Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
NeuralMould, developed by Emmi AI, is an advanced Large Engineering Model specifically designed for injection molding, setting a new benchmark in AI-driven engineering solutions by accommodating any geometry, material, and injection gate configuration within a single framework. Users can easily choose from various geometries while testing different parameters related to injection, materials, and gate placement, allowing for quick simulations of filling behavior, rapid scenario comparisons, optimization of key performance indicators, and the prevention of frozen flow fronts. The complexity of injection molding simulations arises from the necessity to conduct multi-physics calculations, which accurately model the transient flow of viscous plastics through intricately designed thin-walled shapes under high-pressure and high-temperature conditions. NeuralMould effectively captures these critical phenomena across diverse injection scenarios and mold designs, achieving results that rival traditional solvers but with significantly reduced computation times. Additionally, the model is capable of handling multi-material applications, facilitating quick prototyping, accommodating multi-gate setups, and managing a variety of processing parameters thanks to its scalable transformer-based architecture. This innovative approach uniquely positions NeuralMould as a vital tool for engineers seeking to enhance efficiency and precision in the injection molding process.
Description
Utilizing a local density map, REM3D® delivers dependable predictions regarding the resistance of components along with their insulating, noise, and comfort characteristics. By simulating a ‘dual foam’ pouring process, one can observe the transitional areas between foams with varying rigidities. Incorporating "mold tilting" into the simulation replicates realistic process conditions, ensuring they are as accurate as possible. The inclusion of features like automatic mold tilting and the influence of gravity on melt flow enables an analysis of genuine process conditions, thereby ensuring uniformity in your components. Additionally, investigating the placement of injectors minimizes the occurrence of defects. Consequently, you gain trustworthy forecasts related to not only the durability of your components but also their insulating and comfort attributes. For fiber-reinforced plastics, REM3D® also assesses the orientation of the fibers throughout the filling phase and after the cooling process has completed. This comprehensive analysis enhances the overall quality of the final products.
API Access
Has API
API Access
Has API
Integrations
No details available.
Integrations
No details available.
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Emmi AI
Country
Austria
Website
www.emmi.ai/models/neuralmould
Vendor Details
Company Name
TRANSVALOR
Founded
1984
Country
France
Website
www.transvalor.com/en/rem3d
Product Features
Engineering
2D Drawing
3D Modeling
Chemical Engineering
Civil Engineering
Collaboration
Design Analysis
Design Export
Document Management
Electrical Engineering
Mechanical Engineering
Mechatronics
Presentation Tools
Structural Engineering
Product Features
Simulation
1D Simulation
3D Modeling
3D Simulation
Agent-Based Modeling
Continuous Modeling
Design Analysis
Direct Manipulation
Discrete Event Modeling
Dynamic Modeling
Graphical Modeling
Industry Specific Database
Monte Carlo Simulation
Motion Modeling
Presentation Tools
Stochastic Modeling
Turbulence Modeling
Alternatives
No Alternatives