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​Applying artificial intelligence in machinery

Operations and processing by smart mobile machinery can gain higher level of autonomy by employing AI technologies for environmental sensing and model construction, contextual situational awareness and planning of operations and interactions with the environment. AI technologies also help analyse and optimise the utilization of the individual machines and machine fleets by creating insights and actionable intelligence from the data produced during operations. When the operations and conditions are not understood well enough for explicit model building, inference and planning, machine learning techniques can often be used to increase the autonomy of the machines. Fulfilling the safety requirements especially in collaborative operations with humans mandates the use of AI techniques which are dependable and suitable for machine safety assessment.

AI in simulation based engineering design

Simulation based engineering design offers new possibilities to make the product design process more efficient. Ever increasing efficiency and speed-up needs for developing competitive new products to international markets challenges current product development processes, new methods should be taken into use. The design of new products and also the improvement of existing products includes always uncertainties which are difficult to master effectively with traditional methods. AI offers new possibilities to simulation based engineering design as well as to virtual testing. Usually, there exists plenty of gathered data from previous design projects and also from measured field data from current product usage. Especially, the development of new materials leads to new sophisticated and non-linear structural components, which are difficult to model in detail due to lacking input data. For these needs, AI-methods can connect measured and modelled information, and use the information as training data for AI-based processes. Consequently, AI can make the decision making process faster and more reliable. As a result, the costly product prototype experiments can be replaced with more comprehensive virtual prototype models and simulations.
In engineering design, pattern recognition (PR), machine learning (ML) and deep learning (DL) techniques can be used to make the design process more effective and reliable. By using an efficient and automated calculation process together with AI-methods during a design process decision making, it is possible to reach high expectations and to match requirements of new competitive products. In addition, using the modular simulation environment, the process execution can be easily adjusted for individual industrial design needs. AI-methods utilized in certain design phases, enable efficient, simplified, more reliable and faster use of design, simulation and monitoring models in mechanical engineering disciplines.



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