A knowledge-based system for the conceptual design of grippers for handling fabrics

1999 ◽  
Vol 13 (1) ◽  
pp. 13-25 ◽  
Author(s):  
V.C. MOULIANITIS ◽  
A.J. DENTSORAS ◽  
N.A. ASPRAGATHOS
Keyword(s):  
Conceptual Design ◽  
Domain Knowledge ◽  
Heuristic Method ◽  
Search Method ◽  
Knowledge Based System ◽  
Design Concepts ◽  
Knowledge Based ◽  
Reasoning Strategy ◽  
Heuristic Search Method ◽  
Feasible Solutions

The paper presents a knowledge-based system (KBS) for the conceptual design of grippers for handling fabrics. Its main purpose is the integration of the domain knowledge in a single system for the systematic design of this type of grippers. The knowledge presented, in terms of gripper, material and handling process, are classified. The reasoning strategy is based upon a combination of a depth-first search method and a heuristic method. The heuristic search method finds a final solution from a given set of feasible solutions and can synthesize new solutions to accomplish the required specifications. Details of the main features of the system are given, including its ability to take critical design decisions according to four criteria, weighted by the designer. The knowledge-based system was implemented in the Kappa P. C. 2.3.2 environment. Two examples are given to illustrate some critical aspects concerning the KBS development, to explain the operation of the proposed searching heuristic method, and to show its effectiveness in producing design concepts for grippers.

An Advisory Conceptual Design Tool for Mechanical Transmission Systems

1994 ◽  
Author(s):  
C. P. Huang ◽  
F. W. Liou ◽  
J. J. Malyamakkil ◽  
W. F. Lu
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Conceptual Design ◽  
Design Process ◽  
Design Tool ◽  
Mechanical Transmission ◽  
Transmission Systems ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Packaging Machine

Abstract This paper presents an advisory conceptual design tool for mechanical transmission systems. Space consideration was taken into account during the design process. A prototype function tree was built in the form of knowledge-based system to transfer a designer’s idea into a set of mechanical components. An advisory expert system was also developed to help a designer in decision making. As an example, a packaging machine is designed using the developed system.

Hybrid system approach to optimum design of a ship

1999 ◽  
Vol 13 (1) ◽  
pp. 1-11 ◽  
Author(s):  
DONGKON LEE
Keyword(s):  
Optimum Design ◽  
Hybrid System ◽  
Optimization Algorithm ◽  
Search Method ◽  
Design Stage ◽  
Search Efficiency ◽  
Knowledge Based System ◽  
Initial Design ◽  
Optimum Point ◽  
Knowledge Based

To obtain optimal design efficiently in the initial design stage of a ship, a hybrid system is developed by integrating the optimization algorithm and knowledge-based system. The hybrid system can manipulate numeric and symbolic data simultaneously. To increase search efficiency in a design space, the optimization algorithm (optimizer) is implemented by coupling a genetic algorithm (GA) and search method. The optimizer determines a candidate region around the optimum point by using the GA, then searches the optimum point by the search method concentrating in this region, thus reducing calculation time and increasing search efficiency. To generate input data for the optimizer, a rule-based system is developed. Some domain knowledge for ship optimization in the initial design stage is retrieved from a database of existing ship and design experts. The obtained knowledge is stored in the knowledge base. The optimizer incorporates a knowledge-based system with heuristic and analytic knowledge, thereby narrowing the feasible space of the design variables. Therefore, search speed and the capability of finding an optimum point will be increased in comparison with conventional approach. The developed system is applied principally to particulars of optimization of ships with multicriteria. Through application ship design, it shows that the hybrid system can be a useful tool for optimum design.

Capturing Evolutional Knowledge Using Time Interval Tracing

2007 ◽  
Vol 11 (4) ◽  
pp. 373-380
Author(s):  
Shun-Chieh Lin ◽  
◽  
Chia-Wen Teng ◽  
Shian-Shyong Tseng ◽  
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Time Domain ◽  
Domain Knowledge ◽  
Domain Ontology ◽  
Time Interval ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Over Time

Knowledge acquisition is a critical bottleneck in building a knowledge-based system. Much research and many tools have been developed to acquire domain knowledge with embedded rules that may be ignored in constructing the initial prototype. Due to different backgrounds and dynamic knowledge changing over time, domain knowledge constructed at one time may be degraded at any time thereafter. Here, we propose knowledge acquisition, called enhanced embedded meaning capturing under uncertainty deciding (enhanced EMCUD), which constructs a domain ontology and traces information over time to efficiently update time-related domain knowledge based on the current environment. We enrich the knowledge base and ease the construction of domain knowledge that changes with times and the environment.

X-Breed: a multiple-domain knowledge based system integrated through a blackboard architecture

1995 ◽  
Vol 48 (3) ◽  
pp. 243-270 ◽  
Author(s):  
Z. Hochman ◽  
H. Hearnshaw ◽  
R. Barlow ◽  
J.F. Ayres ◽  
C.J. Pearson
Keyword(s):  
Domain Knowledge ◽  
Blackboard Architecture ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Multiple Domain

A knowledge-based system for multi-disciplinary conceptual design synthesis

2012 ◽  
Vol 226 (12) ◽  
pp. 2950-2966 ◽  
Author(s):  
Ze-Lin Liu ◽  
Yong Chen ◽  
You-Bai Xie
Keyword(s):  
Conceptual Design ◽  
Design Synthesis ◽  
Synthesis Algorithm ◽  
Design Task ◽  
Knowledge Based System ◽  
Domain Specific ◽  
Knowledge Based ◽  
Viable Approach ◽  
Prototype Software

Exploring wide multi-disciplinary solution spaces to create conceptual design solutions is a difficult task for human designers due to lack of sufficient multi-disciplinary knowledge. A viable approach would be to develop a computer-aided system to synthesize the wide variety of knowledge for a given design task. However, the existing design synthesis systems are mainly domain-specific, focusing on conceptual design synthesis in a single or few limited disciplines. Therefore, this article introduces the development of a knowledge-based system for multi-disciplinary conceptual design synthesis, including the establishment of a knowledge base for organizing multi-disciplinary principle solutions and a design synthesis algorithm. The implementation of a prototype software is also reported, with the conceptual design of a solar fountain as a demonstrative case. The results of the case study show that the system can automatically and conveniently generate multi-disciplinary conceptual solutions.

The Advisor, a Fuzzy Knowledge-Based System for Rapid Instrumentation and Control Using a Taguchi-Based Quality Function: Part I — Theory

2005 ◽  
Author(s):  
Riko Tantra ◽  
Glenn Y. Masada
Keyword(s):  
Selection Process ◽  
User Preferences ◽  
Fuzzy Decision Making ◽  
Quality Function ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Feasible Solutions ◽  
And Control ◽  
Selection Of ◽  
Fuzzy Knowledge

A fuzzy knowledge-based software system, called The Advisor, is developed for mechanical designers to rapidly select instrumentation and control system solutions. Its knowledge-based system uses application constraints to select feasible solutions, and its fuzzy logic algorithms rank those solutions based upon user preferences. A new Quality Function, inspired by the Taguchi Quality Function, is proposed to combine quantitative and linguistic information in the fuzzy decision-making inference engine to better capture human tradeoff behavior in the equipment selection process. To date, a Motor Advisor and a Temperature Sensor Advisor have been developed and successfully tested. Part I of this paper presents the theory and methodology of The Advisor, its component algorithms to support the selection of motor systems, and the implementation of the new Quality Function in the Motor Advisor. Part II tests The Advisor in the selection of a load motor, motor driver, and motor controller in a motor failure design project.

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