A Network Based Modelling Approach Using the Dimensional Analysis Conceptual Modeling (DACM) Framework for Additive Manufacturing Technologies

Mokhtarian, Hossein; Coatanéa, Éric; Paris, Henri; Ritola, Tuomas; Ellman, Asko; Vihinen, Jorma; Koskinen, Kari T.; Ikkala, Kimmo

doi:10.1115/detc2016-60473

Cited by 7 publications

(8 citation statements)

References 7 publications

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“… We only tested the efficiency of MDA for specimen optimization. However, this approach, using the dimensional analysis conceptual modeling (DACM) framework developed by Coatanéa [ 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 ], can also be extended and successfully implemented in different areas of additive manufacturing. The MLs we deduced are also fully applicable to metals.…”

Section: Discussionmentioning

confidence: 99%

Dimensional Methods Used in the Additive Manufacturing Process

Száva,

Vlase,

Scutaru

et al. 2023

Polymers

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It is a well-known fact that in the field of modern manufacturing processes, additive manufacturing (AM) offers unexpected opportunities for creativity and rapid development. Compared with classical manufacturing technologies, AM offers the advantages of reducing weight and improving performance and offers excellent design capabilities for prototyping and rapid sample manufacture. To achieve its full potential regarding cost, durability, material consumption, and rigidity, as well as maintaining competitiveness, there are several research directions that have not been explored. One less frequently explored direction is the involvement of dimensional methods in obtaining an optimal and competitive final product. In this review, we intend to discuss the ways in which dimensional methods, such as geometric analogy, similarity theory, and dimensional analysis, are involved in addressing the problems of AM. To the best of our knowledge, it appears that this field of engineering has not fully maximized the advantages of these dimensional methods to date. In this review, we survey mainly polymer-based AM technology. We focus on the design and optimization of highly competitive products obtained using AM and also on the optimization of layer deposition, including their orientation and filling characteristics. With this contribution to the literature, we hope to suggest a fruitful direction for specialists involved in AM to explore the possibilities of modern dimensional analysis.

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Section: Discussionmentioning

confidence: 99%

Dimensional Methods Used in the Additive Manufacturing Process

Száva,

Vlase,

Scutaru

et al. 2023

Polymers

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“…The semantic fidelity prism developed by Roca can be a promising answer to these challenges [57]. The Dimensional Analysis Conceptual Modelling (DACM) framework [58,59] offers the basic concepts to operationalise, fidelity measurements in superconductor modelling and simulations. Future solutions in surrogate modelling for superconductors will benefit from explainability and parsimony properties because they should be analysed and understood by humans, contrary to DL solutions that are black boxes.…”

Section: Advances In Science and Technology To Meet Challengesmentioning

confidence: 99%

“…As a result, new decomposition strategies can be developed based on the information obtained from the detailed models. A promising research direction is to develop generic causal graphs representations of MDO problems using the DACM framework [58,59], combined with physical contradictions detection and reduction using the Theory of Inventive Problem Solving separation principles since contradictions are the root cause of optimisation processes.…”

Section: Advances In Science and Technology To Meet Challengesmentioning

confidence: 99%

Roadmap on artificial intelligence and big data techniques for superconductivity

Yazdani-Asrami

Song

Morandi

et al. 2023

Supercond. Sci. Technol.

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This paper presents a roadmap to the application of AI techniques and big data for different modelling, design, monitoring, manufacturing and operation purposes of different superconducting applications. To help superconductivity researchers, engineers, and manufacturers understand the viability of using AI and big data techniques as future solutions for challenges in superconductivity, a series of short articles are presented to outline some of the potential applications and solutions. These potential futuristic routes and their materials/technologies are considered for a 10-20 years time-frame.

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“…. (5) where 'x' are called the repeating variables, yi the performance variable, and αij are the exponents. Equation (5) in this work for detecting contradictions in the system of interest, taking benefit of the mathematical machinery developed by Bhaskar and Nigam [37].…”

Section: =mentioning

confidence: 99%

“…It enables the combined modeling and qualitative simulation of parts and AM processes. The Framework named 'Dimensional Analysis Conceptual Design (DACM) Framework' has been tested and validated on different case studies and for different usages [4], [5] [6]. This article proposes some extensions to the original Framework, which focuses on systems behavior [5] [7], to make it more suitable for Design for Additive Manufacturing.…”

Section: Introductionmentioning

confidence: 99%

A Conceptual Design and Modeling Framework for Integrated Additive Manufacturing

Mokhtarian

Coatanéa

Paris

et al. 2018

Journal of Mechanical Design

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Modeling and simulation for additive manufacturing (AM) is commonly used in industry. Nevertheless, a central issue remaining is the integration of different models focusing on different objectives and targeting different levels of details. The objective of this work is to increase the prediction capability of characteristics and performances of additively manufactured parts and to co-design parts and processes. The paper contributes to this field of research by integrating part's performance model and additive technology process model into a single early integrated model. The paper uses the dimensional analysis conceptual modeling (DACM) framework in an AM perspective to generate causal graphs integrating the AM equipment and the part to be printed. DACM offers the possibility of integrating existing knowledge in the model. The framework supported by a computer tool produces a set of governing equations representing the relationships among the influencing variables of the integrated model. The systematic identification of the weaknesses and contradictions in the system and qualitative simulation of the system are some of the potential uses of the model. Ultimately, it is a way to create better designs of machines and parts, to control and qualify the manufacturing process, and to control three-dimensional (3D) printing processes. The DACM framework is tested on two cases of a 3D printer using the fused filament fabrication (FFF) powder bed fusion. The analysis, applied to the global system formed of the 3D printer and the part, illustrates the existence of contradictions. The analysis supports the early redesign of both parts and AM process (equipment) and later optimization of the control parameters.

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A Network Based Modelling Approach Using the Dimensional Analysis Conceptual Modeling (DACM) Framework for Additive Manufacturing Technologies

Cited by 7 publications

References 7 publications

Dimensional Methods Used in the Additive Manufacturing Process

Dimensional Methods Used in the Additive Manufacturing Process

Roadmap on artificial intelligence and big data techniques for superconductivity

A Conceptual Design and Modeling Framework for Integrated Additive Manufacturing

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