Product Function Matrix and its Request Model

A developed model of the first structural shape of the matrix of product function and its requests model have been shown in practice. It relates functions of the product and its requests model, and technical systems that solve them. Functions are described by parameters, determined by physical laws. The technical systems and functions are related among themselves through correlations that fill the field of the matrix. The correlations are determined through winning parameters. Winning parameters are determined from the set of parameters of each function that have the greatest importance and influence on a specific function. The model is implemented into a developed prototype computer system. The first structural shape of the product function matrix and its requests model for the stator of the electrical motor of the intake unit have been generated through the prototype computer system. By implementing the product function matrix and its requests model in the process of product conceptual design, it becomes possible for a designer to examine himself by comparing the possibilities of implementation of new ideas with previously built products

Fractal-based re-design

Engineering conceptual design is a knowledge-intensive process that generates solutions to a product specification. It is a process that can benefit from past experience of similar designs. In reality however, designers often have limited time to build up the necessary experience and are, in any event, unlikely to become experts in all relevant fields. Hence there is a need to capture, store and reuse valuable knowledge. Currently available conventional CAD systems offer limited possibilities for the re-use of existing designs. Techniques from the field of Artificial Intelligence (Al) may be applied to aid the conceptual design phase, which is known as the area of intelligent computer-aided design. The aim of this work is to identify and externalise design knowledge using a fractal-like model, to understand the role of design knowledge in conceptual design and to use design knowledge as a guide for every stage of concept development. This research provides a framework for supporting conceptual design, which uses the techniques of Case-Based Reasoning (CBR) and fractal theory, for reasoning about the design and development of computer-based design aids. The framework is comprised of three parts. The first is case representation. This research proposes a new representation technique, Fractal-like Design Modelling (FDM), which integrates design knowledge in a graph-based form and has fractal-specific characteristics. The second is case retrieval. Based on FDM, the similarity between a new design and the existing designs is assessed by concurrently applying a feature-based similarity measure and a structure-based similarity measure. The third is case adaptation. With the help of fractal characteristics, an approach of adaptive design is developed by performance revision and by goal-oriented substitution. These three parts work together to achieve an automated, case-based, conceptual design method: Fractal-Based Re-design.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Fractal-based re-design.

Engineering conceptual design is a knowledge-intensive process that generates solutions to a product specification. It is a process that can benefit from past experience of similar designs. In reality however, designers often have limited time to build up the necessary experience and are, in any event, unlikely to become experts in all relevant fields. Hence there is a need to capture, store and reuse valuable knowledge. Currently available conventional CAD systems offer limited possibilities for the re-use of existing designs. Techniques from the field of Artificial Intelligence (Al) may be applied to aid the conceptual design phase, which is known as the area of intelligent computer-aided design. The aim of this work is to identify and externalise design knowledge using a fractal-like model, to understand the role of design knowledge in conceptual design and to use design knowledge as a guide for every stage of concept development. This research provides a framework for supporting conceptual design, which uses the techniques of Case-Based Reasoning (CBR) and fractal theory, for reasoning about the design and development of computer-based design aids. The framework is comprised of three parts. The first is case representation. This research proposes a new representation technique, Fractal-like Design Modelling (FDM), which integrates design knowledge in a graph-based form and has fractal-specific characteristics. The second is case retrieval. Based on FDM, the similarity between a new design and the existing designs is assessed by concurrently applying a feature-based similarity measure and a structure-based similarity measure. The third is case adaptation. With the help of fractal characteristics, an approach of adaptive design is developed by performance revision and by goal-oriented substitution. These three parts work together to achieve an automated, case-based, conceptual design method: Fractal-Based Re-design

Reliability and realizability risk evaluation of concept designs

This thesis addresses the improvement in quality of decision making in design through the use of decomposed design evaluation. The research reported in this thesis is supported by the Design Research Methodology. To perform decomposed decision making, it is necessary to identify criteria that are deemed important for this activity. Questionnaire surveys, literature review and interviews with industry helped to identify these criteria. Reliability and realizability are two criteria that are selected for research in this thesis. The questionnaire surveys are discussed in chapter 2. A review of literature on decision making, reliability and realizability is reported in chapters 3 and 4. Methodologies for evaluating reliability and physical realizability are discussed in chapter 5. Relative reliability risk assessment methodology is applied to various examples consisting of university and industry projects in chapter 6. The application helps to reveal the strengths of the methodology and is termed ‘Verification of the methodology’. Validation issues of both the methodologies are dealt with in chapter 7 using the controlled experimental design. It is found that both the methodologies help to improve the quality of decision making during design evaluation. Relative reliability risk evaluation methodology helps to improve the quality of decision making to a substantial extent but physical realizability evaluation methodology shows only a little improvement in quality of decision making. Finally, it is suggested that the decomposed design evaluation methodology helps to improve the quality of decision making and is therefore proposed to be used by both novice and experienced designers.EThOS - Electronic Theses Online ServiceGBUnited Kingdo