Validate prototypes faster with AI.
“What took 18 months was reduced by 25% using Artificial Intelligence.”
“Accurately predict without doing physical testing.”
“Improve vehicle dynamics and reduce testing by 72%.”
Four ways to use AI to cut validation costs
Build shorter test plans, create fewer prototypes, find errors faster, and validate designs more quickly using AI-based self-learning models.
1. Automatically detect faulty sensors
To avoid downstream issues, your engineers must tediously inspect test data for errors. One failed sensor or wiring malfunction can render thousands of dollars in testing useless.
- Prepare your data for optimal AI performance
- Catch bad data now so you avoid re-engineering later
- Find outliers in your test data fast with intuitive visualisation
2. Predict the critical tests to run
Test too much and you waste time confirming what you already know. Test too little and risk missing performance issues. Schedule, quality and your career depend on finding the balance.
- Run the most important tests and skip the rest
- Optimize resources spent on costly test rigs and facilities
- Validate your designs faster with fewer prototype iterations
3. Determine the cause of system failure
Product design issues during validation risk launch delays and lost market share. Pressure on engineers is high to identify critical parameters causing failure, quickly analyze the root cause, and predict how the product will perform in changing conditions.
- Predict what design changes will most likely fix the failure
- Identify components causing sub-optimal performance
- Avoid long delays and uncertainty in the validation process
4. Calibrate for thousands of conditions
Designing highly complex, non-linear systems that must meet stringent performance standards is challenging. Predicting which combination of inputs will deliver the optimal output, in all operating conditions, is next to impossible.
- Calibrate complex dynamic systems
- Ensure your system performs to spec in all conditions
- Find best-fit values across 1000's of inputs and conditions
Use AI to predict fuel-sloshing noise, solve intractable physics
- Problem: Vehicle acoustics
- Methods tried: CFD, physics-based simulation
- Solution: Predict noise, reduce testing with self-learning models
A commissioned study conducted by Forrester Consulting on behalf of Monolith
The State of AI in Engineering
First-ever study on AI in product development surveys US and European automotive, aerospace and industrial engineering leaders.
"If your model is in your data, Monolith will find it. Built by engineers for engineers, Monolith helps you make better models faster."
Dr. Ted Duclos, Monolith Advisor and Former CTO at Freudenberg Sealing Technologies
My engineers are busy. Why risk trying Monolith over another AI tool?
Monolith is the only AI platform built by engineers for engineers and trusted by the world’s top engineering teams including BMW, Honeywell and BAE. Its advanced capabilities analyse complex engineering data sets quickly and accurately to help you find relationships, new insights and anomalies so you can trust your data fast. With its user-friendly, no-code interface, domain experts can quickly get up to speed and leverage institutional knowledge for future generations.
How is Monolith better than current statistical and simulation methods?
There are five ways Monolith delivers superior performance to statistical and simulation methods.
- Increased accuracy: advanced machine learning algorithms identify complex patterns and relationships in engineering datasets for more accurate predictions and insights
- Faster analysis: algorithms are optimised for speed, allowing it to analyse large datasets much more quickly
- More comprehensive analysis: analyse both structured and unstructured data
- Adaptive learning: continually refine and improve the accuracy of predictions
- Ease of use: no code environment built by engineers for engineers
What if I don't have alot of data or I'm modeling a new product?
Most often, you need less data than you think! 100s of rows and a handful of columns for a tabular data approach can already get you going on your journey to adopt AI. If you don't have enough data for an AI project, we recommend the following:
- Data augmentation: Create new training examples from existing data by applying transformations or scaling to existing data (this is how our customer Kautex Textron did it).
- Transfer learning: Use and reuse data gathered from an old product to improve a new product.
- Consider a simpler model: A simpler model requires less data to train. The best approach will depend on the specific task and constraints of your project.
Our mantra: Start small - but start! Find a small, incremental quick win, and let our AI team of experts guide you on your way forward to intelligently acquire more data and build even more impactful models.
How will having a model in Monolith help me reduce design cycles?
Monolith reduces design cycles by offering rapid analysis, accurate predictions, custom models, collaboration, and optimization capabilities. Its algorithms are optimized for speed, enabling quick iteration and testing of multiple scenarios. Custom models capture domain-specific knowledge, while collaboration and optimization capabilities improve effectiveness and identify the best design options. By leveraging Monolith's capabilities, teams can deliver better designs more efficiently.
Does my team need to start from scratch once an AI model is built?
Monolith's self-learning models automate repetitive or time-consuming tasks, allowing teams to focus on more complex work. However, data quality, model drift, and interpretation are critical considerations, all supported by Monolith. Teams may need to update or retrain models to account for changes and understand their decision-making processes. By monitoring and managing these factors, teams can ensure ongoing value from Monolith's AI models.
"Using Monolith AI platform, we were able to import our rich test stand data and apply machine learning models to conclude on different development options much faster. "
Dr. Bas Kastelein, Sr. Director of Product Innovation, Honeywell Process Solutions
No code software
AI built by engineers for engineers
- Avoid wasted tests due to faulty data
- Build just the right test plan - no more, no less
- Understand what drives product performance and failure
- Calibrate non-linear systems for any condition