Alphaquant implemented a machine learning model to detect faulty parts in manufacturing wit AI. The AI can detect undamaged parts with 100% accuracy.
Challenge
Confronted with a difficult task in the realm of manufacturing, our client grappled with the difficult classification of parts manufactured parts. Available was a vast array of data points. The sheer volume of big data involved rendered manual classification unfeasible. Accordingly the customer needed a technically advanced solution that could navigate the complexities inherent in the dataset.
Solution
Alphaquant, armed with a deep understanding of both machine learning and manufacturing processes, engineered a solution to address the classification challenge. The core of this solution laid in the implementation of a Python-based machine learning model. At this time we designed a model to incorporate advanced techniques such as clustering, neural networks, and support vector machines.
The Python model functioned as a comprehensive framework, autonomously processing and discerning complex patterns within the dataset. Clustering algorithms allowed the model to identify inherent structures. Neural networks facilitated complex pattern recognition, and support vector machines provided the necessary classification tools. This combined use of methodologies resulted in a highly adaptive and nuanced model capable of handling the complexity of the manufacturing data.
Result
The technical prowess of Alphaquant’s machine learning model lead to a remarkable outcome for the classification of parts manufactured and detecting faulty parts in manufacturing with AI. Ultimately the model achieved an unparalleled 100% accuracy in classifying parts labeled as ‘undamaged.’ This technical feat not only underscored the model’s efficiency but also demonstrated its ability to navigate the intricacies of manufacturing data with highest precision.
Technologies
Python, Tensorflow, Scikit-Learn, Keras