How can I make a electroplating rate algorithm ?

Hello , I have a problem with making an electroplating algorithm cause dont know where I can start from. I mean when doing electroplating the copper in brass there are multiple factors like pH , copper concentration in bath , etc., but all this so that it gives different outputs like more corrugation or a matte look in the already electroplated brass the thing is I want to calculate the difference between this flawed outcome with an ideal pattern and give it a rate from 0 to 10. I want to know which tools from matlab can I use and how can I approach this problem ,another issue is that I dont have a large dataset to train something like a deep learning network. I give some images to better depicting the idea. Thanks

Here is the alleged ideal pattern from which the rest of the samples are compared.

Here there is a strained plate caused by the effect of few acid but with a more shiny surface

Heres a matte surface plate caused by lack of surfactant but with less straining

Thnaks a lot for the help.

Answer 1

[Hassaan]

I'll guide you through a general approach and highlight MATLAB tools and functions that might be useful:

1. Define Quality Metrics:

• Identify Parameters: Determine which factors (e.g., pH, copper concentration) affect quality.
• Quality Scale: Define what a 0-10 rating means for your brass electroplating quality.

2. Collect and Preprocess Data:

• Gather Images: Collect images of electroplated brass with various known quality levels.
• Record Conditions: Document the electroplating conditions (pH, concentration) for each sample.
• Image Preprocessing: Use MATLAB's Image Processing Toolbox to standardize and enhance images.

3. Feature Extraction:

• Texture Analysis: Extract texture features from images (MATLAB's graycomatrix, graycoprops).
• Statistical Features: Calculate statistical measures from both images and electroplating conditions.

4. Choose a Modeling Approach:

• Simple Models: Start with simpler models like linear regression (fitlm) if the dataset is small.
• Machine Learning: Consider supervised learning techniques if you have enough data (decision trees, SVM).

5. Develop the Algorithm:

• Coding: Use MATLAB to implement the chosen model, incorporating both image features and electroplating conditions.
• Testing: Test the algorithm on a subset of your data to evaluate its initial performance.

6. Validate and Refine:

• Cross-Validation: Perform k-fold cross-validation to assess the model's generalizability.
• Adjust Model: Based on validation results, adjust your model and features as needed.

7. Rate Quality:

• Scoring Function: Develop a function that inputs the conditions and image, and outputs a 0-10 quality rating.
• User Feedback: If possible, incorporate user feedback to adjust and improve the rating scale.

8. Documentation:

• Code Documentation: Clearly document each part of the code for future reference and modifications.
• Process Documentation: Record your approach, considerations, and modifications for ongoing improvement.

9. Interface (Optional):

• GUI Development: If desired, use MATLAB's App Designer to create a user interface for easier interaction with your tool.

10. Continuous Improvement:

• Iterate: Continuously refine the model with new data and feedback.

By following these steps, you can create a structured approach to developing your electroplating quality assessment tool in MATLAB.

------------------------------------------------------------------------------------------------------------------------------------------------

If you find the solution helpful and it resolves your issue, it would be greatly appreciated if you could accept the answer. Also, leaving an upvote and a comment are also wonderful ways to provide feedback.