Process Optimization and Cost Analysis of Electrochemical
Micromachining for Volume Manufacturing
Name
li-mingyuan-mli97-ME&EECS-2024-thesis.pdf
Description
Thesis PDF
Size
7.21 MB
Format
Adobe PDF
Checksum (MD5)
453823c198379e6582a4c7c969ce20a9
Author(s)
Li, Mingyuan
Advisor(s)
Hardt, David E.
Boning, Duane S.
Date Issued
February 2024
Publisher
Massachusetts Institute of Technology
Abstract
Microfluidic devices have found numerous applications in the medical device, pharmaceutical, and healthcare industries, resulting in an increasing demand for different types of microfluidic devices in various designs and materials, including metals such as stainless steel and titanium. Electrochemical micromachining (ECMM) is a powerful method for manufacturing small channels (~0.1mm) on metal substrates which can be later made into metal microfluidic devices, but so far, it has only been studied in benchtop experiments. Scaling up this process to volume manufacturing is relatively unexplored. In this study, we examine the financial and performance benefits of ECMM in an industrial setting for manufacturing microfluidic devices. We conduct cost analysis and performance comparisons of ECMM and micro-milling, an alternative technology for making micro channels. Our findings demonstrate that channels manufactured using ECMM have less variation in the total volume of material removed when compared to micro-milling. However, the cost of ECMM is currently around 50% higher than micro-milling for the fluidic device analyzed here. By making a few simple design changes and optimizing the ECMM process, we will be able to achieve a >20% cost saving compared to micro-milling. The second part of our study focuses on optimizing the ECMM process in terms of cycle time. The bottleneck for the entire process is the time for photoresist removal. By changing the solvent, agitation method, and hard baking time, we reduce the stripping time from hours or even days to just ~60 minutes, with a standard deviation of ~2.7 minutes, drastically reducing the mean and variation. Furthermore, our investigation finds a correlation between surface roughness and stripping time, which should be further controlled in the manufacturing process in the future.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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