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Knowledge formalization and recommendation in computational design synthesis for 4D printing

Abstract : Coined in 2013, 4D printing is considered as a promising technology that combines additive manufacturing (AM) processes and smart materials (SMs). Through this technology objects can change over time in terms of functionalities, shape, or properties under the effect of energy stimulation. Although AM processes allow shape, hierarchical, functional, and materials complexities to the 3D objects, the conjunction with SMs provides much more freedom in designing adaptive/transformative structures. Over the last decade, a growing body of research with ad hoc proofs-of-concept have been proposed at the physical level (i.e., by using different AM processes with commercial or customized 3D printers and tuned SMs). Despite the numerous endeavors, 4D printing requires much more research efforts in design and engineering to define complex structures aligning with new usage scenarios. Considering that product architects and designers are neither AM experts nor materials experts, this innovative technology requires the development of new models, approaches, methodologies, and tools in design. The main objective of this PhD – falling under the domain of design for 4D printing – is to address research issues on 4D printing knowledge formalization and reuse, especially in computational design synthesis. First, a framework is proposed to build an ontology from both a top-down approach (based on multi-dimensionalism theories and basic formal ontology) and a bottom-up approach (based on research experiments). This resulted ontology formalizes and represents the knowledge in 4D printing including its main pillars, i.e., AM processes, SMs, stimuli, and transformation functions. It constitutes a relevant knowledge base for various purposes in design for 4D printing, such as material-process selection, transformation sequence planning, and materials distribution recommendation to name a few. In addition, the second contribution is focused on the multi-represen tation of a 4D printed object at the computational design synthesis stage because it plays a crucial role (i.e. functional , behavioral, structural) in the advances of the design for 4D printing. This multi-representation is then further described as a sub-ontology to be integrated into the domain ontology respecting different rules and constraints. Finally, a recommendation system allowing quickly identifying the material distribution in 4D printed objects is proposed. This system is populated by experiments from the literature. These experiments constitute the instances for the ontology and a base (composed of words) to feed a vector space model for proposing a specific material distribution. A dedicated tool has been developed in Rhinoceros3D/Grasshopper environment to illustrate the applicability and the relevance of the proposals.
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https://theses.hal.science/tel-03790298
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Submitted on : Wednesday, September 28, 2022 - 11:06:48 AM
Last modification on : Thursday, September 29, 2022 - 4:23:41 AM

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  • HAL Id : tel-03790298, version 1

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Saoussen Dimassi. Knowledge formalization and recommendation in computational design synthesis for 4D printing. Other. Université Bourgogne Franche-Comté, 2022. English. ⟨NNT : 2022UBFCA002⟩. ⟨tel-03790298⟩

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