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Integrated knowledge-based system for containership lashing bridge optimization design
Published in Pentti Kujala, Liangliang Lu, Marine Design XIII, 2018
KBE is regarded as one among the most vigorous branches of artificial intelligence. It principally seeks to incorporate relative design experience, design standards and specifications into design software to design new product (Chapman and Pinfold, 1999; Penoyer, Burnett, Fawcett and Fawcett; 2000; McMahon, Lowe, Culle; 2004). Sanya and Shehab (2014) presentes a novel KBE framework for effectuating platform-independent knowledge-enabled product design systems. Through adopting this method, knowledge reuse shall be promoted, and platform-specific approaches shall be eliminated. In the design domain, KBE is the most frequently adopted method to support customization and automotive design that can shorten the lead time, improve quality and gain more profit (Wei Guo, Jiafu Wen, Hongyu Shao and Lei Wang, 2015). An intelligent optimization design system of hull structure is developed by Yang (2014) whereby KBE technology is applied. A successful application of KBE method is presented by Jin-ju Cui and De-yu Wang (2013) in ship structural design and optimization, and design cycle can be reduced evidently. Yang (2012) presents a KBE methodology for ship hull structural member design. The present KBE technology provides appropriate suggestions, support and information to achieve reuse and accumulation of knowledge. A KBE system is developed by Corallo (2009) for low-pressure turbine automation.
Product Design and Development
Published in Quamrul H. Mazumder, Introduction to Engineering, 2018
Knowledge-based engineering (KBE) is a system that can be programmed to reproduce the decisions that an engineer has to take when producing designs. This system use databases, a knowledge base, and a set of rules are called algorithms, which are able to make decisions using the knowledge contained in the knowledge base. KBE is a step in the development of CAD systems, because it not only uses design information it also includes the rules that are used to create design. KBE systems are also known as expert systems.
Smart Innovation Engineering: Toward Intelligent Industries of the Future
Published in Cybernetics and Systems, 2018
Mohammad Maqbool Waris, Cesar Sanin, Edward Szczerbicki
Use of knowledge-based engineering (KBE) systems to cope with the process of product innovation is the need of an hour. KBE is a particular type of knowledge-based system (KBS) that is based upon the integration of object-oriented and ontology-based programming, artificial intelligence (AI) and computer-aided design technologies (Pinfold and Chapman 2001; Pietranik and Nguyen 2014). A KBE is a system that uses artificial intelligence techniques in problem-solving processes to support human decision-making, learning, and action. One of the hallmarks of the KBE approach is to automate repetitive, noncreative design tasks. Apart from significant time and cost savings, automation also frees up time for creativity (Cooper and LaRocca 2007). Moreover, experience-based knowledge reuse guided by an established KBE framework has the potential to support the product innovation process.
Product development cost estimation through ontological models – a literature review
Published in Journal of Management Analytics, 2019
Rafael Voltolini, Kaio Vasconcelos, Milton Borsato, Margherita Peruzzini
The early stages of product development are full of uncertainties and complex parameters, as they directly affect the estimation of product and project costs, such as development time and manufacturing process (Xu et al., 2012). Experts in different areas, with or without industry and market experience, must deal with the challenges that arise unexpectedly in an agile and responsive manner. For the development of new products, in the initial stages of the design process, the uncertainties and the lack of knowledge on the subject may reveal serious risks for the future project execution (Khodakarami & Abdi, 2014). Collecting tacit knowledge, storing and applying solutions intelligently is a way to avoid forgetting information for future projects (Relich, 2016). Knowledge management is described by knowledge-based engineering (KBE) as a way to manage implicit knowledge and make it explicit for reuse and search for solutions (Quintana-Amate, Bermell-Garcia, & Tiwari, 2015). Expert systems are approaches that simulate the knowledge of a specialist in a certain area of knowledge. In this context, ontological approaches are characterized as a way of capturing knowledge, storing and generating solutions or reusing knowledge later (Sanya & Shehab, 2015). It is a way of making the process flexible and responsive to the demands of the market and customer requirements (Efthymiou, Sipsas, Mourtzis, & Chryssolouris, 2015). In case of a collaborative research, the ontological approach allows data to be inserted and consulted by different ways around the globe. With the evolution of the use, the instrument tends to increase the interoperability, making the instrument continuously refined and increased with the knowledge, increasing its applicability power (Fortineau, Paviot, & Lamouri, 2013).
Knowledge-Based Engineering in the design for manufacture of prefabricated façades: current gaps and future trends
Published in Architectural Engineering and Design Management, 2018
Jacopo Montali, Mauro Overend, P. Michael Pelken, Michele Sauchelli
The purpose of KBE is to reduce the design effort through automation of repetitive tasks, knowledge reuse and to support product development in a multidisciplinary environment (Verhagen, Bermell-Garcia, Van Dijk, & Curran, 2012). KBE encapsulates various forms of knowledge such as heuristic knowledge, cost data, manufacturing best practices, rules-of-thumb and standards. KBE usually merges an object-oriented programming (OOP) approach and a parametric modelling software. The basic configuration of a KBE application is shown in Figure 4 (Reddy, Sridhar, & Rangadu, 2015).