Hossein Mokhtarian

Achieving predictable, reliable, and cost-effective operations in wire and arc additive manufacturing is a key concern during production of complex-shaped functional metallic components for demanding applications, such as those found in aerospace and automotive industries. A metamodel combining localized submodels of the different physical phenomena during welding can ensure stable material deposition. Such a metamodel would necessarily combine submodels from multiple domains, such as materials science, thermomechanical engineering, and process planning, and it would provide a holistic systems perspective of the modeled process. An approach using causal graph-based modeling and Bayesian networks is proposed to develop a metamodel for a test case using wire and arc additive manufacturing with cold metal transfer. The developed modeling approach is used to characterize the effect of manufacturing variables on product dimensional quality in the form of a causal graph. A quantitative simulation using Bayesian networks is applied to the causal graph to enable process parameter tuning. The Bayesian network inference mechanism predicts the effects of the parameters on results, whereas, conversely, with known targets, it can predict the required parameter values. Validation of the developed Bayesian network model is performed using experimental tests.

Additive Manufacturing (AM) is intensifying the digitalization of the manufacturing and
generating disruptive changes and the paradigm shift in the industry. To boost the productivity
in AM, the improvement in parts qualification is required. Qualifying the parts implies the
capability to ensure constant and repeatable part properties meeting the engineering design
requirements. It implies to certify not only the structure, properties and global performance of a
part but also its manufacturing process. Modelling and simulation techniques can target them
separately, but linking models describing those characteristics is quite challenging since they
have the different level of details and sometimes different purposes. Presenting those models in
the form of causal graph will enable us to combine models with different level of details. The
current research utilizes functional analysis and dimensional analysis to present the models in
the form of the causal graph between associated parameters. The DACM Framework integrating
fundamental required methods and theories is briefly explained in this article. The current
research will then aim at developing a surrogate model capable of integrating the different
models such as microstructure models, layer by layer melting/solidification and part models. The
final comprehensive model can simulate to AM system to fulfill multi-criteria performances

Efforts towards a circular economy, increased possibilities of sensor technology and monitoring, and the developments in smart, connected products and systems have all contributed to the shift from selling products towards selling service-based offerings. Product-service systems are one example of service-based offerings, combining physical products or systems to service-based earnings logic.

Many traditional companies might benefit from exploring the possibilities of service-based offerings, but the tools for modeling, simulating and analyzing service-based offerings are somewhat lacking. There are numerous existing methods for modeling products on one hand, or business and economic models on the other, but fewer combining the two. This article presents a modeling framework for describing, analyzing and simulating offerings consisting of interdependent technical systems and business processes, based on a an extension of previous work on the Dimensional Analysis Conceptual Modeling (DACM) framework.

The goal of the framework is to allow modeling combinations of technical systems and business processes, resulting in flows of revenue between the stakeholders over a period of time. Further, the framework can be used to identify objectives of the stakeholders, and identify contradictions on a variable level, or conflicting objectives on a system level, and to propose solutions for solving the conflicts and contradictions based on general solution principles. In the article, the framework is applied on a case study of designing a reverse vending machine

This article presents the dimensional analysis conceptual modeling (DACM) framework, intended as a conceptual modeling mechanism for lifecycle systems engineering. DACM is a novel computer-aided method originally developed for military projects, but it's now available for other applications, too. The DACM framework is a powerful approach for specifying, discovering, validating, and reusing building blocks as well as analyzing system behavior in early development stages. This framework is based on dimensional analysis combined with causal graphs to represent the interactions and interdependencies among system variables. The framework's algorithms are codified into software applications to facilitate its use. This article provides a practical presentation of the steps that encompass the transformation from problem to solution space, key system variables extraction, causal ordering, clustering of variables, and qualitative analyses. The authors provide two examples that cover in detail the DACM's mathematic machinery for deriving a system's behavioral laws from a causal graph.

The application of additive manufacturing technologies in the industry is growing fast. This leads to an increasing need for reliable modeling techniques in the field of additive manufacturing. A methodology is proposed to systematically assess the influence of process parameters on the final characteristics of additively manufactured parts. The current study aims at presenting a theoretical framework dedicated to the modeling of the additive manufacturing technology. More specifically, the framework is used in the context of the study to plan and optimize the experimental process to minimize the amount of experiments required to populate the model. The framework presented is based on the Dimensional Analysis Conceptual Modelling framework (DACM). DACM is an approach supporting the production of models. This approach is designing networks representing a system architecture and behavior using an approach sharing similarities with neural networks. Based on the proposed approach, it is possible to detect where supplementary experimental data have to be collected to complete the model generated by the DACM approach. The modeling of the Direct Material Deposition process is conducted as an illustrative case study. The scope of the approach is vast and supported by validated scientific methods combined to form the core of the DACM method. The DACM framework is step by step extracting information from a description of the system architecture to create semi-automatically a model that can be simulated and used for multiple types of analyses associated for example with innovation and design improvement. The current paper will focus on the usage of the DACM framework, recently developed in a project, in the field of additive manufacturing.

Additive manufacturing technologies are opening new opportunities in term of production paradigm and manufacturing possibilities. Nevertheless, in term of environmental impact analysis supplementary research works require to be made in order to compare and evaluate them with traditional manufacturing processes. In this article, we propose to use Life Cycle Assessment (LCA) method and to associate decision criteria to support the selection of manufacturing strategies for an aeronautic turbine. The dimensionless criteria allow to define environmental trade-offs between additive and subtractive methods. This study provides an approach generalizable to other parts and processes.