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Pages
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Posts
Belief test January 2023: What if I am wrong about……planning?
Published:
I spent a lot of time in 2022 looking at the world and feeling down about how polarizing everything and everyone seem. I took a class that had interesting content around that. The content modules, called ‘perspectives’ talked about rational and irrational thoughts (The elephant and the rider metaphor), confirmation bias and moral foundation theory. It made reflect about how I too have some strong opinions and having them questioned makes my palms sweat and I feel a rush of blood.
Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning: A summary
Published:
This article is a quick summary of an article published by Smoqi et al, in the Journal of Materials Processing Technology.
MeltpoolNet: Melt pool characteristic prediction in Metal Additive Manufacturing using machine learning: A summary
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Introduction:
Crack-free metal printing using physics based machine learning: A summary
Published:
This article is a quick summary of a publication in Acta Materialia by Mondal et al. There is more detail than the abstract, and less detail that the full paper.
portfolio
Using Data Science to Analyze the printability of novel copper powder
I’m currently working on building a dataset of novel copper print data to predict factors that play a crucial role in its printability. Copper alloys are some of the most challenging materials to print using Laser Powder Bed Fusion, owing to their high reflectivity. I’m attempting collect sensor and machine settings data and build tree based classifiers that can help identify successful print zones
publications
Prediction of Material Removal Rate in Die-Sinking Electrical Discharge Machining
Published in Procedia Manufacturing, 2016
This paper presents a semi-empirical model which shows the relationship between material removal rate in die-sinking Electrical Discharge Machining and peak current and on-time. Peak current and on-time are considered as two critical factors that affect the material removal rate.
Recommended citation: Prediction of material removal rate in die-sinking electrical discharge machining NSLB Izwan, Z Feng, JB Patel, WN Hung - Procedia Manufacturing, 2016 http://jigarp12892.github.io/files/paper2.pdf
Quality Enhancement with Ultrasonic Wave and Pulsed Current in Electrochemical Machining
Published in Procedia Manufacturing, 2017
This paper explores a novel experimental set-up that combines pulsed electrochemical machining with ultrasonic vibrations directly applied to electrolyte
Recommended citation: Quality enhancement with ultrasonic wave and pulsed current in electrochemical machining JB Patel, Z Feng, PP Villanueva, WNP Hung - Procedia Manufacturing, 2017 http://jigarp12892.github.io/files/paper1.pdf
talks
Trends in Manufacturing
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Challenges in Physics based Machine Learning in Laser Powder Bed Fusion
Published:
In this talk I spoke about the benefits of introducing physics informed features in machine learning tasks that improve additive manufacturing.
teaching
MMET 181-Manufacturing and Assembly Processes
Workshop, Texas A&M University, Engineering Technology and Industrial Distribution, 2015
I taught in-person manufacturing labs and graded reports and tests for more than 200 students across 4 semesters. Labs skills included basic welding, machining, casting, metrology and CNC machining.
ME 561 - Fluid Power Control Systems
Upper Undergraduate course, University of Waterloo, Mechanical and Mechatronics Engineering, 2022
Grading TA for a class of 40 students