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Remi explains that in such a scenario AI can today track correlations between many machines and identify patterns in the industrial data which can potentially lead to overall improved efficiency. There were 20, real-time telemetry variables collected, and by correlating them and combining them with production data, the AI was able to learn which combinations and variance led to the rejection rate.
What this means in real business terms is that the manufacturer used fewer raw materials, had a lot less rework hours, which essentially translated to saving of half a million dollars per production line per year. Use Case: Fleet Management Another such use-case that Remi explored was in fleet management or freight operations in the logistics sector where maintaining high fuel efficiency for vehicles like trucks or ships is essential for cost-savings in the long run.
Various patterns identified by AI from data, like weather or temperature conditions, operational usage, mileage, etc can potentially be used to pick out anomalies and recommend what the best route or speed would be in order to operate at the highest efficiency levels. Remi agreed that no matter the mode of transportation used trucks or ships, for example , some of the more common emerging use-cases for AI in logistics are around overall fuel consumption analysis for fleets.
Use Case: Engineering Simulation The last use-case that Remi touched on was 3D engineering simulations for design in the field of computational fluid dynamics. Traditional simulation software can be fed input criteria like flow rates of fluids and can generate a 3D simulation which usually takes around 15 minutes to be rendered and involves a long, iterative, and often manual process. Maya HTT claims that this improvement of over x in efficiency gains from 15 minutes to 1 second during the design process enabled design teams to explore far more design variants than previously possible.
Phases of Implementation for Industrial AI When asked about what business leaders in the manufacturing sector interested in adopting AI would need to do, Remi outlines a 5 step approach used at Maya HTT: The first step would be a business assessment to make sure any target AI project will eventually lead to significant business impact. Next businesses would need to understand data source access and manipulations that may be needed in order to train and configure the AI platform.
This may also involve restructuring the way data is collected in order to enhance compatibility. The actual creation and optimization of an AI application. Training and conclusions from AI need to be properly explained and understood. Simcenter Femap delivers just that high-performance FEA modelling for the engineering department. Simcenter Femap is one of the world's leading CAD-independent Windows native pre- and post-processors for advanced finite element analysis.
Try Simcenter Femap for free! Structural Analysis Simcenter Femap is an advanced simulation application for creating, editing and inspecting finite element models of complex products or systems. You can use advanced workflows in Simcenter Femap to create models of components, assemblies or systems and then determine the behaviour of a model when it's subjected to real-world conditions.
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