Implementation of additive manufacturing into product manufacturing suffers from the challenge of part defects prediction. Due to interdependencies of design variables and manufacturing parameters in achieving suitabl...
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Implementation of additive manufacturing into product manufacturing suffers from the challenge of part defects prediction. Due to interdependencies of design variables and manufacturing parameters in achieving suitable part quality, modelling methods are necessary to provide simulation capabilities for part quality analysis at early stages of product development. A systematic methodology is proposed to extract cause-effect relationships among variables and to transform this causal model into a Bayesian network. The Bayesian network is then used to predict the effect of specific design and manufacturing parameters on part defects and to estimate the needed input parameters backwards, based on acceptable output values. (C) 2019 The Authors. Published by Elsevier Ltd.
Additive manufacturing (AM) has created a paradigm shift in product design and manufacturing sector due to its unique capabilities. However, the integration of AM technologies in mainstream production faces the challe...
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Additive manufacturing (AM) has created a paradigm shift in product design and manufacturing sector due to its unique capabilities. However, the integration of AM technologies in mainstream production faces the challenge of ensuring reliable production and repeatable quality of parts. Toward this end, modeling and simulation play a significant role to enhance the understanding of the complex multi- physics nature of AM processes. In addition, a central issue in modeling AM technologies is the integration of different models and concurrent consideration of the AM process and the part to be manufactured. Hence, the ultimate goal of this research is to present and apply a modeling approach to develop integrated modeling in AM. Accordingly, the thesis oversees the product development process and presents the dimensionalanalysisconceptualmodeling (DACM) framework to model the product and manufacturing processes at the design stages of the product development process. The framework aims at providing simulation capabilities and systematic search for weaknesses and contradictions to the models for the early evaluation of solution variants. The developed methodology is applied in multiple case studies to present models integrating AM processes and the parts to be manufactured. This thesis results show that the proposed modelingframework is not only able to model the product and manufacturing process but also provide the capability to concurrently model product and manufacturing process, and also integrate existing theoretical and experimental models. The DACM framework contributes to the design for additive manufacturing and helps the designer to anticipate limitations of the AM process and part design earlier in the design stage. In particular, it enables the designer to make informed decisions on potential design alterations and AM machine redesign, and optimized part design or process parameter settings. DACM framework shows potentials to be used as a metamodeling app
Implementation of additive manufacturing into product manufacturing suffers from the challenge of part defects prediction. Due to interdependencies of design variables and manufacturing parameters in achieving suitabl...
详细信息
Implementation of additive manufacturing into product manufacturing suffers from the challenge of part defects prediction. Due to interdependencies of design variables and manufacturing parameters in achieving suitable part quality, modelling methods are necessary to provide simulation capabilities for part quality analysis at early stages of product development. A systematic methodology is proposed to extract cause-effect relationships among variables and to transform this causal model into a Bayesian network. The Bayesian network is then used to predict the effect of specific design and manufacturing parameters on part defects and to estimate the needed input parameters backwards, based on acceptable output values.
Functional modeling is an analytical approach to design problems that is widely taught in certain academic communities but not often used by practitioners. This approach can be applied in multiple ways to formalize th...
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Functional modeling is an analytical approach to design problems that is widely taught in certain academic communities but not often used by practitioners. This approach can be applied in multiple ways to formalize the understanding of the systems, to support the synthesis of the design in the development of a new product, or to support the analysis and improvement of existing systems incrementally. The type of usage depends on the objectives that are targeted. The objectives can be categorized into two key groups: discovering a totally new solution, or improving an existing one. This article proposes to use the functional modeling approach to achieve three goals: to support the representation of physics-based reasoning, to use this physics-based reasoning to assess design options, and finally to support innovative ideation. The exemplification of the function-based approach is presented via a case study of a glue gun proposed for this Special Issue. A reverse engineering approach is applied, and the authors seek an incremental improvement of the solution. As the physics-based reasoning model presented in this article is heavily dependent on the quality of the functional model, the authors propose a general approach to limit the interpretability of the functional representations by mapping the functional vocabulary with elementary structural blocks derived from bond graph theory. The physics-based reasoning approach is supported by a mathematical framework that is summarized in the article. The physics-based reasoning model is used for discovering the limitations of solutions in the form of internal contradictions and guiding the design ideation effort.
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