Reliability Matters

Reliability Matters is a podcast on the subject of reliability of circuit assemblies. Reliability "best practices" and success stories are discussed. This podcast features interviews with experts in the electronic assembly industry. All electronic production segments which effect product reliability are on the table. This includes contamination, coating, cleanliness assessment, inspection, building for harsh environments, reflow, printing, failure analysis, board fabrication, and much more. Your Host: Mike Konrad began his career in the electronic assembly equipment industry in 1985. Mike founded Aqueous Technologies in 1992 in response to the Montreal Protocol and the resulting international treaty banning most popular cleaning/defluxing solvents. Mike is an internationally known speaker on the subject of increasing reliability through contamination removal and cleanliness quantification techniques and procedures. Mike was awarded “Distinguish Speaker Status” with SMTA in 2018 and received the “Rich Freiberger Best of Conference Award” in 2019. Mike is a member of the SMTA Global Board of Directors where he is Vice President of Communications. Mike is also Vice President of Technical Programs for the Los Angeles / Orange County SMTA Chapter. Visit the Reliability Matters Podcast Website: https://www.reliabilitymatterspodcast.com

Reliability Matters is a education podcast hosted by Unknown Host, with 204 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Unknown Host hosts Reliability Matters, a education show with 204 episodes published.

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  Reliability Matters

  Good Adhesive, Bad Bond: David Dworak on the Hidden Causes of Adhesion Failure - Episode 199

  Aug 11, 202616 min

  Adhesive failures can be frustrating because the problem isn’t always obvious. The adhesive may have been properly selected, the part may look clean, and the process may seem under control. But weeks or months later, a coating lifts, an encapsulant separates, or a bond fails in the field. So what went wrong? In this episode of Reliability Matters I’m joined by David Dworak, Material Scientist at Dymax, to discuss the science behind reliable adhesion in electronics manufacturing. We’ll talk about why adhesive bonding is about much more than choosing the right material. David explains the role of adhesive chemistry, surface energy, wetting, contact angle measurement, surface preparation, plasma treatment, and why even a visually clean surface may not be ready for bonding. We’ll also explore how manufacturers can troubleshoot adhesion failures, control their bonding process, and gather objective evidence that a surface is truly ready for adhesive application. If your process involves conformal coatings, encapsulants, underfills, component bonding, or any adhesive used in electronics assembly, this conversation will help you better understand what really makes a bond reliable. Dymax https://dymax.com “The Principals of Adhesion: Understanding Adhesive Chemistry, Application and Surface Science for Optimal Bonding Performance.” https://circuitsassembly.com/ca/editorial/menu-features/43078-the-principals-of-adhesion-understanding-adhesive-chemistry-application-and-surface-science-for-optimal-bonding-performance.html

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  Reliability Matters

  Why Your Al Training Isn't Sticking: Sean Patterson on Making Al Useful in Manufacturing - Ep. 198

  Jul 21, 20261h 9m

  Artificial intelligence is everywhere right now. It’s in the headlines, it’s being built into software tools, and it’s showing up in more and more conversations about engineering, manufacturing, quality, and process improvement. But here’s the problem. A company can buy the software. A team can attend the training. Everyone can walk away impressed by what AI might be able to do. And then, a few weeks later, very little actually changes. The tool is still there. The potential is still there. But the habits never formed. And that matters directly to reliability. In electronics manufacturing, reliability doesn’t come from good intentions. It comes from repeatable processes, disciplined execution, clear documentation, good decision-making, and the ability to recognize problems before they become failures in the field. That’s where AI can become more than just another interesting tool. Used properly, AI may be able to enhance workflows such as: • Reviewing ECOs and identifying downstream process, documentation, material, or inspection impacts • Analyzing AOI, SPI, test, and process data to detect recurring defect patterns • Supporting root cause analysis by organizing failure data, inspection results, and corrective action history • Monitoring process drift in areas such as reflow, cleaning, stencil printing, placement, and environmental conditions • Assisting with corrective action reports, customer responses, and internal documentation • Capturing tribal knowledge from meetings, shift reports, troubleshooting notes, and past problem-solving activity Those are all reliability-related activities. They touch the way products are designed, built, inspected, documented, corrected, and improved. But AI only helps if it becomes part of the way work actually gets done. My guest today is Sean Patterson, author of the printed circuit design and fabrication article, “Why Your AI Training Isn’t Sticking.” Sean argues that AI adoption is not really a training problem. It’s a habit problem. In other words, the goal isn’t simply to teach people what AI can do. The goal is to connect AI to the work people are already doing, such as preparing for meetings, reviewing engineering change orders, working through technical questions, or organizing complex problems. For those of us focused on reliability, that distinction is important. AI doesn’t replace engineering judgment, process knowledge, or verification. But when used properly, it may help strengthen the behaviors that support reliability: asking better questions, capturing knowledge, reducing overlooked details, and improving the consistency of technical decision-making. So today, we’re going to talk with Sean about why AI training often fails to stick, how small triggers can turn occasional use into practical habits, where AI can fit into engineering and manufacturing workflows, and how companies can approach AI in a way that is useful, responsible, and directly connected to reliability. CrossGen AI https://crossgen-ai.com Why Your AI Training Isn’t Sticking https://circuitsassembly.com/ca/editorial/menu-features/43156-why-your-ai-training-isn-t-sticking.html

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  Closing the Gap Between Validation and Reality with Dr. Nishith Kumar Reddy Gorla - Episode 197

  Jul 6, 20261 hour

  What if passing the test is not enough? In reliability engineering, we often place a great deal of confidence in laboratory validation. We test, measure, analyze, and document. And when the product passes, we naturally assume it is ready for the field. But what happens when a product passes the lab test and still fails in the hands of the customer? Today, we are going to explore the gap between laboratory reliability testing and actual field performance. It is a gap that can be costly, frustrating, and sometimes difficult to explain. Products can perform well under controlled test conditions, yet still experience unexpected failures when exposed to real users, real environments, real service conditions, and real-world variability. My guest today is Dr. Nishith Kumar Reddy Gorla of CORE ITS LLC. Dr. Gorla’s work focuses on system-level reliability testing, field reliability alignment, reliability growth, and test-to-field correlation for complex products. His recent paper, “System-Level Test Case Design for Field Reliability Alignment in Complex Products,” looks at why traditional reliability test methods may miss important field failure modes. More importantly, it proposes a broader approach to test case design that considers not just the product design, but also actual use conditions, field failure data, end-user behavior, service procedures, manufacturing variation, consumables, and product interfaces. This is an important conversation because reliability is not simply about passing a test. It is about understanding how a product behaves in the real world, where variables interact, users behave unpredictably, environments change, and failure mechanisms may not appear until the system is challenged in realistic ways. If you design, build, test, specify, or depend on complex products, this discussion should be especially relevant. Today, we will talk about why products sometimes pass in the lab but fail in the field, how better system-level test cases can improve field correlation, and what reliability professionals can do to create test methods that more accurately reflect real-world performance. Dr. Gorla's Article: https://www.reliabilitymatterspodcast.com/article-1-gorla

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  If We Don't Shape Al, Al Will Shape Us - Dr. James Maisiri on Responsible Technology Adoption # 196

  Jun 23, 202648 min

  Today, we’re going to explore a topic that seems to be touching nearly every industry, every workplace, and increasingly every part of our daily lives: artificial intelligence. But this conversation is not just about what AI can do. It’s about what AI assumes, what it values, what it misses, and what happens when powerful technologies are deployed into environments they were not designed to understand. My guest today is Dr. James Maisiri, an AI researcher and writer whose work focuses on responsible AI, digital transformation, education, labor markets, and the societal impact of emerging technologies. His work has been featured through organizations and publications including UNESCO, Mail & Guardian, and others, and his message is both simple and profound: AI is not neutral. In one of his 3 TEDx talks, Dr. Maisiri said, ‘If Africa does not shape AI, then AI will shape Africa. While his work often focuses on African communities and institutions, the lesson is much broader. Every industry, including electronics manufacturing, needs to think carefully about how we adopt AI, what assumptions are built into these systems, and how we validate their use before trusting their output. In electronics reliability, we often say that context matters. Materials, environments, residues, humidity, temperature, field conditions, and use cases all influence performance. Today, we’ll ask whether the same is true for AI. I’m not really sure how much of this conversation will tie in directly to reliability, but it’s a fascinating subject nonetheless. Let’s see where it takes us. The hidden danger of AI in Africa | James Maisiri | TEDxUniversity of Johannesburg https://www.youtube.com/watch?v=y1BVZvjLevQ How AI and Robotics Reconnects Us to Humanity https://www.ted.com/talks/james_maisiri_how_ai_and_robotics_reconnects_us_to_humanity LinkedIn: https://www.linkedin.com/in/dr-james-maisiri-7b8069281/

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  The Return of Cleaning: Why Post-Reflow Cleaning Is Becoming Mainstream Again (Part 2) - Episode 194

  Jun 9, 202611 min

  This is part two of a two-part series. In Part One, we explored how the electronics industry transitioned from a clean-everything approach to one where cleaning became optional. But what happens when the assumptions behind “no-clean” collide with modern electronics design? In this episode of Reliability Matters, Mike Konrad examines how the definition of cleanliness has fundamentally changed. As assemblies became smaller, denser, and increasingly deployed into harsh environments, the industry discovered that historical cleanliness standards were no longer sufficient to predict real-world reliability. Modern low stand-off components like QFNs, BGAs, and CSPs create tight geometries where residues can become trapped and difficult to remove, while thermal cycling and internal condensation can create localized harsh environments inside the product itself. This episode explores: • Why IPC moved away from fixed cleanliness limits • The growing importance of SIR and ROSE testing • Why “cleanliness” is now tied to risk, not a number • How internal condensation can trigger electrochemical migration • Why no-clean flux has become the most commonly cleaned flux type in the industry • The return of cleaning as a mainstream reliability process • Why modern assemblies require aggressive spray-in-air cleaning technologies instead of historical immersion-based vapor degreasing methods • How diffused spray patterns improve cleaning beneath low stand-off components Mike also explains how modern cleaning challenges are no longer just about chemistry. They are about physics, fluid delivery, and whether the cleaning process can physically reach contamination hidden beneath today’s densely packed components. As electronics continue to shrink and reliability expectations continue to rise, one question becomes increasingly important: Clean enough for what? If you work in electronics manufacturing, reliability engineering, process engineering, or quality assurance, this episode provides a detailed look at why post-reflow cleaning has once again become a critical part of modern electronics manufacturing.

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