A prospective analysis of the engineering design discipline evolution based on key influencing trends

Design and manufacturing sectors are vital agents of an economy. However, multiple challenges influence product designs such as the predicted scarcity of energy and primary materials, the ubiquitous integration of electronic components and computing science in systems' architectures, the pervasive production of data by most systems, the emphasis given to CO2 free energy solutions, recycling, and reuse, the transformation of the consumption model from product ownership to product as a service, as well as the geopolitical conflicts. Major technological advancements leading to transformation in socio-economic practices would be required to address these challenges which can have a profound effect on design and manufacturing activities. This research aims to evaluate the potential impact and modification induced by such transformations on product design process. The research identifies that early design automation can enable coping with unmanageable cognitive load generated by cascading changes. A list of modifications to current design practices is proposed to enable the development of a new generation of design tools. The article provides an initial prospective effort to discuss the potential services and functionality that will be offered by future design tools'.Peer reviewe

Graph models for engineering design : Model encoding, and fidelity evaluation based on dataset and other sources of knowledge

Automatically extracting knowledge from small datasets with a valid causal ordering is a challenge for current state-of-The-Art methods in machine learning. Extracting other type of knowledge is important but challenging for multiple engineering fields where data are scarce and difficult to collect. This research aims to address this problem by presenting a machine learning-based modeling framework leveraging the knowledge available in fundamental units of the variables recorded from data samples, to develop parsimonious, explainable, and graph-based simulation models during the early design stages. The developed approach is exemplified using an engineering design case study of a spherical body moving in a fluid. For the system of interest, two types of intricated models are generated by (1) using an automated selection of variables from datasets and (2) combining the automated extraction with supplementary knowledge about functions and dimensional homogeneity associated with the variables of the system. The effect of design, data, model, and simulation specifications on model fidelity are investigated. The study discusses the interrelationships between fidelity levels, variables, functions, and the available knowledge. The research contributes to the development of a fidelity measurement theory by presenting the premises of a standardized, modeling approach for transforming data into measurable level of fidelities for the produced models. This research shows that structured model building with a focus on model fidelity can support early design reasoning and decision making using for example the dimensional analysis conceptual modeling (DACM) framework.publishedVersionPeer reviewe

Process monitoring by deep neural networks in directed energy deposition : CNN-based detection, segmentation, and statistical analysis of melt pools

The complex interaction between laser and material in Laser Wire Direct Energy Deposition (LW-DED) Additive Manufacturing (AM) benefits from process monitoring methods to ensure process stability and final part quality. Understanding the relationship between process parameters and melt pool geometrical characteristics can be used to effectively monitor and in-process control the process, as the melt pool geometrical characteristics serve as crucial indicators of process stability and quality. This study presents a novel in-situ monitoring approach for LW-DED, utilizing process images for melt pool segmentation, melt pool geometrical characteristics estimation, process stability assessment, and bead geometry prediction. The segmentation of melt pool objects was successfully accomplished using Convolutional Neural Networks (CNN)-based models, enabling the prediction of essential parameters such as melt pool area, height, width, center of area, and the center point of the bounding box enclosing the melt pool. Multiple models were compared regarding the accuracy and processing speed using a controlled central composite design and random experiments. We used an Inconel alloy 625 wire and two distinct substrate materials for deposition, a coaxial laser welding head with a 3 kW fiber laser, and an off-axis welding camera for monitoring. Among the CNN architectures evaluated, YOLOv8l demonstrated superior accuracy with mean Average Precision (mAP) values of 0.925 and 0.853 for Stainless Steel (SS) and low carbon steel (S355) substrates, respectively. Additionally, YOLOv8s exhibited a notable processing speed of over 114 frames per second, which indicates its suitability for real-time process control. Furthermore, the results indicate a significant correlation between process parameters and melt pool geometry variables. Notably, a clear correlation was established between melt pool characteristics and bead geometries obtained through microscopic examinations, including penetration depth and heat-affected zone. The analysis revealed a significant correlation for the bead area and width parameters. In relation to the bead height, while the correlation exhibited a lower magnitude compared to bead area and width, it remained responsive. In addition, the tensor masks derived from the developed models have proven to be highly effective in accurately predicting bead geometries. The results demonstrate the effectiveness of YOLO-based algorithms for detecting and segmenting the melt pool. Statistical analysis confirms the significance of stabilized process data and the accuracy of melt pool geometric models. We demonstrate that integrating advanced monitoring and control techniques using artificial intelligence methods like CNN can facilitate process stability and quality control.Peer reviewe

Using artificial neural networks to model single bead geometries processed by laser-wire direct energy deposition

Peer reviewe

Modélisation intégrée produit-process à l'aide d'une approche de méta-modélisation reposant sur une représentation sous forme de graphes: Application à la fabrication additive

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 modelingin AM. Accordingly, the thesis oversees the product development process and presents the Dimensional Analysis Conceptual Modeling (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 modeling framework 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 designalterations and AM machine redesign, and optimized part design or process parameter settings. DACM framework shows potentials to be used as ametamodeling approach for additive manufacturing.La fabrication additive (FA) a initié un changement de paradigme dans le secteur de la conception et de la fabrication des produits grâce à ses capacités uniques. Cependant, l'intégration des technologies de fabrication additive dans la productique traditionnelle doit permettre d'assurer une production fiable et une qualité reproductible des pièces. Dans cette optique, la modélisation et la simulation jouent un rôle essentiel pour améliorer la compréhension de la nature complexe et multi-physique des procédés de fabrication additive. De plus, l’intégration simultanée de différents modèles multi-physiques et de la prise en compte du procédé utilisé et de la pièce constituent toujours un défi pour la modélisation de ces technologies. L’objectif final de cette recherche est de développer et d’appliquer une approche de modélisation permettant une modélisation intégrée de la fabrication additive. Cette thèse analyse le processus de développement du produit et présente une méthodologie innovante intitulée ‘Dimensional Analysis Conceptual Modeling’ (DACM) pour modéliser les produits et les procédés de fabrication aux différentes étapes de conception. La méthode a été développée pour permettre la simulation de modèles multi-physiques. Elle intègre également une recherche systématique de faiblesses et de contradictions dans une première évaluation des solutions potentielles au problème. La méthodologie développée est appliquée dans plusieurs études de cas afin de présenter des modèles intégrant les processus de fabrication additive et les pièces à fabriquer. Les résultats montrent que la méthodologie DACM permet de modéliser distinctement et simultanément le produit et le processus de fabrication. Cette méthodologie permet aussi d'intégrer les modèles théoriques et expérimentaux déjà existants. Elle contribue à la conception pour la fabrication additive et aide le concepteur à anticiper les limites des procédés et de la conception plus tôt dans les premières étapes de développement du produit. En particulier, cela permet de prendre les bonnes décisions selon les différentes possibilités d'optimiser la conception des pièces et le paramétrage des machines de fabrication additive pour aboutir à la solution la plus adaptée. La méthode permet également de détecter la nécessité de reconcevoir des machines existantes en détectant les faiblesses de celles-ci. Cette thèse montre que la méthode DACM peut être potentiellement utilisée comme une approche de méta-modélisation pour la fabrication additive

The Effects of Illicit Drug Trade on Stabilizing Macro-economy

Annaul production of more than 5000 tons of opiates in Afghanistan and the necessity of its traffic into other countries, affects on Iran’s economy as one of the Afghanistan’s neighboring countries. It is more imprtant when drug traffickers joined to new organized crime groups and run different kinds of criminal acts. On this basis, in this article the effects of illicit drug trade and other criminal acts on macro-economy is studied by by using experiences of some countries. Although some researchers believe that funding the criminal acts in countries which have broad financial activities is more than other economies, there are signs that other countries are not protected from this issue. Illicit drug trade consists %20 of money supply in Colombia where central bank officials are faced with some problems. Owing to the special conditions of Iran where is passing through economical period, in this article the experiences of the countries that have been faced with fund-related criminal acts in implementing their reforms and stabilizing policies are addressed. Such experiences indicate that in economical reforming process, informal markets are emerged and informal (underground) economical sections neuteralize governmental policies. In Russia, for example, the lack of an effective banking system and specified exchange market as well as a legal risk-taking system have resulted in arise of an informal system. In one hand, informal systems which obtain their financial resources through criminal acts, keep the faced prices in a high level and in the other hand, influence heavily over financial policies through increasing interest rates and loaning in informal markets. The experiences of reforming economies indicate that the behaviour of their governments is not under control and they promote informal system (underground economy) by serious interventions or unlimited loaning. This article is also contemplating privitization and liberating policies in the conditions that informal economy acts under the support of criminal organizations

Product-process integrated meta-modeling using a graph-based approach : Application to additive manufacturing

La fabrication additive (FA) a initié un changement de paradigme dans le secteur de la conception et de la fabrication des produits grâce à ses capacités uniques. Cependant, l'intégration des technologies de fabrication additive dans la productique traditionnelle doit permettre d'assurer une production fiable et une qualité reproductible des pièces. Dans cette optique, la modélisation et la simulation jouent un rôle essentiel pour améliorer la compréhension de la nature complexe et multi-physique des procédés de fabrication additive. De plus, l’intégration simultanée de différents modèles multi-physiques et de la prise en compte du procédé utilisé et de la pièce constituent toujours un défi pour la modélisation de ces technologies. L’objectif final de cette recherche est de développer et d’appliquer une approche de modélisation permettant une modélisation intégrée de la fabrication additive. Cette thèse analyse le processus de développement du produit et présente une méthodologie innovante intitulée ‘Dimensional Analysis Conceptual Modeling’ (DACM) pour modéliser les produits et les procédés de fabrication aux différentes étapes de conception. La méthode a été développée pour permettre la simulation de modèles multi-physiques. Elle intègre également une recherche systématique de faiblesses et de contradictions dans une première évaluation des solutions potentielles au problème. La méthodologie développée est appliquée dans plusieurs études de cas afin de présenter des modèles intégrant les processus de fabrication additive et les pièces à fabriquer. Les résultats montrent que la méthodologie DACM permet de modéliser distinctement et simultanément le produit et le processus de fabrication. Cette méthodologie permet aussi d'intégrer les modèles théoriques et expérimentaux déjà existants. Elle contribue à la conception pour la fabrication additive et aide le concepteur à anticiper les limites des procédés et de la conception plus tôt dans les premières étapes de développement du produit. En particulier, cela permet de prendre les bonnes décisions selon les différentes possibilités d'optimiser la conception des pièces et le paramétrage des machines de fabrication additive pour aboutir à la solution la plus adaptée. La méthode permet également de détecter la nécessité de reconcevoir des machines existantes en détectant les faiblesses de celles-ci. Cette thèse montre que la méthode DACM peut être potentiellement utilisée comme une approche de méta-modélisation pour la fabrication additive.Mots-clés: Fabrication Additive, Conception Pour la Fabrication Additive, Modélisation Intégrée, Développement de Produit, Dimensional Analysis Conceptual Modeling FrameworkAdditive 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 the 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 additive manufacturing. Accordingly, the thesis oversees the product development process and presents the Dimensional Analysis Conceptual Modeling (DACM) Framework to model the product and manufacturing processes at the design stages of 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 modeling framework 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 approach for additive manufacturing

Function modelling combined with physics-based reasoning for assessing design options and supporting innovative ideation

International audienc