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AI Copilot for aerospace MRO
and manufacturing
Basetwo provides a complete process engineering solution that integrates data pipelining, model simulation, economic optimization, and quality control into a single low-code platform.
See our platform.png)
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Assembly Line Optimization
Basetwo can be applied for scheduling and scenario analyses in aerospace assembly lines:
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Reduce the overall cycle time of the assembly line (i.e. AFAL) via balancing
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Develop an understanding of factors that lead to non-conformance events and delays
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Operationalize adaptive task and resource scheduling at workstations in response to supplier delays
MRO 4.0
Aircraft Equipment Prognostics
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Provide AI generated maintenance recommendations to extend the lifecycle and utilization rates for aircraft and UAV fleets (platform/OEM agnostic)
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Applies to a wide range of components such as engines, landing gear, ECS etc.
Supply chain and part forecasting
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Use AI to forecast spare part demand and optimize inventory in order to minimize AOG times and maximize utilization rates
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From additive manufacturing to composite fabrication, aerospace manufacturers rely on the fundamentals of material science and engineering to achieve consistent product quality. However, manufacturing operations are often complex, making optimization challenging:
Heterogeneous material quality
Complex multi-step reaction dynamics
Noisy or sparse sensor information
Additive Manufacturing
AM processes (powder bed fusion, laser metal deposition) can be complex and time consuming to achieve thermally stable process conditions and defect-free production.
With Basetwo, companies can connect to several data sources and leverage ML to simulate process parameters impacting geometry and porosity and mitigate conditions leading to irregularities, discontinuities, and distortion.
This ultimately enables AM engineers to improve buy-to-fly ratios, reduce lead times for qualification, and improve material properties.
Advanced Composite Fabrication
Control and optimization of composite fabrication processes is known to be challenging. The temperature response during curing is dependent on many process variables including resin kinetics, carbon thickness and physical properties, tooling thickness, and autoclave properties.
With Basetwo, manufacturers can accurately predict material properties as a function of composition and heat treatment to meet design specifications, as well as simulate and optimize processes like AFP/ATL, autoclave curing, and forming. Ultimately, manufacturers can expect to optimize Carbon Fibre Reinforced Plastic (CFRP) manufacturing cycle time by 20+%.
See how Basetwo can deliver:
of use cases now
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