Who
US-based manufacturer specialising in high-performance polymer extrusion and custom tubing solutions, serving medical, aerospace, automotive, and industrial sectors.
Case Study
Written by
Abhimanyu Bhandankar
Holds an MBA from Schulich School of Business and a BE in IT. He joined Droplet Lab in July 2019 and now leads sales and marketing.
CEO at Droplet Lab
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Technical Review by
Gurdeep Singh Saini
Holds a BASc in Mechanical Engineering (Ryerson) and an MASc from York University. He focuses on the custom AI behind the instrument.
COO at Droplet Lab
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Executive Summary
Problem
Their analog contact angle tool, a manual protractor; introduced 5–10° of operator-dependent variance in surface energy readings, risking downstream adhesion failures and eroding partner confidence in QC documentation.
Solution
Droplet Lab's smartphone-based goniometer system, validated against KRÜSS DSA100E reference measurements and benchmarked in peer-reviewed studies.
Time to Value
4 months from first contact to continuous 24/7 production use.
Results
Variance reduced from ±5–10° to within 0.01% error. Five instruments deployed; Gage R&R study completed and passed. Factory staff now consistently produce lab-grade data.
Client Snapshot
Industry
Polymer extrusion / custom tubing manufacturing
Products / Applications
Medical, aerospace, automotive, and industrial sectors
QC Stage (Incoming / In-Process / Final)
In-process inspection
Users (Roles / Shifts)
Factory floor inspectors, running 24/7 shifts across multiple plants, with no advanced training in optics or surface science.
Materials / Surfaces Tested
Polymer tubing extrusions
Key Constraints
Minimal workflow disruption; 24/7 operability; high repeatability; multi-facility scalability
The Challenge
How They Were Working Before
In one of the company's plants, contact angle measurement, the primary method for assessing surface energy on tubing was performed with an analog protractor. Inspectors manually aligned the midpoint of a sessile water droplet to estimate the angle by eye. No digital capture. No audit trail. Results depended entirely on the individual reading the scale.
Pain points
- Manual alignment introduced consistent human error; no two operators produced identical readings.
- Repeatability was insufficient for a high-stakes manufacturing environment; variance ran to ±5–10°.
- That margin was not academically inconvenient; it was operationally dangerous. At those error levels, adhesion between materials becomes unpredictable.
- QC documentation shared with partners reflected that uncertainty, eroding confidence in the manufacturer's process integrity.
What Was at Stake
A 5–10° measurement error in contact angle is not a rounding issue; it is a material behaviour question. Poor surface energy readings translate directly into adhesion failures, compromised tubing functionality, and QC rejections that ripple into the downstream processes of the medical and aerospace customers depending on consistent product specification. Beyond the production floor, every QC report sent to a partner carried the implicit uncertainty of a method that could not be independently verified. The company needed a way to eliminate that uncertainty without restructuring who does the testing.
What Success Would Require
- Sub-degree measurement accuracy achievable in a production environment, not just a lab.
- Operability by factory floor staff, not limited to trained technicians.
- Compatibility with existing data workflows, including Oracle ERP integration.
- Scalability across multiple facilities without custom re-engineering at each site.
The Solution
What Was Deployed
Droplet Lab's smartphone-based goniometer system, using the Young-Laplace method for contact angle measurement. The system's measurement approach has been benchmarked against KRÜSS DSA100E and cited in peer-reviewed publications. A custom tube holder was engineered by Droplet Lab's hardware team to accommodate the client's specific tubing geometry.
Before vs After
Implementation
Rollout Timeline (high level)
Week 0: Discovery
Client contacted Droplet Lab via website; multiple tubing samples shipped for baseline assessment and method comparison against existing analog process.
Week 1-2: Validation
- Virtual demonstration of system architecture and measurement workflow
- Client shipped multiple tubing samples representing production variants
- Droplet Lab tested all samples using the smartphone-based goniometer with Young-Laplace method
- Side-by-side comparative analysis conducted: Droplet Lab system vs. client's existing analog protractor method on identical samples
- Results and accuracy comparison data returned to client QA team for review
- Custom tube holder scoped and designed by Droplet Lab hardware engineer based on sample geometry
Week 3-4: Deployment
Five instruments purchased and delivered to primary facility; custom tube holder shipped; factory staff onboarded on test workflow and handling procedures.
Month 2+: Active iteration
24/7 production use generates continuous feedback; ML model retraining, lighting redesign, and ERP integration all initiated based on real-world data from this site.
Proof / Validation
Test method
Young Laplace Method
Sample size and operators
100+ samples across 3 operators
Repeatability / reproducibility
Gage R&R study completed
Results
Measured Outcomes
Precision improvement
From ±5–10° (manual estimation) to within 0.01° error rate; a reduction in variance of at least two orders of magnitude
Gage R&R
Study completed across 3 operators; instrument passed
Deployment scale
5 instruments at one facility; continuous 24/7 use for 3+ months with no operational downtime reported
Operational Outcomes
Factory floor inspectors not lab technicians now consistently produce lab-grade surface energy data as part of standard in-process QC.
The client can provide partners with detailed, traceable, instrument-grade QC documentation rather than operator-estimated readings; a material shift in the credibility of their QC reports.
Continuous production use at scale is generating real-world data that directly informs ML retraining, hardware refinement, and the ERP integration roadmap; making this site an active development partner, not just a customer.
Client Quote
"Overall, we've been very impressed. It's been working really well and we really have enjoyed using it. It's a bit different than what we're currently using. It's much more technologically advanced than how we currently do it, which is good. Also, I wanted to let you know we completed our gage R&R study on the unit we have and it performed very well. We’ve been happy with the machine learning add in too, it saves a lot of time on measurements."— Corporate Quality Engineer, Development
What's Next
Delivered
5 smartphone-based goniometers deployed at primary facility
Custom tube holder designed and delivered
Gage R&R study completed and passed
Continuous 24/7 production use established
In Pilot
ML baseline detection retraining on client's real-world image sets
Lighting redesign with a brighter LED module, mount elevated 2cm to reduce reflection interference
Splitters supplied for simultaneous charging and mouse use
Planned
Desktop interface (medium-term) removing phone dependency from the workflow
Oracle ERP API integration to eliminate manual data entry by inspectors
Client fabrication of collapsible enclosures using Droplet Lab open-source designs and their own machine shop
Longer-term migration toward RAW cameras, bypassing phones entirely