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Novineer

Novineer provides generative design technology that creates optimized CAD models with editable geometry, enhancing performance through strength-based and stiffness-based optimization using anisotropic material properties. Their cloud-based high-performance computing accelerates design and simulation workflows, addressing inefficiencies in traditional engineering processes.

Daytona Beach, United StatesFounded 20225300+ followers
Updated 4 months ago

Funding

$280K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

Funding rounds are not available yet.

Founders

Product

Problem

Traditional CAD model design processes are inefficient due to manual iterations and limited optimization for performance and manufacturability. Existing generative design tools often produce models that lack editable geometry and do not fully leverage anisotropic material properties for enhanced strength and stiffness.

Solution

Novineer offers generative design technology that automates the creation of optimized CAD models with editable geometry, addressing the limitations of conventional design workflows. The software employs strength-based and stiffness-based optimization techniques, utilizing anisotropic material properties from the initial design phase to maximize structural performance. By integrating design and toolpath automation, Novineer ensures manufacturability while respecting component-specific constraints such as load and weight limitations. The platform leverages cloud-based high-performance computing to accelerate design and simulation, enabling parallel processing and efficient results.

Target Audience

Novineer targets engineers and designers seeking to enhance the performance and manufacturability of their CAD models through generative design and optimization.

Features

  • Generates optimized CAD models with topologically manageable and editable features.
  • Employs strength-based optimization using the Tsai-Wu failure criteria.
  • Optimizes material orientation to leverage anisotropic properties for efficient load distribution.
  • Integrates design of geometric layouts with toolpath generation for manufacturability.
  • Utilizes cloud high-performance computing for parallel design and simulation.
  • Incorporates design domain decomposition functions to speed up workflows.
This profile is AI-generated and may contain inaccuracies.