Real-time Defect Prediction for Injection Molding Process 

This system uses ensemble learning techniques to predict potential defects in real-time, reducing waste and inefficiency, and leading to substantial improvements in the manufacturing process. 

Backend Development

The backbone of this system is developed using Django, a high-level Python Web framework that supports rapid development and clean, pragmatic design. The robust and scalable architecture of Django allowed for the seamless integration of the machine learning component. This system continuously collects process data and feeds it into the machine learning model to make immediate predictions about potential defects.

Frontend Development

The frontend was designed using HTML, CSS, and JavaScript to deliver an interactive and user-friendly interface. This interface effectively presents real-time data and prediction results in an accessible format, enabling our team to make swift and informed decisions.

Machine Learning and Data Analysis

The core of the predictive system is an ensemble learning algorithm developed in Python. This model integrates various machine learning algorithms, enabling accurate and reliable predictions. The ensemble approach enhances the model's stability and robustness, assuring it performs optimally under diverse conditions.

Detailed data analysis was carried out to identify key features that significantly influence the likelihood of defects in the injection molding process. This informed the training of the ensemble model, augmenting its predictive accuracy and effectiveness.

Describe what role you played on your team. Who did you have to work with to achieve your goal? What was the outcome of the project?