Knowledge-Based Design Support System for Conceptual Design of Product-Service Systems

2013 ◽  
pp. 41-52 ◽  
Author(s):  
Yutaro Nemoto ◽  
Fumiya Akasaka ◽  
Yoshiki Shimomura
Keyword(s):  
Conceptual Design ◽  
Support System ◽  
Service Systems ◽  
Design Support ◽  
Knowledge Based ◽  
Product Service ◽  
Design Support System ◽  
Knowledge Based Design ◽  
Product Service Systems

Development of a knowledge-based design support system for Product-Service Systems

2012 ◽  
Vol 63 (4) ◽  
pp. 309-318 ◽  
Author(s):  
Fumiya Akasaka ◽  
Yutaro Nemoto ◽  
Koji Kimita ◽  
Yoshiki Shimomura
Keyword(s):  
Support System ◽  
Service Systems ◽  
Design Support ◽  
Knowledge Based ◽  
Product Service ◽  
Design Support System ◽  
Knowledge Based Design ◽  
Product Service Systems

Development of a knowledge-based design support system

1992 ◽  
Vol 5 (1) ◽  
pp. 31-40 ◽  
Author(s):  
T. Smithers ◽  
M.X. Tang ◽  
N. Tomes ◽  
P. Buck ◽  
B. Clarke ◽  
...  
Keyword(s):  
Support System ◽  
Design Support ◽  
Knowledge Based ◽  
Design Support System ◽  
Knowledge Based Design

A Knowledge-Based Conceptual Design Support System

2011 ◽  
Vol 308-310 ◽  
pp. 1540-1545 ◽  
Author(s):  
Woldemichael Dereje Engida ◽  
Fakhruldin Mohd Hashim ◽  
Sujan Debnath
Keyword(s):  
Conceptual Design ◽  
Design Process ◽  
Support System ◽  
Computer Aided Design ◽  
Concept Generation ◽  
Design Quality ◽  
Design Support ◽  
Knowledge Based ◽  
Concept Evaluation ◽  
Design Support System

Conceptual design process is considered as the most critical and important phase of product design process. It is the stage where product’s fundamental features are determined, large proportion of the lifecycle cost of the product is committed, and other major decisions are made, which have significant impact on the downstream design and related manufacturing processes. It is also a knowledge intensive process where diverse knowledge and several years of experience are put together to design quality and cost effective products. Unfortunately, computer support systems for this phase are lagging behind compared to the currently available commercial computer aided design (CAD) tools for the later stage of design. This paper proposes a knowledge-based conceptual design support system in which design concepts from existing products and previous experiences are captured and used to design future products. The conceptual design support system will assist designers during the conceptual design process by generating concepts on a morphology chart and handling some of the repetitive tasks. In addition, the generated concepts may inspire designers to generate new concepts. The design support system addresses the key features of conceptual design process such as functional analysis, concept generation and concept evaluation with aid of the production rules within the knowledge-based system.

A knowledge-based architecture for intelligent design support

1997 ◽  
Vol 12 (4) ◽  
pp. 387-406 ◽  
Author(s):  
MING XI TANG
Keyword(s):  
Knowledge Representation ◽  
Engineering Design ◽  
Support System ◽  
Intelligent Design ◽  
Knowledge Engineering ◽  
Design Knowledge ◽  
Design Support ◽  
Knowledge Based ◽  
Design Support System ◽  
Knowledge Based Design

The development of a knowledge-based design support system is a lengthy and costly process because various computational techniques necessary for intelligent design support are not readily available in a knowledge-based environment. The systematisation of design knowledge needs combined efforts from designers and knowledge engineers. Existing knowledge-based system development tools offer limited support to intelligent design support which require sophisticated knowledge engineering techniques in terms of knowledge representation, inference, control, truth maintenance and learning. In this paper, a knowledge-based architecture for intelligent design support is described. The existing knowledge-based design system architectures are reviewed first. Five key issues in intelligent design support using knowledge engineering techniques, i.e. design knowledge representation, structure of design knowledge base, intelligent control of design process, consistency and context management of design knowledge, and modelling of design collaboration are then discussed. These discussions provide a basis for a description of a knowledge-based design support system architecture which has been implemented in a Lisp-based environment and tested in two different domains. Current application of this architecture in the development of a design support system in the domain of mechanical engineering design at the Cambridge Engineering Design Centre is presented and evaluated.

scholarly journals A Hybrid Knowledge-Based Recommender for Product-Service Systems Mass Customization

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 296
Author(s):  
Laila Esheiba ◽  
Amal Elgammal ◽  
Iman M. A. Helal ◽  
Mohamed E. El-Sharkawi
Keyword(s):  
Recommender System ◽  
Mass Customization ◽  
Constraint Satisfaction Problem ◽  
Service Systems ◽  
Prototype System ◽  
Knowledge Based ◽  
Product Service ◽  
Wide Range ◽  
Hybrid Knowledge ◽  
Product Service Systems

Manufacturers today compete to offer not only products, but products accompanied by services, which are referred to as product-service systems (PSSs). PSS mass customization is defined as the production of products and services to meet the needs of individual customers with near-mass-production efficiency. In the context of the PSS mass customization environment, customers are overwhelmed by a plethora of previously customized PSS variants. As a result, finding a PSS variant that is precisely aligned with the customer’s needs is a cognitive task that customers will be unable to manage effectively. In this paper, we propose a hybrid knowledge-based recommender system that assists customers in selecting previously customized PSS variants from a wide range of available ones. The recommender system (RS) utilizes ontologies for capturing customer requirements, as well as product-service and production-related knowledge. The RS follows a hybrid recommendation approach, in which the problem of selecting previously customized PSS variants is encoded as a constraint satisfaction problem (CSP), to filter out PSS variants that do not satisfy customer needs, and then uses a weighted utility function to rank the remaining PSS variants. Finally, the RS offers a list of ranked PSS variants that can be scrutinized by the customer. In this study, the proposed recommendation approach was applied to a real-life large-scale case study in the domain of laser machines. To ensure the applicability of the proposed RS, a web-based prototype system has been developed, realizing all the modules of the proposed RS.

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