deal with uncertainty. The study has significance in theory and practice for AI and logical reasoning research.

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38

Jared, G. E. M., M. G. Limage, I. J. Sherrin, and K. G. Swift. "Geometric reasoning and design for manufacture." Computer-Aided Design 26, no. 7 (July 1994): 528–36. http://dx.doi.org/10.1016/0010-4485(94)90084-1.

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39

Paugh, Patricia, and Kristen Wendell. "Disciplinary Literacy in STEM: A Functional Approach." Journal of Literacy Research 53, no. 1 (January 21, 2021): 122–44. http://dx.doi.org/10.1177/1086296x20986905.

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This study explores disciplinary literacy instruction integrated within an elementary engineering unit in an urban classroom. A multidisciplinary team of university literacy and engineering educators and classroom teachers served as the research team for this case study. A social semiotic language theory (systemic functional linguistics) and a framework of mechanistic reasoning informed the instruction and analysis of classroom discourse and student writing. The study illustrates how a flexible set of disciplinary language choices functioned to support students’ evolving reasoning as part of the engineering design process. These findings provide insights into synergy between language and reasoning as a habit of design. These findings also inform calls to align science, technology, engineering, and mathematics (STEM) literacy and core disciplinary practices within both Common Core State Standards for (English language arts) ELA and Next Generation Science Standards.

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40

Watson, Ian, and Srinath Perera. "Case-based design: A review and analysis of building design applications." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11, no. 1 (January 1997): 59–87. http://dx.doi.org/10.1017/s0890060400001840.

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AbstractThis paper presents a review of CBD and its application to building design in particular. Case-based design is the application of case-based reasoning to the design process. Design maps well to case-based reasoning because designers use parts of previous design solutions in developing new design solutions. This paper identifies problems of case representation, retrieval, adaptation, presentation, and case-based maintenance along with creativity, legal, and ethical issues that need to be addressed by CBD systems. It provides a comprehensive review of CBD systems developed for building design and provides a detailed comparison of the CBD systems reviewed.

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Asghar, Anila, Ying-Syuan Huang, Kenneth Elliott, and Yannick Skelling. "Exploring Secondary Students’ Alternative Conceptions about Engineering Design Technology." Education Sciences 9, no. 1 (February 24, 2019): 45. http://dx.doi.org/10.3390/educsci9010045.

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This paper presents the assessment items that were developed by science and technology teachers in Québec to explore their students’ alternative ideas about engineering design technology and technological systems. These assessment items were administered to Secondary Cycle One students in Francophone and Anglophone schools in Québec to elicit their ideas about the foundational technology concepts included in the science and technology curriculum. Students’ responses are presented to share their alternative and scientific explanations. In addition, various approaches to facilitate a deeper understanding of scientific models and mechanistic reasoning in students are also discussed.

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Bardasz, Theodore, and Ibrahim Zeid. "DEJAVU: Case-based reasoning for mechanical design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 7, no. 2 (May 1993): 111–24. http://dx.doi.org/10.1017/s0890060400000809.

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The architecture and implementation of a mechanical designer's assistant shell called DEJAVU is presented. The architecture is based on an integration of design and CAD with some of the more well known concepts in case-based reasoning (CBR). DEJAVU provides a flexible and cognitively intuitive approach for acquiring and utilizing design knowledge. It is a domain independent mechanical design shell that can incrementally acquire design knowledge in the domain of the user. DEJAVU provides a design environment that can learn from the designer(s) until it can begin to perform design tasks autonomously or semi-autonomously. The main components of DEJAVU are a knowledge base of design plans, an evaluation module in the form of a design plan system, and a blackboard-based adaptation module. The existance of these components are derived from the utilization of a CBR architecture. DEJAVU is the first step in developing a robust designer's assistant shell for mechanical design problems. One of the major contributions of DEJAVU is the development of a clean architecture for the utilization of case-based reasoning in a mechanical designer's assistant shell. In addition, the components of the architecture have been developed, tailored or modified from a general CBR context into a more synergistic relationship with mechanical design.

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Kumara, Soundar, Inyong Ham, M. Al-Hamando, and Ken Goodnow. "Causal Reasoning and Data Abstraction in Component Design." CIRP Annals 38, no. 1 (1989): 145–48. http://dx.doi.org/10.1016/s0007-8506(07)62671-7.

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Browne, Fiona, Niall Rooney, Weiru Liu, David Bell, Hui Wang, Philip S. Taylor, and Yan Jin. "Integrating textual analysis and evidential reasoning for decision making in Engineering design." Knowledge-Based Systems 52 (November 2013): 165–75. http://dx.doi.org/10.1016/j.knosys.2013.07.014.

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Gonçalves Motta, José Antonio, and Simone Diniz Junqueira Barbosa. "Investigating the Case-Based Reasoning Process During HCI Design." Journal on Interactive Systems 6, no. 1 (October 9, 2015): 1. http://dx.doi.org/10.5753/jis.2015.653.

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Given that the design activity makes use of previous design knowledge, we turned to case-based reasoning (CBR) to help identify opportunities to support the design of human-computer interaction (HCI). Using interviews with professional designers and Semiotic Engineering, we developed a CBR tool called CHIDeK (Computer-Human Interaction Design Knowledge), with which we conducted a study to observe how it influenced the HCI design activity. We found that the cases recorded in CHIDeK supported design by motivating the designers’ reflective process, triggering their memories of experiences with similar systems, and helping to generate new ideas. We have also identified limitations in our case representation and case access methods, which offer opportunities for further research.

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CHOU, YON-CHUN. "Geometric reasoning for layout design of machining fixtures." International Journal of Computer Integrated Manufacturing 7, no. 3 (May 1994): 175–85. http://dx.doi.org/10.1080/09511929408944607.

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Wang, Haiqiao, and Ruikun Niu. "Knowledge Service Technology for Supporting Intelligent Product Design." Shock and Vibration 2021 (November 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/2561950.

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In this paper, a knowledge service method that supports the intelligent design of products is investigated. The proposed method provides the solutions to computational problems and reasoning and decision-making problems in the field of intelligent design. The requirement analysis of a knowledge-based intelligent design system integrates design knowledge into case-based reasoning activities through scheme analysis, scheme evaluation, and scheme adjustment, thus achieving knowledge-based intelligent reasoning and decision-making. During the similarity matching, a new hybrid similarity measurement method is proposed to calculate the similarity of crisp and fuzzy sets. This method integrates the fuzzy set similarity theory based on the traditional similarity measurement method. A method of attribute level classification is proposed to assign weight coefficients. The attributes are divided into the primary matching and auxiliary matching levels according to the decisiveness of case matching, and the set of weight coefficients is continuously and dynamically updated through case-based reasoning learning. Then, the weighted global similarity measure is used to obtain the set of similar cases from the case database. Finally, a design example of a computer numerical control tool holder product is studied to present the practicability and effectiveness of the proposed method.

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Zhou, Jian Zhao, Xiao Qiang Yang, Guo Liang Lu, and Kai Ming Ma. "The Design of Hydraulic Decontaminating Equipment for Engineering Machinery." Applied Mechanics and Materials 66-68 (July 2011): 2017–21. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.2017.

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A type of hydraulic pollutant decontaminating equipment was made in this paper. The decontamination and maintenance means were studied. The circulating decontaminating method was put forward and developed by modularized technique. The hardware of the decontaminating equipment was implemented with single-chip microcomputer and integrated circuit technology. The controlling application program integrated with fuzzy logic and reasoning function was developed. The practical use showed that the decontaminating equipment could be used in the maintenance of hydraulic circuit of bulldozer as well as the other engineering machinery, therefore realizing the quick decontamination and cleaning of hydraulic component and oil on-line purifying.

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Kang, Chung-Chia, and Wen-June Wang. "Fuzzy reasoning-based directional median filter design." Signal Processing 89, no. 3 (March 2009): 344–51. http://dx.doi.org/10.1016/j.sigpro.2008.09.003.

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Wang, Juan, Si Yu Lai, and Yi Liu. "Applying Computer Aided Design in Composite Molding." Advanced Materials Research 322 (August 2011): 235–38. http://dx.doi.org/10.4028/www.scientific.net/amr.322.235.

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Introduce the computer-assisted molding process system in composite components knowledge base. The feature reasoning based method is applied to the composite molding process retrieval in the light of the issues of large number of parameters, hard management and the little probability of experience sharing. Besides the production rules based reasoning approach is proposed to decide the composite materials molding technology. The overall design structure, including database of materials management, molding instance process, molding parameters management and secondary molding process selection are demonstrated. We take the object and component oriented technology to realize the design.

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