Thin-film Supercapacitor Automation AssemblyTomas Gutierrez1, Glenn Saunders1 and Daryl Ludlow1

Center for Automation, Technologies and Systems, Rensselaer Polytechnic Institute

• Currently, technicians manually

assemble the super-capacitors using

a long, tedious and repetitive process

which is not “scalable.”

• For company interested in scaling-up,

automation can be a huge financial

risk without first testing the feasibility.

• This research project focuses on the

design and development of a

scalable automation process using

industrial robotics applications for use

in energy systems assembly.

Gonalez, Franco, and Peter Harrop.

"Batteries & Supercapacitors in

Consumer Electronics 2013-2023:

Forecasts, Opportunities,

Innovation." Http://www.idtechex.co

m/. N.p., n.d. Web. 27 Sept. 2015.

Recorded Time Intervals

1 Time of setup materials into

fixture

2 Time to initialize the program

3 Time it takes from start to end of

first seal pickup

4 Time to replace seal

5Time to complete cycle

The goal of this investigation was to determine the

process time and success rate out of 50 trails. The

results were used to develop statistical models from

which daily throughput could be determined using

simulations.

Areas of possible optimization

include:

1) The use camera prior to pick up;

so that self-corrections based on

original placement can be

achieved

2) Improving lighting and imaging

issues

3) Developing additional software

integration from ROS for

integration and the creation of a

closed loop automation process

Matlab’s Visual Toolbox was used to run

edge detection for each placed part. A

data set is created each time the code

is executed from the placement fixture

to gauge the distance offset and

rotation of placed parts.

• Note that the seal is difficult to

reliably see using image recognition

software.

• Not always able to detect edges and

determine the centroid of each part

which it uses to determine the offset

and direction.

• This makes it difficult to qualify

performance of the automation

procedure, as it requires manually

selecting edges ultimately

compromising reliable data

collection.

Fully implemented automation procedure concept

Time intervals taken to determine automation

cycle times.

Pickup Success Rate

Face Up

ElectrodeSeal Separator Seal

Face Down

Electrode

Success

Rate Per

Cycle

100 % 94 % 1.00 90 % 100 % 86%

-The market for portable, multifunction platform devices is the

largest and fastest growing. This market will be worth $86

billion by 2023 (idtechex.com).

-The increase in demand has required manufactures to seek

out secondary energy storage sources with higher power

density performance. Unlike batteries, supercapacitors can

deliver high power instantly and do not rely on chemical

processes for storage so they last longer.

-Thin-film supercapacitors are

an emerging niche that provide

flexible form factors with

promising characteristics which

are anticipated to revolutionize

the technology landscape.

-From a production standpoint,

to meet the consumer demand

for this new entrant a company

will need a smart, automation

process to achieve higher

throughput manufacturing

Preliminary results using a UR

robot and vacuum end-effector

afforded faster assembly times.

Results conclude that the process

can indeed be automated.

Further optimization in design

and refinement of a closed loop

automation process should

increase throughput, reduce

manufacturing costs.

Sequential placement image capture from ThorLabs DCU224C F1.4 digital camera on the left. Matlab’s Visual Toolbox was used to take the

raw images and run edge detection to determine offset and rotation of the parts as shown on the right.

Emerging Markets for supercapacitors taken from

“Batteries & Supercapacitors in Consumer Electronics

2013-2023: Forecasts, Opportunities, Innovation”

published by IDTechEx.

End effector design used for automaton process.

Introduction

Approach

Image Analysis

Future Work

ConclusionSimulationsEvaluation of Process

References

Average success rates for each component after 50 trails. The overall success of each component being

picked up and placed within the fixture was determined to be 86%. Note positional accuracy not

accounted for in the success rate.

Simulation throughputs accounting for the automation success rates. The top row

corresponds to the throughout expected to see if no automation procedure was used.

Phases of Design:

1) Design of End Effector

2) Automation Program Generation

3) Placement Accuracy Testing

4) Refinement of Automation

Procedure

5) Simulation of Daily throughput

“Setup Time” (interval 1) distribution compared with

the “Robot Cycle” (time intervals 3-5) distribution

using Minitab.

Automation Procedure

Simulate a 6 hour day so an estimation of

daily throughput could be generated using

Minitab running a Monte Carlo simulation. An

additional, Monte Carlo simulation was run in

Matlab to explore how much more productive

two technicians would be operating one

robot.