Overview / Usage

E-waste is one of the major concerns as of today, and them being a major concern makes it a necessity for its decomposition and recycling more important. We propose an idea to take analysis data of the current situation of the E waste being generated and all its sources and other factors. This data will be analyzed to choose what is to be done with each of the E-waste recovered, location of where the E-waste needs to be monitored and optimization of the recovery methods. Everyone's heard about the three R's of recycling, that is, Reduce, Reuse and Recycle but which is best suited for all the different types of E-waste making it more economical for a world to come.

In this project, we will compare the initial manufacturer's image of the new PCB and compare it with the actual image of the PCB. Thus it will let us know if the PCB is damaged or it is still usable.

In order to for the user to utilize our project, we first have to upload a picture of the printed circuit board to the prediction algorithm. That image would be compared with the printed circuit board image that was first captured when the PCB was manufactured using the model number. This way, we can identify where the user's PCB has damage, and an external source will suggest whether or not to dispose of the PCB.

Methodology / Approach

Detecting defects in printed circuit boards (PCBs) is an important part of the manufacturing process. Predictive analysis is a powerful tool that can be used to identify potential defects before they occur. Here is a methodology for using predictive analysis for PCB defect detection:

1. Data Collection: The first step is to collect data on the PCB manufacturing process. This includes data on the raw materials used, the manufacturing process, and the quality control measures in place.
2. Feature Selection: Next, you need to identify the key features or variables that are most likely to influence the occurrence of defects. These could include factors such as temperature, humidity, voltage, and current.
3. Data Preprocessing: Once you have identified the relevant features, you need to preprocess the data to remove any noise or outliers. This can include techniques such as smoothing, normalization, and outlier detection.
4. Model Selection: There are a variety of predictive modeling techniques that can be used for PCB defect detection, including decision trees, logistic regression, and neural networks. Choose the best model based on your data characteristics and the type of defects you are looking to detect.
5. Model Training: Once you have selected a model, you need to train it using your preprocessed data. This involves splitting your data into training and testing sets, and using the training data to optimize the model parameters.
6. Model Evaluation: After training the model, you need to evaluate its performance using the testing data. This will give you an idea of how well the model is able to predict defects.
7. Defect Detection: Finally, you can use the trained model to predict defects in real-time. This involves feeding in new data from the manufacturing process and using the model to identify potential defects before they occur.

Overall, using predictive analysis for PCB defect detection can help to improve the efficiency and reliability of the manufacturing process, reducing the likelihood of costly defects and improving product quality.

Technologies Used

Front-end

1. HTML
2. CSS
3. JavaScript

Back-end

1. Python

Repository

https://github.com/loneclawtiger/PCB

Collaborators

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Twinkle Gupta

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Tyrell Fernandes

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