CODING

PROJECTS

Increase Additive Manufacturing Yield

Project Goal

Increase the yield of metal 3D printed components by correlating multiple data sets in the Additive Manufacturing industry utilizing Machine Learning. By understanding the correlation between the three data sets, the printing process can be optimized for fully dense parts which is key to strong and reliable components.

Data Sets

  • Print Parameters: Laser Power, Print Speed, etc.

  • Melt Pool Metrics: ​Temperature, Area, etc.

  • Material Properties: ​Porosity

Additive Manufacturing Report

I created this project to capture repeatable data and generate a formatted report. This would be beneficial if you needed to review a weekly report with your team. The set up is fairly simple. Open up word, create a formatted document with titles, tables, headers, etc. Next, the template needs to be filled in with variables.

There are single variables like "company", "department", and "phone". There are also table variables like "Part Overview". The neat thing about this table is that whether there is one row of 100 rows, it expands to fit the data.

The python code includes the values for the single variables. The tabular data is contained with Pandas Dataframes. This way the data can be formatted per column. Once the data is created, the Python library DocxTemplate is used to combine the data and the template and export it into a word document.

Version 2 of this project would be to include plots like a first pass yield metric, tracked over multiple weeks to see progress. The plots could be generated by utilizing MatPlotLib or Seaborn libraries and saved as PNGs.

MuscleHub A/B Test

MuscleHub AB Test Presentation (Reviewed).pdf