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June 3, 2024

Machine Learning Regression Task with Low-Code

Deep Dive into a Machine Learning regression task with our Solution engineer Ram.

The core of this blog is integrating machine learning into a regression task for CAD files. Machine learning is particularly useful when the relationship between inputs and outputs is complex and hard to define through conventional methods. Two fundamental tasks in machine learning are regression and classification. Regression predicts a continuous value, such as temperature, while classification categorizes data into discrete classes, such as weather conditions.

Practical Example: Regression Task

Suppose you're an engineer at a bearing company, tasked with predicting specific outcomes from certain input conditions. First, we generate a dataset by creating parametric models with varying conditions—such as part thickness and applied force—and measure the resulting stress, displacement, and mass.

Next, we extract these features and use them as inputs for our machine-learning model. Using Synera's design exploration capabilities, we generate multiple designs and their corresponding feature datasets.

After preparing the dataset, we move to the machine learning phase. Synera simplifies the process with pre-built nodes for model training and prediction. By connecting these nodes to our feature dataset, we train a regression model and use it to predict outcomes based on new input conditions.

Demo: Creating a Regression Model in Synera
  1. Generating the Dataset: We use parametric workflows to create diverse part designs and extract their geometric features.
  2. Training the Model: We preprocess the dataset, handle null values, and normalize the features. Using Synera's regression model node, we then train the model.
  3. Making Predictions: With the trained model, we predict new outputs by feeding new input conditions into the prediction node.

During the demo, questions arose about handling null values and tuning hyperparameters. While our tool aims to simplify the process, advanced users can integrate custom scripts for more control over the models and hyperparameters. For instance, users can import their own scripts and automate optimization processes within Synera.

Conclusion

Synera's low-code platform offers engineers a powerful yet accessible way to integrate machine learning into their workflows. Whether automating regression tasks or exploring advanced classification models, our tool provides the flexibility and ease of use needed to drive innovation in CAD regression.

Discover the benefits yourself – Test Synera's Low-Code platform!

Would you like to experience the benefits of Connected Engineering and our Low-Code Platform firsthand? We invite you to test Synera's Low-Code Platform 14 days for free and discover how you can make your product development more efficient and agile. Experience the future of product development and explore the possibilities that our innovative solution offers. Click here to explore the Synera Low-Code Platform and optimize your development process for your use case today.


Or get a free demo from our CEO Daniel Siegel. Every first Tuesday of the month he will take you on a guided tour of our synera software. You will discover how to automate your workflow and how to speed up your development process. There will also be a Q&A session where you can ask all your burning questions. Register here for free.

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