On Thu, Mar 11,  Matthew Hu presented “ASQ RRD series webinar: Robustness Thinking in Design for Reliability – A Best Practice in Design for Reliability”

Reliability is one of the most important characteristics of an engineering system. Reliability can be measured as robustness over time as a leading key performance indicator (KPI). Robustness thinking is essential to improve quality and reliability proactively by factoring the activities of design for reliability. Nothing can be substituted for thinking. Early robustness development in manufacturing can reduce the variability of those processes with valuable benefits to manufacturing yields, cycle time and costs. Product Development has a huge impact on revenue stream, reliability. It is most cost-effective and less time-consuming to make design insensitive to uncontrollable user environments in upfront design phase. Robustness development in Design for Reliability (DFR) process provides benefits in reduction of early-on physical testing and traditional test-fix-test cycles. Robustness achieved early in development enables shorter cycle times in the later design phases.

Objectives of the presentation
• Define robustness
• Explain product development using Robust Engineering versus traditional product development
• Explain Robust Design for Reliability
• Define Objective Function, Basic Function, and Ideal Function
• Explain how Ideal Function and Two-step Optimization lead to robust technology development and achieve “Better, Cheaper, Faster” product development
• Explain how to conduct a preliminary robustness assessment
• Explain the value of robustness assessment
• LiDar case study in robust autonomous driving technology development

Important Takeaway
• Make design insensitive to uncontrollable user environment (Noise)
• Early development of robustness is key to proactive quality and reliability Improvement
– Capture, front load noise and manage noise
– Gain control of your product performance
– Optimize robustness – avoid all failure modes
• Apply Robust design principles at early stages of product design to “forecast” problems and take preventive action.


ASQ RRD series webinar: Studying Fractures – Recognizing and Understanding Failure Modes

Thu, Jul 8, 2021 12:00 PM – 1:00 PM EDT

Presenter: Shane Turcott


Investigating equipment failures is one of the key roles of the reliability team. Instead of guessing how a part failed, its fracture features can be studied and the failure mode diagnosed. This talk will introduce how mechanical fracture features of steel failures can be recognized, then how diagnosis can help direct an RCA investigation. Brittle fracture will be used to illustrate the logic sequence applied in diagnosing, understanding why something failed and applying this information towards developing effective solutions.

Shane graduated with his B.Eng and M.A.Sc in Materials Engineering from McMaster University, Canada. He has performed failure analysis for various employers before founding Steel Image in 2009. Steel Image which is a lab-based metallurgical engineering company supporting reliability efforts by providing failure analysis and on-site material evaluations. He is the past Ontario Chair of the American Society of Materials, author of ‘Decoding Mechanical Failures’ and is the host of the ASM Materials World Class Series.

ASQ RRD series webinar: Fault Tree Analysis as a Means to Promote Safety

Thu, Jun 10, 2021 12:00 PM – 1:00 PM EDT

Presenter: Jennifer B. Akers


Designing safe products and systems is critical to reduce accident risk and minimize product liability. There are various reliability and quality analysis methodologies used to ensure safety including risk analysis techniques such as fault tree analysis (FTA), failure mode and effects analysis (FMEA), and others. This webinar focuses on providing an introductory look at FTA and its role in promoting safety. We review basics about fault tree analysis, including what it is, its history, its uses and advantages. A thorough review of qualitative and quantitative FTA results, including minimal cut sets (MCS), quantitative metrics such as unavailability, and importance measures, is included. The presentation concludes with guidance on how FTA qualitative and quantitative results can be used to design inherently safer products and systems.

Jennifer Akers is an Education Development Specialist with Relyence Corporation. She is responsible for training services and technical support offered by Relyence. Jennifer has a Bachelor of Science degree in Chemical Engineering from Lafayette College. Jennifer has over 20 years of experience in the reliability and quality fields. Her work has spanned software development, quality assurance, technical support, and education and training for reliability and quality software tools. She has co-authored several papers and tutorials on topics such as closed loop corrective action, fault tree analysis, and warranty analysis.

ASQ RRD Series Webinar: Evolution Of Electrolytic Capacitors- Why A Reliability Engineer Should Know This:

Thursday May 13th, noon – 1pm Eastern Time

Presenter: Greg Caswell


 Why would a Reliability practitioner need to know this? Where there is electricity, that is, electrons moving collectively, there is capacitance. In systems, especially complex systems, changes in capacitance, or the incidental formative presence of an undesired capacitance can impose a spurious system response which could lead to a functional failure. In the grand scheme of things, it would be advantageous for the reliability practitioner to have a perspective on capacitance. This presentation provides one such viewpoint.

Electrolytic capacitors have long been identified as a weak link for long term high reliability applications.  However, capacitor manufacturers have made significant improvements to the materials and manufacturing processes to enhance their reliability.  This webinar will discuss those changes, provide insight into the various failure mechanisms for electrolytic capacitors and describe appropriate accelerated tests to validate performance.

We will take a deeper dive into the methodologies utilized to improve capacitor performance, e.g. foil purity and electrolyte volume.  We will also discuss, from a reliability perspective, the impact of changing to a higher temperature electrolyte (from ethylene glycol to DMF, DMA and GBL) and also changes in the bung material (from butyl to EPDM).

There are several environmental factors involved in the aging of electrolytic capacitors.  Electrolyte loss due to drying out and leakage current due to oxide degradation are thermally related as is the self-heating associated with ripple current.  The impact of the applied voltage level is also a driver as it can cause leakage current increases as well.  All of these issues result in a capacitance decrease, an increase in ESR, and a change to the dissipation factor.  Many other failure mechanisms associated with manufacturing will also be discussed.

Examples of calculations for life expectancy will be shown to demonstrate the effects of applied voltage, rated temperature, ripple current, and the endurance factor coupled with the application usage profile. 


•        Greg has over 50 years of experience in electronics manufacturing focusing on failure analysis and reliability. He is passionate about applying his unique background to enable his clients to maximize and accelerate product design and development while saving time, managing resources, and improving customer satisfaction.

•        Greg, a Lead Consulting Engineer for Ansys, is an industry recognized expert in the fields of SMT, advanced packaging, printed board fabrication, circuit card assembly, and bonding solutions using nanotechnology. He has been well-regarded as a leader in the electronics contract manufacturing and component packaging industries for the past 50 years. Prior to joining Ansys Greg was the Vice President of Engineering at Reactive Nanotechnology (RNT), where he led application development for the RNT Nanofoil® and ensured a successful transition of product technology to Indium Corporation. His previous appointments include Vice President of Business Development for Newport Enterprises, Director of Engineering for VirTex Assembly Services, and Technical Director at Silicon Hills Design. He has presented over 270 papers at conferences all over the world and has taught courses at IMAPS, SMTA and IPC events.  He helped design the 1st pick and place system used exclusively for SMT in 1978, edited and co-authored the 1st book on SMT in 1984 for ISHM and built the 1st SMT electronics launched into space.  Look for his new book entitled “Design for Excellence in Electronics Manufacturing” to be published in April 2021.

•        B.A., Management (St. Edwards University)

•        B.S., Electrical Engineering (Rutgers University)

On 14th of January 2021, Bob Deysher presented: ASQ RRD Series: Auditing ISO 9001 Clause 8.3, Design and Development of Products and Services (Risk Based Thinking)

Internal auditing is a requirement in ISO 9001:2015. Its purpose is to assess if processes are maintained and effective. This webinar will review how internal auditing, as well as the use of risk based thinking (RBT), can add value to the process of design and development of product and services. Focus will be on project reviews as well as verification and validation requirements providing examples of areas that had shown prior weaknesses. It is also recognized that industry specific standards such as aerospace (AS 9100) and automotive (IATF 16949) have added additional design and development requirements. These requirements will need to be audited if the organization registered to the industry specific quality management system.

1. Estimate the individual part failure rate given a base failure rate of  0.0333 failure/hour, a quality factor of 0.98 and an environmental stress factor of 0.92.

A. 0006

B. 0.300                      

C. 0.027                   

D. 0.0300

2. Consider a three component independent series system. The component reliabilities are described according to the following:

Component (1): Weibull (β = 1.6 η = 9,500)

Component (2): Exponential (λ = 0.000087)

Component (3): Lognormal (μ = 7.5 σ = 0.81) .

The reliability of the system at 1,000 hours is most nearly:

A. 0.6846 

B. 0.9995 

C. 0.3155  

D. 0.7673

3. A failure reporting and corrective action system should ensure that all steps are taken to :

A. Determine responsibilities for failures.

B. Record costs associated with the corrective action.

C. Identify, investigate and analyze failures.

D. Define the goals of the FRACAS team.?

4. The injection mold for a manufacturing process requires periodic maintenance due to the failure of a forming mechanism. The failures occur at a constant rate of 0.0002 per hour and the repairs occur according to a constant rate of 0.0625 per hour. The steady state probability of the system being operational is most nearly:

A. 0.0032  

B. 0.9998  

C. 0.9968  

D. 0.9375

5. Monte Carlo simulation is being used by a manufacturer to determine the probability of mechanical failure of a new design. Which of the following are required to perform this analysis?

I. Computer‐generated random numbers

II.  Stress distribution

III. Strength distribution                                        

IV.  Environmental conditions

A.   I         

B. I, II, & III     

C. All of the Above    

D. II  &  III

6. What is the best description for a Reliability Program and Reliability Information?

A. Reliability Program is more effective without Reliability Information

B. Reliability Program has nothing to do with Reliability Information.

C. Reliability Program only collects Reliability Information.

D. Reliability Program utilizes Reliability Information to improve reliability.

7. Consider the following Reliability Block Diagram:

Success will occur even if a failure occurs in the following elements?

A. I & IV  

B. I & III  

C. II & III  

D.  I & II

8. An electronic system needs any 2 out of 4 serial power supplies to be operational.

What is the reliability of this 2 out of 4 power supply design? Assuming the switching has 100% reliability.

Each power supply has the same reliability of 95%. All power supplies are activated during system operation.

A. 0.8095    

B. 0.9095    

C. 0.9595    

D. 0.9995

9. The system reliability of an active redundant or parallel system:

A. is greater than the reliability of any subsystem.

B. is equal to the reliability of the ‘best’ subsystem.

C. decreases as more redundant subsystems are added to the system.

D. increases if the subsystem with the lowest reliability is removed.

10. Given a set of test data for estimating wear‐out reliability of a mechanical part design (assuming normal distribution of the data), which of the following factors is NOT required to obtain a reliability estimate at 90% confidence level?

A. Number of Degrees of Freedom (DF)

B. Standard deviation of the population

C. Standard error of the mean

D.  Z‐score for the specified confidence level