Session: 16-02-01: Poster Session: NSF Research Experience for Undergraduates (REU), NSF Posters

Paper Number: 99713

99713 - Investigation of In-Situ Compression Mechanisms for 𝜇Cp Quality Control 

In this project, we will investigate contact surface behavior and compression propagation mechanism in roll-to-roll (R2R) microcontact printing (𝜇CP) processes. The conventional practice is to measure the printed pattern geometries after printing while designing and controlling the R2R 𝜇CP process based on a rough assumption of homogeneous material and simple static mechanism model. Such a practice attempts to use a static compression model established off line for in-situ control of printed pattern, hence missing the in-situ compression mechanisms for 𝜇CP quality control. Instead of the static model, we will explore the dynamic compression mechanisms in R2R 𝜇CP processes through in situ measurement of stamp contact area and pressure while the web substrate is moving and printed. Through running various computational and analytical tests we aim to answer the questions: How will stamp and ink materials and compression parameters in the R2R 𝜇CP process affect the pattern quality (e.g. spatial variation and dimensional integrity etc.) and product performance? Will a conventional control ignoring significantly varying stamp pattern geometries and using a static model from offline measurement be sufficiently accurate for R2R 𝜇CP? These questions serve to guide us towards our overarching goal, which is to optimize the detection and limitation of physical defects and consistent quality control of varying patterns in the R2R 𝜇CP process. 

In limiting the frequency and amount of defects in the print,we expect to  get a higher output of consistent, desired and optimal results. This can maximize the quantity and quality of usable parts created using this optimized process.This work will be a significant research experience in that it will add value to these current research areas on an NSF Career research project for the visualization of the printed pattern on a moving web in a 𝜇CP process. In addition to providing valuable research contributions, the REU students will gain a more in-depth understanding of the manufacturing process control, making for better equipped future engineers.

In order to analyze this process, the REU students divided duties pertaining to developing defect detection and prevention software, simulating the printing process through modeling a single roll set up and observing the pattern variations and defects on a microscopic level using a stationed camera. For the R2R 𝜇CP process, we can segment the patterns on the web first, and then use the features of regularity and periodicity of the pattern distribution on the web to classify patterns from defects and noises. The detected defects will be input for an associate learning artificial neural network for classification. As the defect has a strong indication of specific pre-etch faulty parameters, a quick check and operation on defect-parameter on the printer can solve the problems. For example, line break indicates the imbalance among print force, ink drying flow rate and web speed. Also, through modeling components of the set up with autoCAD sketches, researchers were able to come up with theoretical values for the printing process in order to make sure the actual experiments didn’t stray too far from what was expected with the current dimensions of the single R2R set up. 

As a result of extensive analysis of the R2R 𝜇CP process, researchers will be able to identify types of defects and their causes and come up with ways to correct these causes. In the end this will make for a more efficient printing process that can be easily replicated and used to further improve the R2R 𝜇CP process in order to make better parts for the advancement of technology. Researchers will gain significant experience in understanding machine learning tools and manufacturing processes control, resulting in better preparation for future engineering projects.

 

Presenting Author: Bella Lambros University Of Massachusetts Amherst

Presenting Author Biography: Isabella Lambros is a student at the University of Massachusetts Amherst working towards her B.S in mechanical engineering. She prides herself on her dedicated work ethic and enthusiasm for learning and has made the deans list for all four semesters at UMass Amherst. She enjoys working with computer aided design software both professionally and creatively. Rock climbing and drawing are some of her favorite pass times.

Authors:

Bella Lambros University Of Massachusetts Amherst
Ilya Mccune-Pedit University of Massachusetts Amherst
Xian Du University of Massachusetts Amherst