Making Sense of the Noise

Slides:



Advertisements
Similar presentations
Tales from the Lab: Experiences and Methodology Demand Technology User Group December 5, 2005 Ellen Friedman SRM Associates, Ltd.
Advertisements

Michal Kašpárek Technical University in Liberec Faculty of Mechanical Engineering Department of Applied Cybernetics
5387 Avion Park Drive Highland Heights, Ohio INTUNE v4.4 Demonstration.
Gage R&R Estimating measurement components
CSUN Engineering Management Six Sigma Quality Engineering Week 11 Improve Phase.
SSRS 2008 Architecture Improvements Scale-out SSRS 2008 Report Engine Scalability Improvements.
Tissue World conference, Istanbul, , Vesterinen/controlEYE 1 THE WAY TO LOOK.
ENERGY SAVING Product Offering COMPRESSOR MARKETING OPPORTUNITIES.
P10505 – Cold Pressure Fusing II Performance Review Team Fusion 5/7/2010.
Bob Travica Class 10 Decision Making Processes, System Support & Decision Support System MIS 2000 Instructor: Bob Travica Updated February of 14.
Case Studies of Batch Processing Experiments Diane K. Michelson International Sematech Statistical Methods May 21, 2003 Quality and Productivity Research.
Tension Control Upgrade
CHE 185 – PROCESS CONTROL AND DYNAMICS
De Constant Speed Power Take Off Improving performance and fuel-efficiency, using a CVT for driving auxiliary equipment on distribution trucks Daniël de.
1 Adaptive Tightening Control Adaptive Tightening Control (ATC)* automatically adjusts for different joint designs and part variations.
System Design and Analysis
Enhancing Paper Mill Performance through Advanced Diagnostics Services Dr. BS Babji Process Automation Division ABB India Ltd, Bangalore IPPTA Zonal Seminar.
SDLC. Information Systems Development Terms SDLC - the development method used by most organizations today for large, complex systems Systems Analysts.
DMAIC DefineMeasureAnalyzeImproveControl D Define M Measure A Analyze I Improve C Control Implementing Six Sigma Quality at Better Body Manufacturing.
The Quality Improvement Model
44 th Annual Conference & Technical Exhibition By Thomas Hartman, P.E. The Hartman Company Georgetown, Texas Sustainable Chilled Water.
Autonomous Control of Scalextric Slot Car on User-Defined Track Siddharth Kamath Souma Mondal Dhaval Patel School of Electrical and Computer Engineering.
Premium Reduction & Delivery Improvement
1. 2 What is Six Sigma? What: Data driven method of identifying and resolving variations in processes. How: Driven by close understanding of customer.
Chapter 18 Optimizing and Controlling Processes through Statistical Process Control.
Static Pressure Control Loop The purpose of the static pressure control loop is to maintain an optimal static pressure in the ductwork. The control loop.
Using Six Sigma to Achieve CMMI Levels 4 and 5
Introduction to Systems Analysis and Design Trisha Cummings.
Year Seven Netbook Project. Aims of the Project To evaluate the impact on learning and teaching of using portable technologies both within and outside.
Discrete Event Simulation in Automotive Final Process System Vishvas Patel John Ma Throughput Analysis & Simulations General Motors 1999 Centerpoint Parkway.
Paul Macdonald Sales Manager Mark Andy UK Advancements in Flexo Technology.
AVTC Model Based Design Curriculum Development Project.
Support Package. SAP System consists of different software layers, also called Software components All of these layers are regularly updated using Support.
INT 506/706: Total Quality Management Introduction to Design of Experiments.
THE ROLE OF QUALITY CONTROL IN THE PREDICITION AND REDUCTION OF PRODUCTION DEFECTS OF SOME TEXTILE PREPARATION STAGES (WARPING ) Ibrahim, G., E., & Dorgham,
Variable Acceleration
NDIA Systems Engineering Supportability & Interoperability Conference October 2003 Using Six Sigma to Improve Systems Engineering Rick Hefner, Ph.D.
Lean6σ GOLD Urgent Support A Reduction in Urgent Customer Requests Resolution Cycle Time Charles Hollingsworth.
Topics of presentation
Process Improvement. It is not necessary to change. Survival is not mandatory. »W. Edwards Deming Both change and stability are fundamental to process.
MSE-415: B. Hawrylo Chapter 13 – Robust Design What is robust design/process/product?: A robust product (process) is one that performs as intended even.
THE TEST AND EVALUATION OF EO SENSORS Raymond F. Beach Senior EO Test Engineer SENSOR SYSTEMS PATUXENT RIVER, MD NDIA 6 TH Annual Systems.
Mobile Application Testing Mobile Application Testing.
6 Class 6 Organization in Process View MIS 2000 Information Systems for Management Bob Travica Updated Jan
SPECIAL FUNCTIONS ON ASDA-A2. To Audience Advance level This slide will teach some special functions on ASDA-A2. For better understanding the content,
Wireless Bluetooth Controller For DC Motor. Introduction Wireless becoming more and more available and widely used Bluetooth is one of the major players.
G. Trad on the behalf of the BSRT team Emittance meeting 04/11/2015.
Enterprise Resource Planning ESOLPK. INTRODUCTION  Enterprise resource planning (ERP) is business method management software package that permits a company.
Coordinator MPC with focus on maximizing throughput
Overview Modern chip designs have multiple IP components with different process, voltage, temperature sensitivities Optimizing mix to different customer.
High Density Polyethylene Pipes Quality Improvement
Model Reduction & Interface Modeling in Dynamic Substructuring Application to a Multi-Megawatt Wind Turbine MSc. Presentation Paul van der Valk.
Network Optimization Executive Seminar Track 1, Session A
Six Sigma Approach.
BANKING INFORMATION SYSTEMS
Data Analysis and Decision Making (Albrigth, Winston and Zappe)
OPS/571 Operations Management
Quality Management Six Sigma
Gage R&R Estimating measurement components
ENERGY SAVING Product Offering
Agenda Basics of powder dosing Gain-in-Weight technique
Yiyu Shi*, Wei Yao*, Jinjun Xiong+ and Lei He*
PLC / SCADA / HMI Controllers: Name : Muhammad Zunair Comsats University Date: 28-October-2018.
Smita Vijayakumar Qian Zhu Gagan Agrawal
Implementing Six Sigma Quality
Why Are Acrylic Coatings Important?
A practical approach for process control optimization during start-up
Design Of Experiment Eng. Ibrahim Kuhail.
Case Study 1 By : Shweta Agarwal Nikhil Walecha Amit Goyal
DESIGN OF EXPERIMENTS by R. C. Baker
Presentation transcript:

Making Sense of the Noise Finding and Eliminating Variation in Roll-to-Roll Processes AIMCAL Web Coating & Handling Conference October 28-31, 2018| Phoenix, AZ

Objective Identify the source of machine and cross-machine direction coat weight variation in a Pilot Line Current slot die coating line capabilities for coat weight uniformity MD: +/- 5-10% CMD: +/- 2% Backing roll and TWOSD processes The suspects: Speed Control Tension Control Fluid Delivery Control The plan: Current capabilities vs. Necessary capabilities Develop a solution to improve the MD and CD coat weight variation to +/- 1%

Experimental Outline Run a set of ladder studies: Speed Tension Develop designed factorial(s) to study the variables Analyze the experiment(s) Data acquisition and analysis tools: SCADA package for data collection PLC and drive trending Statistical software Experimental execution, data collection, analysis, and prepare conclusions and recommendations

Coating Line Layout

Initial Analysis Speed Control Ladder Study Full range of pilot line speeds vs. motor speed error (scaled for clarity) Current coating motor speed error range: ~ 5-20% Desired motor speed error range: ~ 0.30%

SCADA Software What can it do? How can we use the information?

SCADA Real-Time Data Trending

SCADA Real-Time Trends Error as a function of tension setpoint Error % increases with decreasing tension setpoint Independent of speed Independent of rewind diameter Online, real-time trouble shooting Data output to .csv, etc.

Factorial DOE and Results ¼ Fraction Factorial 6 factors (line speed, tension, tension mode, nip pressure, nip position, substrate type) 2 levels The variability within the speed overpowers almost all of the other factors with respect to speed control 3-5X stronger The variability in tension was affected by: Tension set point Tension mode (ratio or tension) Nip position (open or closed) Line speed

Effect of Speed on Variability

Effects on Tension

Initial Conclusions Fix the speed control first Retest speed and tension control Tune control loops

Implementing Speed Control Solutions Made market-based decision to reduce pilot coater line speed via gear reduction to allow higher motor speeds (better resolution) Designed and installed new sheaves and belts to produce 3:1 gear reduction without compromising machine’s original drive layout New top line speed: 650 FPM All motor encoders upgraded to sin/cos encoders to improve resolution throughout entire motor speed range Old encoders – 1,024 PPR (Pulses Per Rev) New encoders – 1,048,576 PPR

Motor Speed Control Review Target coating speeds on pilot line are 1, 3, and 5 MPM Motor error is around 1-2%, 0.4-0.6%, and 0.25-0.39% respectively for those speeds Previous errors of 10-20%, 3-6%, and 2-4% for same respective line speeds Result is a 10X improvement in motor speed control At new top line speed of 650 FPM motor errors are in range of 0.01-0.04% error New top line speed still in line with current market demand

Motor Speed Tuning All motors tuned under no-load conditions with new encoders All motors tuned under load for inertia mismatches Speed control ladder study (Focused on low speeds 0-20 FPM)

Study of Tension Control 3 modes of control Non-extensible Extensible Extensible plus Shrink-Compensation Nip pressure Nips open or closed Tension range Pilot line tension zones

Tension Control Obtained new baseline for current tension control (separate but directly related to speed control) Currently analyzing trends to begin optimizing tension control strategies +/- 1% MD/CMD Uniformity still end goal Resume testing with updated speed control – find new current uniformity limitations Reach uniformity goal with further tension control tuning

Tension Oscillations

Conclusions Speed control strategy was a success and gives pilot coater line much greater control in speed range that customers typically request for coating tests New “standard” set in terms of MD/CMD coating uniformity with new speed control Upgrades and study results led to consistent and repeatable coating uniformity control to within +/-1% for MD and XMD Continued instabilities and measurement system limitations prevent good process capability at the new standard Improvements in process capability will require further improvement in filtering noise from the input materials and measurement systems

Conclusions What else did we learn from this case study and exercise? Deeper understanding of how the pilot line works The ability to react to problems that arise and solve them faster Identify areas for future improvement Tension control tuning to offer further improvement in both MD/CMD battery coating uniformity Additional simplification of controls possible?

Questions?