Improving Manufacturing Testing Steps Using Artificial Intelligence and Machine Learning Algorithms Trained on Simulated Waveforms

In the present era, Artificial Intelligence and Machine Learning are helpful tools for solving any challenges we encounter in both our personal and professional life. Artificial Intelligence and Machine Learning have made it possible to find solutions for a wide range of global problems. In this study, a special technology is presented, which allows manufacturing to obtain more accurate product testing results. A simple simulation of waveform signal data production is used in order to illustrate how to enhance decisions performed in different testing stages of the manufacturing process. Once data generation started, a failure prediction model can be constructed through the implementation of Artificial Intelligence and Machine Learning techniques. Three models have been used: Random Forest Classifier, Decision Tree Classifier, and Support Vector Classification (SVC) and those respective results are compared. This approach is suitable for any industrial context and is readily tailored to a real-world use case.