Swarm-based evaluation of nonparametric SysML mechatronics system design

Chami, Mohammad; Ammar, Haitham Bou; Voos, Holger; Tuyls, Karl; Weiß, Gerhard

doi:10.1109/icmech.2013.6518576

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…This limitation is currently addressed by companies through extending SysML with their own customizations (e.g., a block is extended for systems, components, and functions). We have experimented the application of AI on SysML elements (Chami, Bou Ammar, Voos, Tuyls, & Weiss, 2013) and discovered the challenges in non-numerical elements, the text-based ones.…”

Section: Capability 1 Ai4mbse Competencies Mbse Universities Training...mentioning

confidence: 99%

Artificial Intelligence Capabilities for Effective Model‐Based Systems Engineering: A Vision Paper

Chami,

Abdoun,

Bruel

2022

INCOSE International Symp

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Both Model-Based Systems Engineering (MBSE) and Artificial Intelligence (AI) have been challenged for their deployment in real-world applications. Although MBSE remains the focal point of any systems engineering activities, its adoption still faces significant hurdles to demonstrate its return on investment. Recently, AI has received intensive attention, and its applications made their way into our daily life products. From an industrial perspective, within the context of the design and development of mechatronic systems, there is a lack of coherent foundation to enable the application of AI in MBSE. This vision paper discusses the role of AI in solving a set of MBSE challenges. As a result, we contribute by describing the actual MBSE adoption challenges and follow up with the characterization of the capabilities of AI in solving these challenges. With this initial work, we aim to trigger both AI and MBSE communities for further research discussions and industrial applications to help in achieving an intelligent design and development environment. Motivation and BackgroundDuring the last decades, technology has been enormously revolutionized for products we use in our daily lives and people's expectations increased. Indeed, competition between companies got more intense and brought new challenges to deliver smarter, safer, adaptable, and sustainable products in a faster and cheaper way. Companies designing and developing mechatronic products, for instance in transportation, aerospace and automotive, regularly face huge difficulties due to the multidisciplinary nature and complexity of their products. In most cases, these difficulties could be traced back to a set of technological, financial, or human factors (Chami & Bruel, 2018). In some cases, these difficulties can lead to massive unforeseen costs and catastrophic failures.In order to maintain a profitable business, employees perform diverse technical, administrative and cognitive activities to bridge their stakeholder needs with most of their products' features. Although these activities might sound trivial, their evolving nature triggers new challenges for keeping them up-to-date, efficient and optimized. Therefore, instead of focusing solely on delivering intelligent products, we ask ourselves why not supporting as well designing and developing them with the help of some intelligent environment?

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Section: Capability 1 Ai4mbse Competencies Mbse Universities Training...mentioning

confidence: 99%

Artificial Intelligence Capabilities for Effective Model‐Based Systems Engineering: A Vision Paper

Chami,

Abdoun,

Bruel

2022

INCOSE International Symp

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Architecture frameworks, multiobjective optimization and multiphysics simulation: Challenges and opportunities

Hammami¹

2015

2015 Annual IEEE Systems Conference (SysCon) Proceedings

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System architecture design have been proved to be NP-Hard. With regard to this theoretical complexity the general problem to be solved is to achieve a target model quality Q under resources R and time T constraints. The general optimization problem is a multiobjective problem where no global optimum exists. This problem implies several optimization problems , multicriteria scheduling under resource constraints, deterministic bilevel problems and multistage stochastic optimization problems.All problems require architecture evaluation and multilevel simulation is used. We describe the overall process SADA based on these principles.

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Swarm-based evaluation of nonparametric SysML mechatronics system design

Cited by 2 publications

References 11 publications

Artificial Intelligence Capabilities for Effective Model‐Based Systems Engineering: A Vision Paper

Artificial Intelligence Capabilities for Effective Model‐Based Systems Engineering: A Vision Paper

Architecture frameworks, multiobjective optimization and multiphysics simulation: Challenges and opportunities

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