David Goodrich, profile picture
Uploaded byDavid Goodrich
PPTX, PDF7,062 views

The Role of Process Characterization in Process Validation

The document discusses the role of process characterization in process validation. It begins by defining process validation and process characterization. Process characterization aims to identify and quantify all significant sources of variation, especially inherent variation in materials and technology as applied to the specific product design. Characterization studies determine how the process performs under worst-case conditions prior to qualification testing. The document then provides two case studies as examples of using design of experiments to characterize processes and identify optimal process settings.

Embed presentation

Downloaded 257 times
The Role of Process Characterization in Process Validation Speaker David A. Goodrich, P.E., CQE, CQA Presented at Greater Fort Worth ASQ Section 1416 2016 Cowtown Quality Roundup 22 April 2016 David A. Goodrich, P.E.
Process Validation Establishing by objective evidence that a process consistently produces a result or product meeting its predetermined specifications. Process Characterization - Identifying and quantifying all significant sources of variation, especially characterization of variation inherent to the materials and technology as applied to the specific product design. David A. Goodrich, P.E.
IQ OQ PQ David A. Goodrich, P.E.
Characterization studies determine what happens when conditions occur which stress the process. These conditions are considered the "most challenging" or worst-case. Must be completed prior to qualification testing, for processes chosen to validate. David A. Goodrich, P.E.
Worst-Case A set of process settings and conditions encompassing upper and lower processing limits. These settings pose the greatest chance of process or product failure when compared to ideal conditions. Such conditions do not necessarily induce product or process failure. David A. Goodrich, P.E.
When Should We Begin Characterization? Pre-Characterization Requirements: - Product and Process Specifications - Equipment Installation Qualifications - Software Validations (as applicable) - Process Risk Assessment Complete - Characteristics of materials verified to meet requirements - Applicable test methods qualified Characterization may use pre-production work instructions. David A. Goodrich, P.E.
When is a Characterization Study not Required? Transferring a Mature (Robust) Process to Another Facility – When operating limits are established at the developing or transferring facility, the decision may be made to test the operating limits, eliminating the need for a process characterization study. Replicating a Process with Identical Equipment – When operating limits are established, the decision may be made to test the operating limits, eliminating the need for a process characterization study (usually done when several identical machines are being validated at one time). Must demonstrate consistency between processes. The amount of qualification and validation testing required should be determined based on risk assessment. David A. Goodrich, P.E.
Look for Potential Sources of Process Variation - Equipment - Process - Materials - Measurement - People - Environment David A. Goodrich, P.E. Process Characterization Studies should identify and control the sources that can affect the process
What are the effects of materials variation on the process? • Critical Component dimensions - MMC, LMC • Material composition / hardness range • Component variation from multiple mold cavities • Cleanliness / Residual Process Materials – Mold release agents – Lubricants – Particulate • “Fresh” material vs. near end of shelf life • May require advance planning to procure materials for characterization activities ($$$ well spent!) David A. Goodrich, P.E. Some Common Examples:
Risk Analysis Process Failure Modes and Effects Analysis (PFMEA) Identifies sources of process variability affecting essential design requirements, and scores potential critical and key process parameters. Failures are prioritized according to how serious their consequences are (Severity), how frequently they occur (Occurrence) and how easily they can be detected (Detection). Calculate the risk priority number, or RPN, which equals S × O × D The purpose of the FMEA is to take actions to eliminate or reduce failures, starting with the highest-priority ones. Provides a rationale for risk-based confidence and reliability requirements (and corresponding sample size requirements) http://asq.org/learn-about-quality/process-analysis-tools/overview/fmea.html David A. Goodrich, P.E.
David A. Goodrich, P.E.
- Assign a Risk Rating from the PFMEA Risk Priority Number (RPN) - Example of typical Risk Ratings and associated RPNs (based on a 1 to 10 scale for severity, occurrence and detection) - Low Risk (RPN 1- 300) - Medium Risk (RPN 301-600) - High Risk (RPN 601 – 1000) - Confidence and Reliability “Requirements” (this may vary depending upon type of risks associated with the product, industry, etc.) Low Risk 95% Confidence / 90 % Reliability Medium Risk 95% Confidence / 95 % Reliability High Risk 99% Confidence / 99% Reliability Risk-based Confidence and Reliability Requirements David A. Goodrich, P.E.
• Only the parameters thought to be critical should be included in the characterization. • Start with the essential design requirements and study the process parameters that deliver those. • Design of Experiments (DOE) is a recommended tool for process characterization. The power of the DOE tool can identify significant variation factors, and their interaction(s), that impact process performance, product performance, and quality. Process Characterization Which process parameters should we study? David A. Goodrich, P.E.
DOE is an efficient and effective method for quantifying process factors and testing the limits of the process and/or technology. Factorial DOE provides the objective evidence of interaction factors and the Response Surface Method (RSM) can be used to predict both Worst-Case, as well as best case or optimum conditions or settings. The output of DOE can include a transfer function, or mathematical model of process behavior useful in describing process performance and understanding relationships between various process factors. Design of Experiments (DOE) David A. Goodrich, P.E.
- Identify the factors that could impact the process - Determine what levels of each factor to evaluate - Design the experiment - Run the experiment - Analyze the results Design of Experiments (DOE) David A. Goodrich, P.E.
Case Study 1: Kiln-Fired Ceramic Coating Process Existing Production Process - Poor Process Yield (coating thickness variation, pits and bubbles, poor adhesion) - High Rework Level (re-coat, re-fire, re-inspect) - High Reject Rate (rework largely not effective) Initial review of process identified considerable process variability Mellon Ramp Up Ney Ramp Up Mellon Cool Down Ney Cool Down David A. Goodrich, P.E.
6420-2-4 99 95 90 80 70 60 50 40 30 20 10 5 1 Standardized Effect Percent A Mix Ratio B Retract Speed C Fire Temp D Fire Time E C rash C ool Factor Name Not Significant Significant Effect Type DE B A Normal Plot of the Standardized Effects (response is Thickness, Alpha = 0.05) Stepwise Regression: Alpha-to-Enter: 0.15 Alpha-to-Remove: 0.15 Response is Thickness on 5 predictors, with N = 32 Step 1 2 3 Constant -0.03184 -0.03472 -0.03753 Mix Ratio 0.00075 0.00075 0.00075 T-Value 5.41 5.74 5.96 P-Value 0.000 0.000 0.000 Retract Speed 0.0029 0.0029 T-Value 2.20 2.28 P-Value 0.036 0.030 Fire Time 0.00019 T-Value 1.79 P-Value 0.085 S 0.00196 0.00185 0.00178 R-Sq 49.35 56.60 61.04 Factorial DOE using the Existing Process to Identify Significant and Non-significant Variables and Interactions D C E Design of Experiments – Coating Thickness Case Study 1: Kiln-Fired Ceramic Coating Process David A. Goodrich, P.E. Design-Expert Output
0.000933 0.00123 0.00167 0.00181 0.00230 0.00277 0.00330 0.00402 0.00450 0.00495 0.00575 0.00665 0.00764 0.0085 y = 0.0007e0.0856x R² = 0.9883 0.000 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 5 10 15 20 25 30 Seconds I n c h e s Coating Thickness vs. Viscosity Cup Drain Time Measuring Viscosity Directly (instead of mix ratio) Compensated for Solvent Evaporation (Mix Ratio change) and Dip Temperature Variation Enamel Ratio Operating Range 76% - 82% (68F – 78F) Case Study 1: Kiln-Fired Ceramic Coating Process Controls for the Most Significant Factor (Factor A – Mix Ratio) David A. Goodrich, P.E.
Case Study 1: Kiln-Fired Ceramic Coating Process Controls for 2nd Most Significant Factor (Factor B – Retract Speed) Controls for Interaction Factor (Factor DE – Fire Time/Crash Cool) One-Speed Setting on Dipping Machine - Eliminated operator variation (easy fix!) - Installed Programmable Cycle Furnaces – Identical Units - Less than 5% variation between units (Ramp up / Cool Down Cycle) Before After David A. Goodrich, P.E.
Resulting Process (Coating Thickness) – Cpk > 2.0 28252219161310741 7.50 7.25 7.00 IndividualValue _ X=7.2591 UCL=7.6810 LCL=6.8371 28252219161310741 0.4 0.2 0.0 MovingRange __ MR=0.1587 UCL=0.5184 LCL=0 28252219161310741 0.30 0.15 0.00 SampleRange _ R=0.0952 UCL=0.3110 LCL=0 9.69.08.47.87.26.66.0 LSL USL LSL 6 USL 10 Specifications 8.07.57.0 B/W Overall Specs Btw 0.1274 Within 0.08439 B/W 0.1528 Overall 0.2021 StDev Cp 4.36 Cpk 2.75 PPM-B/W 0.00 Pp 3.30 Ppk 2.08 Cpm * PPM-O 0.00 Capa Stats Between/Within Capability Sixpack of Coating Thickness Individuals Chart of Subgroup Means Moving Range Chart of Subgroup Means Range Chart of All Data Tests performed with unequal sample sizes Capability Histogram Normal Prob Plot AD: 0.446, P: 0.273 Capability Plot Case Study 1: Kiln-Fired Ceramic Coating Process David A. Goodrich, P.E.
- New Cleaning Method (Ultrasonic Cleaning Line) - New Coating Application Controls - Solvent Ratios based on viscosity measurements - Dipping speed controlled - Storage controls (temperature) - New PLC controlled furnaces provide highly repeatable furnace ramp up/down - Gage R&R’s (Attribute Agreement Analyses) for every inspection method - Calibration required process equipment - Material Cert. of analysis /Shelf life - Adhesion specification and test developed - Detailed Work Instructions Case Study 1: Kiln-Fired Ceramic Coating Process Source of Variation Controls Implemented - Manual “Brush” process for Pre-Coat Cleaning - Inadequate Coating Controls - Solvent Ratios based on “operator experience” - Dipping speed unknown - No storage controls - Extreme variation between firing ramp up/down - Unqualified inspection methods - Other general improvements David A. Goodrich, P.E.
Case Study 1: Kiln-Fired Ceramic Coating Process Post Improvement Process Results - Coating Thickness Cpk (> 2.0) - Statistically-based sample inspection for coating thickness - Near Elimination of Pits and Bubbles - 2x Improvement in Coating Adhesion strength - Adhesion strength monitoring implemented - Elimination of adhesion “events” - First pass yields > 99% Pre-Improvement Process Results - High Coating Thickness reject rate required 100% inspection multiple times - High levels of Pit and Bubbles requiring significant rework - Coating Adhesion “events” with no known cause or controls - First pass yield less than 50% - Final yield approximately 60% David A. Goodrich, P.E.
Case Study 2: Ultrasonic Welding Process Process to weld a cap onto a cartridge used to contain oil. - Five Welding Process variables: - Weld Pressure - Weld Time - Weld Amplitude - Hold Time - Trigger Force - An initial 5-factor, two-level full factorial screening experiment identified three significant process factors: - Weld Pressure - Weld Time - Weld Amplitude - Essential Design Requirements - Burst Pressure – 500 psi minimum - No flash at weld seam – 0.030” max weld collapse - No allowable leakage at weld seam – vacuum leak test (pass/fail) David A. Goodrich, P.E.
3 Factors - Weld Pressure, Weld Time, Weld Amplitude 2 - Responses – Burst Pressure and Weld Collapse Case Study 2: Ultrasonic Welding Process Performed a Central Composite Design - Response Surface DOE to find the optimized process settings Design Summary Study Type Response Surface Experiments 20 Initial Design Central Composite Blocks No Blocks Design Model Quadratic Response Name Units Obs Minimum Maximum Trans Model Y1 Collapse in 20 0.015 0.028 None Quadratic Y2 busrt pressure psi 20 800 1632 None Quadratic Factor Name Units Type Low Actual High Actual Low Coded High Coded A Pressure psi Numeric 15 35 -1 1 B Weld Time s Numeric 0.2 0.3 -1 1 C Amplitude % Numeric 60 80 -1 1 Response Surface DOE David A. Goodrich, P.E.
Case Study 2: Ultrasonic Welding Process Response Surface DOE – Minitab Output David A. Goodrich, P.E.
Case Study 2: Ultrasonic Welding Process Response Surface DOE – Minitab Output Response Optimization: burst pressure (psi), Collapse (in) Parameters Response Goal Lower Target Upper Weight Importance burst pressure (psi) Maximum 800.000 1632.00 1 1 Collapse (in) Target 0.011 0.026 0.028 1 1 Variable Ranges Variable Values Pressure (psi) (20, 30) Weld Time (s) (0.22, 0.28) Amplitude (%) (65, 75) burst pressure Pressure Weld Amplitude (psi) Collapse (in) Composite Solution (psi) Time (s) (%) Fit Fit Desirability 1 20 0.277468 75 1059.58 0.0260000 0.558568 Multiple Response Prediction Variable Setting Pressure (psi) 20 Weld Time (s) 0.277468 Amplitude (%) 75 Response Fit SE Fit 95% CI 95% PI burst pressure (psi) 1059.6 54.9 ( 937.2, 1182.0) ( 745.0, 1374.1) Collapse (in) 0.026000 0.000355 (0.025208, 0.026792) (0.023964, 0.028036) Optimized “Nominal” Process Settings David A. Goodrich, P.E.
Case Study 2: Ultrasonic Welding Process Response Surface DOE – Minitab Output David A. Goodrich, P.E.
Case Study 2: Ultrasonic Welding Process Response Surface DOE – Minitab Output Prediction for Collapse (in) Variable Setting Pressure (psi) 18 Weld Time (s) 0.26 Amplitude (%) 72 Fit SE Fit 95% CI 95% PI 0.0228894 0.0003434 (0.0221243, 0.0236544) (0.0208639, 0.0249148) Variable Setting Pressure (psi) 22 Weld Time (s) 0.29 Amplitude (%) 78 Fit SE Fit 95% CI 95% PI 0.0287903 0.0004147 (0.0278663, 0.0297142) (0.0266996, 0.0308809) Prediction for burst pressure (psi) Variable Setting Pressure (psi) 18 Weld Time (s) 0.26 Amplitude (%) 72 Fit SE Fit 95% CI 95% PI 892.425 53.0526 (774.216, 1010.63) (579.468, 1205.38) Variable Setting Pressure (psi) 22 Weld Time (s) 0.29 Amplitude (%) 78 Fit SE Fit 95% CI 95% PI 1222.80 64.0725 (1080.04, 1365.56) (899.768, 1545.83) Process “Minimum” Settings Process “Maximum” Settings David A. Goodrich, P.E.
1800150012009006003000 20 15 10 5 0 PSI Frequency 5000 1404 168.9 30 964.4 103.9 30 811.7 95.23 30 Mean StDev N max nom min Variable Normal Burst Pressure at Max, Nominal, Min Process Settings Burst Pressure Results Minimum, Maximum, and Nominal Process Settings Case Study 2: Ultrasonic Welding Process Burst Pressure results became more dispersed (less capable) at the Maximum Process settings. This was caused by increasing variation in the weld process as the Weld Collapse approached the allowable maximum (0.030 inch). Response Surface DOE David A. Goodrich, P.E.
Case Study 2: Ultrasonic Welding Process Response Optimization: burst pressure (psi), Collapse (in) Parameters Response Goal Lower Target Upper burst pressure (psi) Maximum 800.000 2000.00 Collapse (in) Target 0.011 0.036 0.038 Solution burst Pressure Weld Amplitude pressure Solution (psi) Time (s) (%) (psi) Collapse (in) 1 26.5463 0.33 86.82 1753.97 0.0360000 Response Surface DOE Using the Response Surface Model: “What If” the Target Collapse is increased from 0.026” to 0.036”? (This would require a component design change) Burst Pressure prediction increases significantly, indicating a much more robust weld David A. Goodrich, P.E.
1800150012009006003000 20 15 10 5 0 PSI Frequency 5000 1404 168.9 30 964.4 103.9 30 811.7 95.23 30 Mean StDev N max nom min Variable Normal Burst Pressure at Max, Nominal, Min Process Settings Burst Pressure Prior to Component Design Optimization Burst Pressure After Component Design Optimization Case Study 2: Ultrasonic Welding Process David A. Goodrich, P.E.
0 5 10 15 20 25 Frequency Total Collapse (inches) Total Weld Collapse Minimum, Nominal, Maximum Process Settings Lower Machine Reject Limit Upper Machine Reject Limit In-Process Monitoring – Validated Welding Process Automated Machine Shut Down Based on these Process Reject Limits Case Study 2: Ultrasonic Welding Process David A. Goodrich, P.E.
Process Characterization Study Deliverables • A fully defined process with appropriate process controls established. • Products (product family) covered by the characterization. • Material limitations (where applicable). • Measurable process characteristics. • Identification of Sources of anticipated significant process variation. • Proposed process controls strategy (recommendations) such as: - setup parameters, - process instructions, - nominal process settings (optimized for “best case”), - operating limits of the process identified (anticipated operating ranges including the “worst case” (min/max) settings. - actions to be taken if control limits are exceeded David A. Goodrich, P.E.
Characterization Study Deliverables • Gauge R&R Studies Completed (where applicable) • Attribute Agreement Studies Completed (where applicable) • Include test data and methods used during the characterization study • Updated Process Risk Analysis (e.g. Process FMEA, FTA) • Formal (Approved) Characterization Report Process Qualification runs that challenge the anticipated operating ranges including the “worst case” (min/max) settings are typically done after the approved characterization report as a part of an Operational Qualification Process Challenge (risk based sample sizes apply here!) David A. Goodrich, P.E.
Questions? Comments? Criticisms? http://www.statease.com/dx10.html Design-Expert Software Version 10 (45-day free trial) http://www.minitab.com/en-us/ Minitab Software Release 17 (30-day free trial) David A. Goodrich, P.E.
Factor Factor Factor Response Response Pressure Weld Time Amplitude Collapse Burst Pressure psi s percent in psi 35 0.3 60 0.0337 1229 25 0.25 53.18 0.0259 1002 25 0.25 70 0.0281 904 41.82 0.25 70 0.0381 1096 8.18 0.25 70 0.0109 465 25 0.25 70 0.0274 963 25 0.33 70 0.0331 1632 25 0.25 70 0.0282 1083 25 0.17 70 0.0235 693 15 0.3 60 0.0213 735 35 0.3 80 0.0377 1624 35 0.2 80 0.0323 1099 15 0.2 60 0.0163 565 25 0.25 70 0.0276 1112 35 0.2 60 0.0304 923 25 0.25 86.82 0.03 1183 25 0.25 70 0.028 1013 15 0.2 80 0.0179 904 15 0.3 80 0.023 915 25 0.25 70 0.0284 1116 Data Used for Case Study 2: Ultrasonic Welding Process Surface Response DOE Example David A. Goodrich, P.E.

More Related Content

PPTX
Quality by Design : Control strategy
byGMP EDUCATION : Not for Profit Organization
 
PPTX
US FDA Process Validation Stage 2 : No. Of Batches Required for Process Perfo...
byGMP EDUCATION : Not for Profit Organization
 
PPTX
Product validation
bysuhasini
 
PPTX
QbD and PAT Presentation
bysunp994
 
PDF
Process Validation Guidances FDA and Global
byInstitute of Validation Technology
 
PPTX
Process Validation for Beginners - FDA - EMA Approach
byGMP EDUCATION : Not for Profit Organization
 
PPTX
Real time release testing
byGMP EDUCATION : Not for Profit Organization
 
PPTX
New approach to Process Validation 4
bySantosh Singh
 
Quality by Design : Control strategy
byGMP EDUCATION : Not for Profit Organization
 
US FDA Process Validation Stage 2 : No. Of Batches Required for Process Perfo...
byGMP EDUCATION : Not for Profit Organization
 
Product validation
bysuhasini
 
QbD and PAT Presentation
bysunp994
 
Process Validation Guidances FDA and Global
byInstitute of Validation Technology
 
Process Validation for Beginners - FDA - EMA Approach
byGMP EDUCATION : Not for Profit Organization
 
Real time release testing
byGMP EDUCATION : Not for Profit Organization
 
New approach to Process Validation 4
bySantosh Singh
 

What's hot

PDF
Corrective and preventive action plan CAPA report form
byConnie Dello Buono
 
PPTX
PROCESS VALIDATION
byPharmaceutical
 
PPTX
Process validation and its types
byAnjali9410
 
PDF
An Overview of Biologics Manufacturing Processes and Things to Consider from ...
byWPICPE
 
PPT
Revised Process Validation
bypharmaakd
 
PPTX
Process validation
byabhishek awasthi
 
PPTX
Handling of Out of Specification Results
byGMP EDUCATION : Not for Profit Organization
 
PDF
Process validation fda
byFasika Mekete
 
PPTX
Pharmaceutical industry
by8149221398
 
PPTX
21 code of federal regulation
byUniversity Institute of Pharmaceutical Sciences
 
PPT
GMP- APQR training
byAmsavel Vel
 
PPTX
ICH Q11 Slide Summary
byDilawarHussain34
 
PPTX
Quality target product profile (QTPP)
byPriyesh singh
 
PPTX
Manufacturing Documentation
bySagar K Savale
 
PPTX
Role of quality assurance in pharmaceutical industry
byMuhammad Zubair
 
PPTX
Statistical Process Control Part 2
byMalay Pandya
 
PPTX
BPR review and batch release
byDr. Amsavel A
 
PDF
Pharmaceutical Manufacturing Operations
byObaid Ali / Roohi B. Obaid
 
PPTX
Second Party Audit and External Third Party Audit
byShantanuThakre3
 
PPTX
Quality metrics
bySrashtiMaheshwari2
 
Corrective and preventive action plan CAPA report form
byConnie Dello Buono
 
PROCESS VALIDATION
byPharmaceutical
 
Process validation and its types
byAnjali9410
 
An Overview of Biologics Manufacturing Processes and Things to Consider from ...
byWPICPE
 
Revised Process Validation
bypharmaakd
 
Process validation
byabhishek awasthi
 
Handling of Out of Specification Results
byGMP EDUCATION : Not for Profit Organization
 
Process validation fda
byFasika Mekete
 
Pharmaceutical industry
by8149221398
 
21 code of federal regulation
byUniversity Institute of Pharmaceutical Sciences
 
GMP- APQR training
byAmsavel Vel
 
ICH Q11 Slide Summary
byDilawarHussain34
 
Quality target product profile (QTPP)
byPriyesh singh
 
Manufacturing Documentation
bySagar K Savale
 
Role of quality assurance in pharmaceutical industry
byMuhammad Zubair
 
Statistical Process Control Part 2
byMalay Pandya
 
BPR review and batch release
byDr. Amsavel A
 
Pharmaceutical Manufacturing Operations
byObaid Ali / Roohi B. Obaid
 
Second Party Audit and External Third Party Audit
byShantanuThakre3
 
Quality metrics
bySrashtiMaheshwari2
 

Viewers also liked

PDF
Quality by Design at a Biopharma CMO (Contract Manufacturing Organization)
byKBI Biopharma
 
PDF
A Manufacturer’s Perspective on Innovations in Biomanufacturing
byKBI Biopharma
 
PDF
A Lifecycle Approach to Process Validation
byInstitute of Validation Technology
 
PPTX
Rapid mixer
bylamrin33
 
PPTX
RAPID MIXER GRANULATOR OR HIGH SHEAR MIXER
byRIDDHI PHARMA MACHINERY LTD
 
PPT
Validation Part7
byHoussam Mansi
 
PPTX
Coating pans
byMalla Reddy College of Pharmacy
 
PPT
Rapid mixer graqnulator
byMalla Reddy College of Pharmacy
 
PPT
Validation of mixing, granulation, lubrication, compression and coating
byMalla Reddy College of Pharmacy
 
DOCX
Caracterización de procesos
by1nataliadimate
 
PPTX
Process Validation of API
bysrirao3462
 
PPTX
Validation of solid oral dosage form, tablet 1
byJamia Hamdard
 
PPTX
Granulation ppt.
byNamdeo Shinde
 
PPTX
FBD ppt
bySushma Appala
 
PPTX
Elaboración de tablas de frecuencia, estadística
byGerardo Lagos
 
PPT
The 8 Methods Of Characterization Powerpoint
byes99.trish.turner
 
PPTX
Presentation for Tablet Coating
byMd. Shafiqul Islam
 
Quality by Design at a Biopharma CMO (Contract Manufacturing Organization)
byKBI Biopharma
 
A Manufacturer’s Perspective on Innovations in Biomanufacturing
byKBI Biopharma
 
A Lifecycle Approach to Process Validation
byInstitute of Validation Technology
 
Rapid mixer
bylamrin33
 
RAPID MIXER GRANULATOR OR HIGH SHEAR MIXER
byRIDDHI PHARMA MACHINERY LTD
 
Validation Part7
byHoussam Mansi
 
Coating pans
byMalla Reddy College of Pharmacy
 
Rapid mixer graqnulator
byMalla Reddy College of Pharmacy
 
Validation of mixing, granulation, lubrication, compression and coating
byMalla Reddy College of Pharmacy
 
Caracterización de procesos
by1nataliadimate
 
Process Validation of API
bysrirao3462
 
Validation of solid oral dosage form, tablet 1
byJamia Hamdard
 
Granulation ppt.
byNamdeo Shinde
 
FBD ppt
bySushma Appala
 
Elaboración de tablas de frecuencia, estadística
byGerardo Lagos
 
The 8 Methods Of Characterization Powerpoint
byes99.trish.turner
 
Presentation for Tablet Coating
byMd. Shafiqul Islam
 

Similar to The Role of Process Characterization in Process Validation

PDF
q-Maxim’s approach to waste reduction in foundry application using Taguchi ...
byq-Maxim
 
PPTX
Doe As Process Control Introduction
byKelly Brown
 
PPT
2011 QbD and More
byAlainPoncin
 
PDF
Determination of Optimum Parameters Affecting the Properties of O Rings
byIRJET Journal
 
PDF
Determination of Optimum Parameters Affecting the Properties of O Rings
byIRJET Journal
 
PDF
Statistical solutions to help you with 5 FDA medical devices stages
byMinitab, LLC
 
PPT
QM-011-Design Process FMEA
byhandbook
 
PDF
Failure mode effect_analysis_fmea
byJitesh Gaurav
 
PPTX
FMEA training required as per knowledge gain and improve.pptx
byVipinKumar957897
 
PPT
design of experiments
bysigma-tau
 
PDF
Process fmea
byCardiff City FC
 
PDF
Process fmea breakfast
byCardiff City FC
 
PPT
2004 4052 b1-09_Hussain-Arden-UK-Presentation
byAjaz Hussain
 
PDF
DFMEA DR & DVP 261113 KCV
byKamal Vora
 
PDF
Risk management in complex changeovers through cfmea
byDr. Bikram Jit Singh
 
PDF
FMEA-MarApr14_FINAL
byJeannie Counce
 
PPT
Ssoverview
bySubhash Raje
 
PPT
Introduction to six sigma
bynatrajsenthil
 
PDF
The certified six sigma green belt handbook, 2nd edition (1)
byThuong19
 
PPT
FMEA - What is FMEA. Everything about FMEA.
byDheeraj Chavan
 
q-Maxim’s approach to waste reduction in foundry application using Taguchi ...
byq-Maxim
 
Doe As Process Control Introduction
byKelly Brown
 
2011 QbD and More
byAlainPoncin
 
Determination of Optimum Parameters Affecting the Properties of O Rings
byIRJET Journal
 
Determination of Optimum Parameters Affecting the Properties of O Rings
byIRJET Journal
 
Statistical solutions to help you with 5 FDA medical devices stages
byMinitab, LLC
 
QM-011-Design Process FMEA
byhandbook
 
Failure mode effect_analysis_fmea
byJitesh Gaurav
 
FMEA training required as per knowledge gain and improve.pptx
byVipinKumar957897
 
design of experiments
bysigma-tau
 
Process fmea
byCardiff City FC
 
Process fmea breakfast
byCardiff City FC
 
2004 4052 b1-09_Hussain-Arden-UK-Presentation
byAjaz Hussain
 
DFMEA DR & DVP 261113 KCV
byKamal Vora
 
Risk management in complex changeovers through cfmea
byDr. Bikram Jit Singh
 
FMEA-MarApr14_FINAL
byJeannie Counce
 
Ssoverview
bySubhash Raje
 
Introduction to six sigma
bynatrajsenthil
 
The certified six sigma green belt handbook, 2nd edition (1)
byThuong19
 
FMEA - What is FMEA. Everything about FMEA.
byDheeraj Chavan
 

Recently uploaded

PPTX
Rooted in Christ, Sabbath School Object lesson
byRexfordKouto2
 
PDF
A Brief Introduction About Dr. T. La Mont Holder
byDr. T. La Mont Holder
 
PDF
UnityNet Champions of the World — Knowledge Note (2026-01-26)
byLHelferty
 
DOCX
Teacher Induction Program course 1 The Dep ed Teacher
byjanaalfonso1
 
PPTX
Exploring Various Career Pathways powerpoint
byelizabethpaguia1
 
PDF
The Legacy of Aspirin - A century-old medicine that shaped therapeutics
byObaid Ali / Roohi B. Obaid
 
PPTX
Nehemiah 1.5-11 6.2-16 GPBC 02.01.26.pptx
byLazarou Richard
 
PDF
BLU8 Introduction by Ian H. Bates - February 2026
byIan Bates
 
Rooted in Christ, Sabbath School Object lesson
byRexfordKouto2
 
A Brief Introduction About Dr. T. La Mont Holder
byDr. T. La Mont Holder
 
UnityNet Champions of the World — Knowledge Note (2026-01-26)
byLHelferty
 
Teacher Induction Program course 1 The Dep ed Teacher
byjanaalfonso1
 
Exploring Various Career Pathways powerpoint
byelizabethpaguia1
 
The Legacy of Aspirin - A century-old medicine that shaped therapeutics
byObaid Ali / Roohi B. Obaid
 
Nehemiah 1.5-11 6.2-16 GPBC 02.01.26.pptx
byLazarou Richard
 
BLU8 Introduction by Ian H. Bates - February 2026
byIan Bates
 

The Role of Process Characterization in Process Validation

  • 1.
    The Role ofProcess Characterization in Process Validation Speaker David A. Goodrich, P.E., CQE, CQA Presented at Greater Fort Worth ASQ Section 1416 2016 Cowtown Quality Roundup 22 April 2016 David A. Goodrich, P.E.
  • 2.
    Process Validation Establishing byobjective evidence that a process consistently produces a result or product meeting its predetermined specifications. Process Characterization - Identifying and quantifying all significant sources of variation, especially characterization of variation inherent to the materials and technology as applied to the specific product design. David A. Goodrich, P.E.
  • 3.
  • 4.
    Characterization studies determinewhat happens when conditions occur which stress the process. These conditions are considered the "most challenging" or worst-case. Must be completed prior to qualification testing, for processes chosen to validate. David A. Goodrich, P.E.
  • 5.
    Worst-Case A set ofprocess settings and conditions encompassing upper and lower processing limits. These settings pose the greatest chance of process or product failure when compared to ideal conditions. Such conditions do not necessarily induce product or process failure. David A. Goodrich, P.E.
  • 6.
    When Should WeBegin Characterization? Pre-Characterization Requirements: - Product and Process Specifications - Equipment Installation Qualifications - Software Validations (as applicable) - Process Risk Assessment Complete - Characteristics of materials verified to meet requirements - Applicable test methods qualified Characterization may use pre-production work instructions. David A. Goodrich, P.E.
  • 7.
    When is aCharacterization Study not Required? Transferring a Mature (Robust) Process to Another Facility – When operating limits are established at the developing or transferring facility, the decision may be made to test the operating limits, eliminating the need for a process characterization study. Replicating a Process with Identical Equipment – When operating limits are established, the decision may be made to test the operating limits, eliminating the need for a process characterization study (usually done when several identical machines are being validated at one time). Must demonstrate consistency between processes. The amount of qualification and validation testing required should be determined based on risk assessment. David A. Goodrich, P.E.
  • 8.
    Look for PotentialSources of Process Variation - Equipment - Process - Materials - Measurement - People - Environment David A. Goodrich, P.E. Process Characterization Studies should identify and control the sources that can affect the process
  • 9.
    What are theeffects of materials variation on the process? • Critical Component dimensions - MMC, LMC • Material composition / hardness range • Component variation from multiple mold cavities • Cleanliness / Residual Process Materials – Mold release agents – Lubricants – Particulate • “Fresh” material vs. near end of shelf life • May require advance planning to procure materials for characterization activities ($$$ well spent!) David A. Goodrich, P.E. Some Common Examples:
  • 10.
    Risk Analysis Process FailureModes and Effects Analysis (PFMEA) Identifies sources of process variability affecting essential design requirements, and scores potential critical and key process parameters. Failures are prioritized according to how serious their consequences are (Severity), how frequently they occur (Occurrence) and how easily they can be detected (Detection). Calculate the risk priority number, or RPN, which equals S × O × D The purpose of the FMEA is to take actions to eliminate or reduce failures, starting with the highest-priority ones. Provides a rationale for risk-based confidence and reliability requirements (and corresponding sample size requirements) http://asq.org/learn-about-quality/process-analysis-tools/overview/fmea.html David A. Goodrich, P.E.
  • 11.
  • 12.
    - Assign aRisk Rating from the PFMEA Risk Priority Number (RPN) - Example of typical Risk Ratings and associated RPNs (based on a 1 to 10 scale for severity, occurrence and detection) - Low Risk (RPN 1- 300) - Medium Risk (RPN 301-600) - High Risk (RPN 601 – 1000) - Confidence and Reliability “Requirements” (this may vary depending upon type of risks associated with the product, industry, etc.) Low Risk 95% Confidence / 90 % Reliability Medium Risk 95% Confidence / 95 % Reliability High Risk 99% Confidence / 99% Reliability Risk-based Confidence and Reliability Requirements David A. Goodrich, P.E.
  • 13.
    • Only theparameters thought to be critical should be included in the characterization. • Start with the essential design requirements and study the process parameters that deliver those. • Design of Experiments (DOE) is a recommended tool for process characterization. The power of the DOE tool can identify significant variation factors, and their interaction(s), that impact process performance, product performance, and quality. Process Characterization Which process parameters should we study? David A. Goodrich, P.E.
  • 14.
    DOE is anefficient and effective method for quantifying process factors and testing the limits of the process and/or technology. Factorial DOE provides the objective evidence of interaction factors and the Response Surface Method (RSM) can be used to predict both Worst-Case, as well as best case or optimum conditions or settings. The output of DOE can include a transfer function, or mathematical model of process behavior useful in describing process performance and understanding relationships between various process factors. Design of Experiments (DOE) David A. Goodrich, P.E.
  • 15.
    - Identify thefactors that could impact the process - Determine what levels of each factor to evaluate - Design the experiment - Run the experiment - Analyze the results Design of Experiments (DOE) David A. Goodrich, P.E.
  • 16.
    Case Study 1:Kiln-Fired Ceramic Coating Process Existing Production Process - Poor Process Yield (coating thickness variation, pits and bubbles, poor adhesion) - High Rework Level (re-coat, re-fire, re-inspect) - High Reject Rate (rework largely not effective) Initial review of process identified considerable process variability Mellon Ramp Up Ney Ramp Up Mellon Cool Down Ney Cool Down David A. Goodrich, P.E.
  • 17.
    6420-2-4 99 95 90 80 70 60 50 40 30 20 10 5 1 Standardized Effect Percent A MixRatio B Retract Speed C Fire Temp D Fire Time E C rash C ool Factor Name Not Significant Significant Effect Type DE B A Normal Plot of the Standardized Effects (response is Thickness, Alpha = 0.05) Stepwise Regression: Alpha-to-Enter: 0.15 Alpha-to-Remove: 0.15 Response is Thickness on 5 predictors, with N = 32 Step 1 2 3 Constant -0.03184 -0.03472 -0.03753 Mix Ratio 0.00075 0.00075 0.00075 T-Value 5.41 5.74 5.96 P-Value 0.000 0.000 0.000 Retract Speed 0.0029 0.0029 T-Value 2.20 2.28 P-Value 0.036 0.030 Fire Time 0.00019 T-Value 1.79 P-Value 0.085 S 0.00196 0.00185 0.00178 R-Sq 49.35 56.60 61.04 Factorial DOE using the Existing Process to Identify Significant and Non-significant Variables and Interactions D C E Design of Experiments – Coating Thickness Case Study 1: Kiln-Fired Ceramic Coating Process David A. Goodrich, P.E. Design-Expert Output
  • 18.
    0.000933 0.00123 0.00167 0.00181 0.00230 0.00277 0.00330 0.00402 0.00450 0.00495 0.00575 0.00665 0.00764 0.0085 y =0.0007e0.0856x R² = 0.9883 0.000 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 5 10 15 20 25 30 Seconds I n c h e s Coating Thickness vs. Viscosity Cup Drain Time Measuring Viscosity Directly (instead of mix ratio) Compensated for Solvent Evaporation (Mix Ratio change) and Dip Temperature Variation Enamel Ratio Operating Range 76% - 82% (68F – 78F) Case Study 1: Kiln-Fired Ceramic Coating Process Controls for the Most Significant Factor (Factor A – Mix Ratio) David A. Goodrich, P.E.
  • 19.
    Case Study 1:Kiln-Fired Ceramic Coating Process Controls for 2nd Most Significant Factor (Factor B – Retract Speed) Controls for Interaction Factor (Factor DE – Fire Time/Crash Cool) One-Speed Setting on Dipping Machine - Eliminated operator variation (easy fix!) - Installed Programmable Cycle Furnaces – Identical Units - Less than 5% variation between units (Ramp up / Cool Down Cycle) Before After David A. Goodrich, P.E.
  • 20.
    Resulting Process (CoatingThickness) – Cpk > 2.0 28252219161310741 7.50 7.25 7.00 IndividualValue _ X=7.2591 UCL=7.6810 LCL=6.8371 28252219161310741 0.4 0.2 0.0 MovingRange __ MR=0.1587 UCL=0.5184 LCL=0 28252219161310741 0.30 0.15 0.00 SampleRange _ R=0.0952 UCL=0.3110 LCL=0 9.69.08.47.87.26.66.0 LSL USL LSL 6 USL 10 Specifications 8.07.57.0 B/W Overall Specs Btw 0.1274 Within 0.08439 B/W 0.1528 Overall 0.2021 StDev Cp 4.36 Cpk 2.75 PPM-B/W 0.00 Pp 3.30 Ppk 2.08 Cpm * PPM-O 0.00 Capa Stats Between/Within Capability Sixpack of Coating Thickness Individuals Chart of Subgroup Means Moving Range Chart of Subgroup Means Range Chart of All Data Tests performed with unequal sample sizes Capability Histogram Normal Prob Plot AD: 0.446, P: 0.273 Capability Plot Case Study 1: Kiln-Fired Ceramic Coating Process David A. Goodrich, P.E.
  • 21.
    - New CleaningMethod (Ultrasonic Cleaning Line) - New Coating Application Controls - Solvent Ratios based on viscosity measurements - Dipping speed controlled - Storage controls (temperature) - New PLC controlled furnaces provide highly repeatable furnace ramp up/down - Gage R&R’s (Attribute Agreement Analyses) for every inspection method - Calibration required process equipment - Material Cert. of analysis /Shelf life - Adhesion specification and test developed - Detailed Work Instructions Case Study 1: Kiln-Fired Ceramic Coating Process Source of Variation Controls Implemented - Manual “Brush” process for Pre-Coat Cleaning - Inadequate Coating Controls - Solvent Ratios based on “operator experience” - Dipping speed unknown - No storage controls - Extreme variation between firing ramp up/down - Unqualified inspection methods - Other general improvements David A. Goodrich, P.E.
  • 22.
    Case Study 1:Kiln-Fired Ceramic Coating Process Post Improvement Process Results - Coating Thickness Cpk (> 2.0) - Statistically-based sample inspection for coating thickness - Near Elimination of Pits and Bubbles - 2x Improvement in Coating Adhesion strength - Adhesion strength monitoring implemented - Elimination of adhesion “events” - First pass yields > 99% Pre-Improvement Process Results - High Coating Thickness reject rate required 100% inspection multiple times - High levels of Pit and Bubbles requiring significant rework - Coating Adhesion “events” with no known cause or controls - First pass yield less than 50% - Final yield approximately 60% David A. Goodrich, P.E.
  • 23.
    Case Study 2:Ultrasonic Welding Process Process to weld a cap onto a cartridge used to contain oil. - Five Welding Process variables: - Weld Pressure - Weld Time - Weld Amplitude - Hold Time - Trigger Force - An initial 5-factor, two-level full factorial screening experiment identified three significant process factors: - Weld Pressure - Weld Time - Weld Amplitude - Essential Design Requirements - Burst Pressure – 500 psi minimum - No flash at weld seam – 0.030” max weld collapse - No allowable leakage at weld seam – vacuum leak test (pass/fail) David A. Goodrich, P.E.
  • 24.
    3 Factors -Weld Pressure, Weld Time, Weld Amplitude 2 - Responses – Burst Pressure and Weld Collapse Case Study 2: Ultrasonic Welding Process Performed a Central Composite Design - Response Surface DOE to find the optimized process settings Design Summary Study Type Response Surface Experiments 20 Initial Design Central Composite Blocks No Blocks Design Model Quadratic Response Name Units Obs Minimum Maximum Trans Model Y1 Collapse in 20 0.015 0.028 None Quadratic Y2 busrt pressure psi 20 800 1632 None Quadratic Factor Name Units Type Low Actual High Actual Low Coded High Coded A Pressure psi Numeric 15 35 -1 1 B Weld Time s Numeric 0.2 0.3 -1 1 C Amplitude % Numeric 60 80 -1 1 Response Surface DOE David A. Goodrich, P.E.
  • 25.
    Case Study 2:Ultrasonic Welding Process Response Surface DOE – Minitab Output David A. Goodrich, P.E.
  • 26.
    Case Study 2:Ultrasonic Welding Process Response Surface DOE – Minitab Output Response Optimization: burst pressure (psi), Collapse (in) Parameters Response Goal Lower Target Upper Weight Importance burst pressure (psi) Maximum 800.000 1632.00 1 1 Collapse (in) Target 0.011 0.026 0.028 1 1 Variable Ranges Variable Values Pressure (psi) (20, 30) Weld Time (s) (0.22, 0.28) Amplitude (%) (65, 75) burst pressure Pressure Weld Amplitude (psi) Collapse (in) Composite Solution (psi) Time (s) (%) Fit Fit Desirability 1 20 0.277468 75 1059.58 0.0260000 0.558568 Multiple Response Prediction Variable Setting Pressure (psi) 20 Weld Time (s) 0.277468 Amplitude (%) 75 Response Fit SE Fit 95% CI 95% PI burst pressure (psi) 1059.6 54.9 ( 937.2, 1182.0) ( 745.0, 1374.1) Collapse (in) 0.026000 0.000355 (0.025208, 0.026792) (0.023964, 0.028036) Optimized “Nominal” Process Settings David A. Goodrich, P.E.
  • 27.
    Case Study 2:Ultrasonic Welding Process Response Surface DOE – Minitab Output David A. Goodrich, P.E.
  • 28.
    Case Study 2:Ultrasonic Welding Process Response Surface DOE – Minitab Output Prediction for Collapse (in) Variable Setting Pressure (psi) 18 Weld Time (s) 0.26 Amplitude (%) 72 Fit SE Fit 95% CI 95% PI 0.0228894 0.0003434 (0.0221243, 0.0236544) (0.0208639, 0.0249148) Variable Setting Pressure (psi) 22 Weld Time (s) 0.29 Amplitude (%) 78 Fit SE Fit 95% CI 95% PI 0.0287903 0.0004147 (0.0278663, 0.0297142) (0.0266996, 0.0308809) Prediction for burst pressure (psi) Variable Setting Pressure (psi) 18 Weld Time (s) 0.26 Amplitude (%) 72 Fit SE Fit 95% CI 95% PI 892.425 53.0526 (774.216, 1010.63) (579.468, 1205.38) Variable Setting Pressure (psi) 22 Weld Time (s) 0.29 Amplitude (%) 78 Fit SE Fit 95% CI 95% PI 1222.80 64.0725 (1080.04, 1365.56) (899.768, 1545.83) Process “Minimum” Settings Process “Maximum” Settings David A. Goodrich, P.E.
  • 29.
    1800150012009006003000 20 15 10 5 0 PSI Frequency 5000 1404 168.9 30 964.4103.9 30 811.7 95.23 30 Mean StDev N max nom min Variable Normal Burst Pressure at Max, Nominal, Min Process Settings Burst Pressure Results Minimum, Maximum, and Nominal Process Settings Case Study 2: Ultrasonic Welding Process Burst Pressure results became more dispersed (less capable) at the Maximum Process settings. This was caused by increasing variation in the weld process as the Weld Collapse approached the allowable maximum (0.030 inch). Response Surface DOE David A. Goodrich, P.E.
  • 30.
    Case Study 2:Ultrasonic Welding Process Response Optimization: burst pressure (psi), Collapse (in) Parameters Response Goal Lower Target Upper burst pressure (psi) Maximum 800.000 2000.00 Collapse (in) Target 0.011 0.036 0.038 Solution burst Pressure Weld Amplitude pressure Solution (psi) Time (s) (%) (psi) Collapse (in) 1 26.5463 0.33 86.82 1753.97 0.0360000 Response Surface DOE Using the Response Surface Model: “What If” the Target Collapse is increased from 0.026” to 0.036”? (This would require a component design change) Burst Pressure prediction increases significantly, indicating a much more robust weld David A. Goodrich, P.E.
  • 31.
    1800150012009006003000 20 15 10 5 0 PSI Frequency 5000 1404 168.9 30 964.4103.9 30 811.7 95.23 30 Mean StDev N max nom min Variable Normal Burst Pressure at Max, Nominal, Min Process Settings Burst Pressure Prior to Component Design Optimization Burst Pressure After Component Design Optimization Case Study 2: Ultrasonic Welding Process David A. Goodrich, P.E.
  • 32.
    0 5 10 15 20 25 Frequency Total Collapse (inches) TotalWeld Collapse Minimum, Nominal, Maximum Process Settings Lower Machine Reject Limit Upper Machine Reject Limit In-Process Monitoring – Validated Welding Process Automated Machine Shut Down Based on these Process Reject Limits Case Study 2: Ultrasonic Welding Process David A. Goodrich, P.E.
  • 33.
    Process Characterization Study Deliverables •A fully defined process with appropriate process controls established. • Products (product family) covered by the characterization. • Material limitations (where applicable). • Measurable process characteristics. • Identification of Sources of anticipated significant process variation. • Proposed process controls strategy (recommendations) such as: - setup parameters, - process instructions, - nominal process settings (optimized for “best case”), - operating limits of the process identified (anticipated operating ranges including the “worst case” (min/max) settings. - actions to be taken if control limits are exceeded David A. Goodrich, P.E.
  • 34.
    Characterization Study Deliverables •Gauge R&R Studies Completed (where applicable) • Attribute Agreement Studies Completed (where applicable) • Include test data and methods used during the characterization study • Updated Process Risk Analysis (e.g. Process FMEA, FTA) • Formal (Approved) Characterization Report Process Qualification runs that challenge the anticipated operating ranges including the “worst case” (min/max) settings are typically done after the approved characterization report as a part of an Operational Qualification Process Challenge (risk based sample sizes apply here!) David A. Goodrich, P.E.
  • 35.
    Questions? Comments? Criticisms? http://www.statease.com/dx10.html Design-Expert Software Version10 (45-day free trial) http://www.minitab.com/en-us/ Minitab Software Release 17 (30-day free trial) David A. Goodrich, P.E.
  • 36.
    Factor Factor FactorResponse Response Pressure Weld Time Amplitude Collapse Burst Pressure psi s percent in psi 35 0.3 60 0.0337 1229 25 0.25 53.18 0.0259 1002 25 0.25 70 0.0281 904 41.82 0.25 70 0.0381 1096 8.18 0.25 70 0.0109 465 25 0.25 70 0.0274 963 25 0.33 70 0.0331 1632 25 0.25 70 0.0282 1083 25 0.17 70 0.0235 693 15 0.3 60 0.0213 735 35 0.3 80 0.0377 1624 35 0.2 80 0.0323 1099 15 0.2 60 0.0163 565 25 0.25 70 0.0276 1112 35 0.2 60 0.0304 923 25 0.25 86.82 0.03 1183 25 0.25 70 0.028 1013 15 0.2 80 0.0179 904 15 0.3 80 0.023 915 25 0.25 70 0.0284 1116 Data Used for Case Study 2: Ultrasonic Welding Process Surface Response DOE Example David A. Goodrich, P.E.