A process knowledge representation approach for decision support in design of complex engineered systems

2021 ◽  
Vol 48 ◽  
pp. 101257
Author(s):  
Ru Wang ◽  
Anand Balu Nellippallil ◽  
Guoxin Wang ◽  
Yan Yan ◽  
Janet K. Allen ◽  
...  
Keyword(s):  
Decision Support ◽  
Knowledge Representation ◽  
Process Knowledge ◽  
Complex Engineered Systems ◽  
Engineered Systems

Knowledge-Based Design Guidance System for Cloud-Based Decision Support in the Design of Complex Engineered Systems

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Ru Wang ◽  
Jelena Milisavljevic-Syed ◽  
Lin Guo ◽  
Yu Huang ◽  
Guoxin Wang
Keyword(s):  
Decision Support ◽  
Value Chain ◽  
Industry 4.0 ◽  
Guidance System ◽  
Design Engineering ◽  
Knowledge Based ◽  
Complex Engineered Systems ◽  
Engineered Systems ◽  
Knowledge Based Design ◽  
Design Guidance

Abstract The automation and intelligence highlighted in Industry 4.0 put forward higher requirements for reasonable trade-offs between humans and machines for decision-making governance. However, in the context of Industry 4.0, the vision of decision support for design engineering is still unclear. Additionally, the corresponding methods and system architectures are lacking to support the realization of value-chain-centric complex engineered systems design lifecycles. Hence, we identify decision support demands for complex engineered systems designs in the Industry 4.0 era, representing the integrated design problems at various stages of the product value chain. As a response, in this paper, the architecture of a Knowledge-Based Design Guidance System (KBDGS) for cloud-based decision support (CBDS) is presented that highlights the integrated management of complexity, uncertainty, and knowledge in designing decision workflows, as well as systematic design guidance to find satisfying solutions with the iterative process “formulation-refinement-exploration-improvement” (FREI). The KBDGS facilitates diverse multi-stakeholder collaborative decisions in end-to-end cloud services. Finally, two design case studies are conducted to illustrate the proposed work and the efficacy of the developed KBDGS. The contribution of this paper is to provide design guidance to facilitate knowledge discovery, capturing, and reuse in the context of decision-centric digital design, thus improving the efficiency and effectiveness of decision-making, as well as the evolution of decision support in the field of design engineering for the age of Industry 4.0 innovation paradigm.

Rave: A Computational Framework to Facilitate Research in Design Decision Support

2012 ◽  
Vol 12 (2) ◽  
Author(s):  
Matthew J. Daskilewicz ◽  
Brian J. German
Keyword(s):  
Decision Support ◽  
Wide Spectrum ◽  
Problem Formulation ◽  
Design Decision ◽  
Computational Framework ◽  
Complex Engineered Systems ◽  
Research Platform ◽  
Engineered Systems ◽  
Support Methods ◽  
Design Decision Support

The cognitive challenges in the design of complex engineered systems include the scale and scope of decision problems, nonlinearity of the trade space, subjectivity of the problem formulation, and the need for rapid decision making. These challenges have motivated an active area of research in design decision-support methods and the development of commercial and openly available design frameworks. Although these frameworks are extremely capable, most are limiting as a basis for research relating to design decision support because they offer little user flexibility for incorporating and evaluating new features or techniques. This paper describes Rave (www.rave.gatech.edu), a computational framework designed specifically as a research platform for design decision-support methods. Rave has been structured to be flexible and adaptable, handle data with systematic data structures and descriptive metadata, facilitate a wide spectrum of visualization types, provide features to enable user interactivity and linking of graphics, and incorporate surrogate modeling and optimization as enabling capabilities. This framework is envisioned to provide the research and industrial communities an easily expandable and customizable baseline capability to facilitate investigation of further design decision-support advancements.

Ontology-Based Representation of Design Decision Hierarchies

2016 ◽  
Author(s):  
Zhenjun Ming ◽  
Yan Yan ◽  
Guoxin Wang ◽  
Jitesh H. Panchal ◽  
Chung Hyun Goh ◽  
...  
Keyword(s):  
Decision Support ◽  
Engineering Design ◽  
Building Blocks ◽  
Design Decision ◽  
Information Flows ◽  
Knowledge Modeling ◽  
Frame Design ◽  
Complex Engineered Systems ◽  
Portal Frame ◽  
Engineered Systems

It is efficacious to capture and represent the knowledge for decision support in engineering design. Ontology is a promising knowledge modeling scheme in the engineering domain. In this paper, an ontology is proposed for capturing, representing and documenting the knowledge related to hierarchical decisions in the design of complex engineered systems. The ontology is developed based on the coupled Decision Support Problem (DSP) construct, taking into consideration the requirements for a computational model that represents a decision hierarchy. Key to the ontology is the concept of two classes, namely, Process which represents the basic hierarchy building blocks where the DSPs are embedded, and Interface which represents the DSP information flows that link different Processes to a hierarchy. The efficacy of the ontology is demonstrated using a portal frame design example.

Ontological Knowledge Representation and Inference Rules for MTM Decision Support System

2014 ◽  
Author(s):  
Rahul Renu ◽  
Gregory Mocko
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Representation ◽  
Support System ◽  
Time Estimation ◽  
Time Measurement ◽  
Inference Rules ◽  
Assembly Time ◽  
Time Estimates ◽  
Definition Of

The objective of the research presented is to develop and implement an ontological knowledge representation for Methods-Time Measurement assembly time estimation process. The knowledge representation is used to drive a decision support system that provides the user with intelligent MTM table suggestions based on assembly work instructions. Inference rules are used to map work instructions to MTM tables. An explicit definition of the assembly time estimation domain is required. The contribution of this research, in addition to the decision support system, is an extensible knowledge representation that models work instructions, MTM tables and mapping rules between the two which will enable the establishment of assembly time estimates. Further, the ontology provides an extensible knowledge representation framework for linking time studies and assembly processes.

scholarly journals Forensic Engineering Use Of Fault Tree Analysis

2006 ◽  
Vol 23 (2) ◽  
Author(s):  
Frank H. Johnson ◽  
DeWitt William E.
Keyword(s):  
Fault Tree ◽  
Fault Tree Analysis ◽  
Tree Analysis ◽  
Complex Engineered Systems ◽  
Track Record ◽  
Forensic Engineering ◽  
Fire And Explosion ◽  
Engineered Systems ◽  
Analytical Tools

Analytical Tools, Like Fault Tree Analysis, Have A Proven Track Record In The Aviation And Nuclear Industries. A Positive Tree Is Used To Insure That A Complex Engineered System Operates Correctly. A Negative Tree (Or Fault Tree) Is Used To Investigate Failures Of Complex Engineered Systems. Boeings Use Of Fault Tree Analysis To Investigate The Apollo Launch Pad Fire In 1967 Brought National Attention To The Technique. The 2002 Edition Of Nfpa 921, Guide For Fire And Explosion Investigations, Contains A New Chapter Entitled Failure Analysis And Analytical Tools. That Chapter Addresses Fault Tree Analysis With Respect To Fire And Explosion Investigation. This Paper Will Review The Fundamentals Of Fault Tree Analysis, List Recent Peer Reviewed Papers About The Forensic Engineering Use Of Fault Tree Analysis, Present A Relevant Forensic Engineering Case Study, And Conclude With The Results Of A Recent University Study On The Subject.

scholarly journals Guideline Formalization and Knowledge Representation for Clinical Decision Support

2013 ◽  
Vol 1 (2) ◽  
pp. 1-11 ◽  
Author(s):  
Tiago Oliveira ◽  
José Neves ◽  
Paulo Novais
Keyword(s):  
Decision Support ◽  
Knowledge Representation ◽  
Clinical Decision Support ◽  
Decision Model ◽  
Clinical Decision ◽  
Medical Community ◽  
Clinical Process ◽  
Generic Tasks ◽  
Medical Recommendations ◽  
Representation Formalism

The prevalence of situations of medical error and defensive medicine in healthcare institutions is a great concern of the medical community. Clinical Practice Guidelines are regarded by most researchers as a way to mitigate theseoccurrences; however, there is a need to make them interactive, easier to update and to deploy. This paper provides a model for Computer-Interpretable Guidelines based on the generic tasks of the clinical process, devised to be included in the framework of a Clinical Decision Support System. Aiming to represent medical recommendations in a simple and intuitive way. Hence, this work proposes a knowledge representation formalism that uses an Extension to Logic Programming to handle incomplete information. This model is used to represent different cases of missing, conflicting and inexact information with the aid of a method to quantify its quality. The integration of the guideline model with the knowledge representation formalism yields a clinical decision model that relies on the development of multiple information scenarios and the exploration of different clinical hypotheses.

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