Engineering Quality Assurance Methods

A $12 prototype can make $50,000 of engineering analysis look ridiculous A team of engineers was stuck on a bearing failure analysis for six weeks. Vibration data, FFT analysis, metallurgy reports - they had everything except answers. The client kept asking for root cause and the engineers kept finding more variables to analyze. Temperature gradients, load distributions, contamination levels, manufacturing tolerances. Each analysis created more questions. Then the intern did something that made the engineers feel stupid. She 3D printed a transparent housing and filled it with clear oil so the engineers could actually see what was happening inside the bearing assembly. Took her four hours and $12 in materials. They watched the oil flow patterns and immediately saw the lubrication wasn't reaching the critical contact points. All their sophisticated analysis was based on assuming proper lubrication distribution. Wrong assumption. Six weeks of wasted effort. The visual prototype didn't just solve the problem - it changed how the engineers approach these types of investigations. Now they build crude mockups before diving into analysis rabbit holes. Cardboard, tape, clear plastic, whatever works. Physical models force you to confront your assumptions before you spend weeks analyzing the wrong thing. Sometimes the cheapest prototype teaches you more than the most expensive simulation. #engineering #prototyping #problemsolving

The Secret to China and Tesla Speed? - ASPICE. (It’s their gold standard) Not The truth - software engineers don’t care for ASPICE. They see it as a burden. ASPICE is vague, outdated, and written in a language software engineers don’t use. Ask software engineers what the gold standard is, and they’ll say: ➜ DevOps, CI/CD, Shift-Left (TDD/BDD) ➜ Automate everything, exploratory testing ➜ Microservices, contract-first design Yet ASPICE, the so-called “automotive software engineering gold standard,” doesn’t even mention these terms. Instead, it defines required work products like: ➜ Review record, change history, configuration item As if we’re still in the 90s, storing printed change logs in physical ledgers. But today? ➜ We work cloud-based, collaboratively. ➜ Changes take minutes, everything is versioned. ASPICE Isn’t Fundamentally Wrong—It’s Just Stuck in the Past. The real issue? Software engineers hate it. Quality managers love it. It was designed by OEMs to control big-budget waterfall projects at suppliers— Not to empower high-speed, iterative software engineering. The goal back then? Minimize risk. Control. Assign blame. The goal today? Speed. Relentless testing. Transparency. Trust. Lightning-fast iterations. “The Engineers Are Right.” The methods and tools must suit them. If you want great software, you need happy engineers. That should be Quality’s priority. But in my experience? ➜ Quality managers say, “Engineers must understand ASPICE.” ➜ They should be saying, “How do we make ASPICE work for engineers?” Quality Should Enable Engineers—Not Slow Them Down. Instead of forcing rigid, outdated processes, quality should focus on: ➜ Automated compliance → Integrated into CI/CD, not manual bureaucracy. ➜ GenAI-enabled tooling → Seamless, intuitive, guiding engineers without friction. ➜ Process guardrails that feel invisible → Supporting, not obstructing. It’s no mystery. Look at Tesla’s DSM (Digital Self-Management). Quality at Tesla isn’t about checking boxes—it’s about removing friction so engineers can move fast. But what I see most of the time? ➜ Quality creates processes that make quality managers happy. ➜ Engineers just work around them. That’s the real problem. What’s the fix? ⤷ Quality needs to evolve—or get out of the way. What’s your thoughts? What’s your gold standard in automotive software development?

As an Engineering Manager or Software Engineer, your brain is your greatest asset. Here's how to keep it sharp, not shredded: 𝟭/ 𝗥𝘂𝘁𝗵𝗹𝗲𝘀𝘀 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Start by slashing your to-do list. If it’s not moving the needle, it’s moving you backward. Adopt the 80/20 rule: 20% of your actions drive 80% of your results. Identify those actions. Focus there. Relentlessly. 𝟮/ 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗕𝗹𝗼𝗰𝗸 𝗦𝘆𝘀𝘁𝗲𝗺 Carve your day into power blocks. Three hours in the morning. Two in the afternoon. During these blocks, your only job is to produce your highest value work. No emails. No calls. Pure, undiluted focus. 𝟯/ 𝗧𝗵𝗲 𝗔𝗿𝘁 𝗼𝗳 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗟𝗮𝘇𝗶𝗻𝗲𝘀𝘀 Yes, laziness - but make it strategic. Schedule your downtime. Your brain isn’t wired to go full-throttle 24/7. It needs rest to process and recharge. Take a walk. Meditate. Nap. High performance isn’t a sprint; it’s a well-paced marathon. 𝟰/ 𝗧𝗵𝗲 𝗧𝘄𝗼-𝗠𝗶𝗻𝘂𝘁𝗲 𝗥𝘂𝗹𝗲 If a task takes less than two minutes, do it immediately. This simple rule keeps small tasks from piling up and turning into mental clutter. Keep your mind clear for the big plays. 𝟱/ 𝗧𝗵𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗟𝗼𝗼𝗽 Dedicate 30 minutes daily to learning. Read. Listen. Watch. High performers are perpetual students. Oh and remember, Knowledge X Action = Power. 𝟲/ 𝗧𝗵𝗲 𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗥𝗶𝘁𝘂𝗮𝗹 End your day with 10 minutes of reflection. What worked? What didn’t? High performance is about constant iteration. You can’t improve what you don’t measure. 𝟳/ 𝗦𝗹𝗲𝗲𝗽 𝗟𝗶𝗸𝗲 𝗮 𝗣𝗿𝗼 Lastly, and most importantly, prioritize sleep. Your brain’s ability to problem-solve, innovate, and think critically is directly tied to your sleep quality. It’s not a luxury; it’s a necessity. 7-9 hours (Non-negotiable) 𝗜𝗻 𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻: The goal is not to be busy but to be impactful. Redefine your routine, redefine your life. --- Repost if you found this useful. Thank you.

✨ 70% of chip design time goes into Verification. Now imagine AI cutting that time in half 😀 Working as a Design Verification Engineer at Google, I can clearly see how AI is rapidly reshaping VLSI. What once felt like a distant future is already becoming part of our everyday workflow. Today, the industry already has tools that can: 🔹 Generate RTL code directly from specification documents or architecture diagram. 🔹 Auto-create SystemVerilog/UVM testbenches from high-level inputs 🔹 Use ML to analyze coverage gaps and suggest corner-case tests 🔹 Assist in debugging waveforms and highlight potential root causes 💡 The big shift: Verification engineers will spend less time on repetitive coding and more on guiding AI, validating results, and applying domain expertise. Even in my own work, I don’t remember a single day in last month where I haven’t used some form of AI tools 🧠 Of course, the best AI tool really depends on what you need: some are great for coding, some are best for Circuit diagrams, while a few are better suited for documentation and writing. The key is to mix and match based on your requirement. Beyond popular tools like ChatGPT, Gemini, or Perplexity, here are some AI tools I’ve found particularly useful in Design Verification & VLSI : a) Claude AI – https://claude.ai/new b) Cursor AI – cursor.com/agents c) Bronco AI – https://www.bronco.ai/ 🚀 The pace of change is incredible. AI isn’t just “supporting” verification anymore – it’s starting to reshape how we design and verify chips. 👉 Curious to know in the comments: Which AI tools do you find most effective in your workflow? #VLSI #Semiconductor #Google

What if the best solutions for your process started with cardboard? When testing new ideas or improvements, jumping straight to high-cost, permanent solutions can be risky—and expensive. That’s where cardboard engineering comes in. Cardboard is one of the simplest, most cost-effective tools for rapid prototyping and testing ideas. It’s lightweight, easy to shape, and lets you visualize, test, and refine your concepts before committing to more expensive materials. Why Cardboard Is Perfect for Prototyping: 1️⃣ Low-Cost Experimentation Testing with cardboard lets you try multiple iterations of a design without worrying about material costs. 2️⃣ Fast Feedback Loops You can build and modify a prototype in minutes, gathering instant feedback from your team or operators. 3️⃣ Hands-On Collaboration Cardboard prototypes allow teams to actively engage with ideas, making it easier to identify issues or opportunities for improvement. 4️⃣ Visual Validation Sometimes, seeing a physical model highlights challenges that wouldn’t be obvious in a drawing or plan. How to Use Cardboard for Lean Improvements: 🔍 Test Workstation Layouts Use cardboard cutouts to mock up layouts and placement of tools, parts, and equipment. Adjust until everything flows smoothly. 📦 Simulate Material Flow Prototype racks, bins, or carts to ensure materials are stored and moved efficiently before building them with more durable materials. 🛠️ Design Fixtures or Jigs Create cardboard versions of fixtures or jigs to test their functionality in the process. Refine the design before investing in the final version. 📐 Test Ergonomics Mock up equipment or workstation designs with cardboard to test ease of use, reach, and operator comfort. Example of Cardboard in Action: A manufacturing team wanted to redesign a workstation to reduce operator motion. Instead of committing to expensive reconfigurations, they used cardboard to prototype the layout. After several iterations, they found the optimal setup, reducing motion by 25% and saving hours of work. Cardboard isn’t just for packaging—it’s a powerful tool for testing and refining your ideas. By prototyping with low-cost materials, you can experiment, learn, and improve quickly without breaking the bank.

Handling corroded steel structures carrying heavy load. Normally industries prefer steel structure to house their production and infrastructure units. They take less time for fabrication and erection, they are stubborn and strong as compared to concrete structures and they are also convenient for maintenance. However when they are corroded or start losing their structural parameters, then they are left with couple of options. One is to replace them and another is to rehabilitate them. Replacing them is a convenient option but it calls for stopping all the activities happening over the structure which might be a loss of production for a process industry. It is also a part of depletion of natural resource - metal ore' which must be avoided. When the option of their rehabilitation is sought, the first and most important activity in this direction is to carry out the actual assessment of damages by non destructive or partial destructive method. These methods are listed below. 1.     Ultrasonic thickness measurement test, This test measures actual thickness of the section which can be compared with the original thickness and reduction in thickness if any – can be known. 2.     Magnetic particle test, This test indicates disturbance in micro particles of the metal matrix if any which can again be compared with the original molecular structure of the metal member to arrive at the variance if any. 3.     Microstructure analysis, Here the metal member is observed under 600x magnification under radioactive source which helps to know the granular structure of the metal member. 4.     Dye Penetration test, This test highlights crevices and fishers if developed within the metal member. 5.     Themography test, This is an advance test which indentifies hot zone and cold zone within metal matrix. The thermal representation of test result can be correlated with internal damage of metal member in lamination pattern. 6.     Radiography. As the name suggests, radioactive waves are passed through metal member to know complete details of internal structure of the metal member. This test can cause radioactive infection so it is done within ‘no man’s land’. Test results are then to be comprehensively interpreted and evaluated to arrive at the deficiency developed within the metal member. More often than not the help of reverse engineering is taken to quantify the deficiency and actual parameters in which augmentation required to be done. Accordingly the rehabilitation scheme for corroded or under-performing structural steel structure can be designed and executed at site. Pictures of highly corroded / deteriorated structures tell everything about the possible damages.

Enhancing Reliability in EV Power Electronics: #FMEA for Traction Inverter Design ⚡🚗 In electric vehicles (EVs), the traction inverter plays a crucial role in converting DC #battery power into AC power for the electric motor. A failure in this system can lead to power loss, reduced efficiency, or even vehicle breakdown. To ensure reliability and performance, we use Failure Modes and Effects Analysis (FMEA) to identify and mitigate potential failures in the EV inverter system. 📌 FMEA considers: ✔ Severity (S) – Impact of failure (1 = low, 10 = critical). ✔ Occurrence (O) – Likelihood of failure happening (1 = rare, 10 = frequent). ✔ Detection (D) – How easily the failure can be detected (1 = easily detectable, 10 = undetectable). ✔ Risk Priority Number (RPN) = S × O × D – A score to prioritize risks. 🔴 Key Failure Modes in EV Traction Inverter 🔹 IGBT/MOSFET Short Circuit → Overcurrent, overheating, potential powertrain shutdown. ⚠️ S = 10 | O = 4 | D = 3 | RPN = 120 👉 Mitigation: Advanced short-circuit protection, thermal monitoring, robust gate driver design. 🔹 IGBT/MOSFET Open Circuit → No power transfer to the motor, loss of acceleration. ⚠️ S = 9 | O = 3 | D = 3 | RPN = 81 👉 Mitigation: Redundant power paths, fault detection circuits. 🔹 Gate Driver Malfunction → Incorrect switching, increased losses, reduced efficiency. ⚠️ S = 9 | O = 5 | D = 4 | RPN = 180 👉 Mitigation: Shielding against EMI, optimized PCB layout, reliable driver components. 🔹 DC Link Capacitor Degradation → Higher voltage ripple, increased heat, reduced motor performance. ⚠️ S = 8 | O = 5 | D = 4 | RPN = 160 👉 Mitigation: High-quality capacitors, active cooling, periodic diagnostics. 🔹 DC Link Capacitor Short Circuit → Inverter shutdown, potential vehicle breakdown. ⚠️ S = 10 | O = 3 | D = 3 | RPN = 90 👉 Mitigation: Overvoltage protection, pre-charge circuit, high-reliability capacitors. 🔹 Control Board Software Failure → Incorrect switching signals, unstable power delivery, or sudden inverter failure. ⚠️ S = 9 | O = 4 | D = 5 | RPN = 180 👉 Mitigation: Watchdog timers, redundant safety logic, secure software updates. 🔹 Temperature Sensor Failure → No thermal protection, leading to possible overheating and failure. ⚠️ S = 9 | O = 4 | D = 3 | RPN = 108 👉 Mitigation: Redundant sensors, real-time thermal diagnostics. 🔹 Cooling System Failure (Liquid Cooling/Pump Malfunction) → Excessive heat buildup, inverter derating, or failure. ⚠️ S = 10 | O = 5 | D = 4 | RPN = 200 👉 Mitigation: Preventive maintenance, thermal shutdown features, and redundant cooling circuits. Why FMEA is Critical for EV Inverters ✅ Ensures safety and reliability in electric drivetrains. ✅ Improves efficiency and thermal management for long-term operation. ✅ Reduces risk of breakdowns and increases vehicle lifespan. As #EV adoption grows, traction #inverter must be designed for high performance and durability under real-world conditions.

I would like to introduce some useful things for solar panel Testing: ⚡ Solar Panel Testing: What We Check Before Procurement & Installation Before any solar panel hits the field, rigorous testing is essential. Here's a detailed breakdown of the key tests and standards we perform to ensure top-tier quality, performance, and long-term reliability. ✅ 1. Flash Test (I-V Curve under STC) 📌 Purpose: Measures actual electrical performance under Standard Test Conditions (STC) 📊 STC Parameters: 1000 W/m² irradiance 25°C cell temperature Air Mass 1.5 🔍 Key Checks: Pmax (Maximum Power): Must be within ±3% of rated capacity Voc (Open Circuit Voltage) & Isc (Short Circuit Current): Should show tight consistency between modules 💡 Why it matters: Verifies that real output matches the manufacturer’s datasheet—no surprises after installation. ✅ 2. NOCT – Nominal Operating Cell Temperature 📌 Purpose: Predicts real-world performance under actual outdoor conditions 📊 Typical Conditions: 800 W/m² irradiance 20°C ambient temp 1 m/s wind speed 🎯 Ideal Range: 42°C – 48°C 💡 Why it matters: Lower NOCT = less heat = better energy yield in the field. ✅ 3. Electroluminescence (EL) Imaging 📌 Purpose: Reveals hidden cell-level defects 🔬 Method: Apply low voltage in darkness to produce infrared emission 🔍 Detects: Microcracks Broken cells Soldering faults 💡 Why it matters: Early detection prevents hotspots, power loss, and premature failure. ✅ 4. Insulation Resistance & High-Voltage Withstand Test 📌 Purpose: Ensures electrical safety and system durability 📊 Test Voltage: 1000–1500V DC, depending on system design 🎯 Minimum Resistance: >40 MΩ at 1000V (per IEC 61730) 💡 Why it matters: Critical for shock prevention, fire safety, and long-term reliability. ✅ 5. PID (Potential Induced Degradation) Test 📌 Purpose: Assesses vulnerability to voltage-induced performance loss 📊 Test Conditions: ~85°C 85% RH -1000V applied for 96–168 hours 🎯 Degradation Threshold: <5% power loss 💡 Why it matters: Vital for high-voltage and humid-climate installations. ✅ 6. QAP (Quality Assurance Plan) Review 📌 Purpose: Evaluates the manufacturer’s internal QA processes 📝 What We Verify: ISO Certifications (e.g., ISO 9001) Recent factory audits Random sampling results (IEC 61215 / 61730) Raw material traceability 💡 Why it matters: Adds confidence beyond lab tests—ensures production consistency and traceability. ✅ 7. Thermal Cycling & Damp Heat Test 📌 Standard: IEC 61215 📊 Test Parameters: Thermal Cycling: 200 cycles from -40°C to +85°C Damp Heat: 1000 hours at 85°C / 85% RH 🎯 Acceptable Loss: <5% degradation 💡 Why it matters: Demonstrates durability in extreme environments (deserts, tropics, snow zones). ✅ 8. Visual Inspection 📌 What We Check: Glass cracks Delamination Frame warping Junction box damage Edge sealing & backsheet integrity 💡 Why it matters: Catching cosmetic or structural issues early prevents installation delays and long-term performance risks.

LLMs are just stateless functions. If you want something dependable, it’s all about how you engineer the wrapper. In this issue, I share some lessons and patterns I’ve found (or learned from others) that help LLM-based agents go from flaky to functional: --- Why the “magic” is in the loop, not the model --- How to think about tool use, error handling, and context windows --- The value of owning your control flow --- Why smaller, focused agents usually win ... I also included one of our open-sourced projects: GenAI Agents Infrastructure - a lightweight setup we’ve been using internally to run LLM agents. You’ll find the GitHub link inside, give it a try and let me know how it goes! ___________ Welcome to the Learn AI Together newsletter — 90% buzzword-free and focused on learning materials & news in AI. Let’s grow together! Alex Wang

𝗛𝗼𝘄 𝘀𝘁𝗿𝗼𝗻𝗴 𝗶𝘀 𝘆𝗼𝘂𝗿 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗱𝗲𝗳𝗲𝗻𝘀𝗲? Quality failures are a major cause for Supply Chain incidents. But they don’t happen overnight. Issues and incidents slip through gaps in systems, processes, and people, bypassing layers of risk defense. The 𝗦𝘄𝗶𝘀𝘀 𝗖𝗵𝗲𝗲𝘀𝗲 𝗠𝗼𝗱𝗲𝗹, introduced by James Reason in 1990, is a powerful way to visualize how a series of weaknesses can align, creating a “window of opportunity” for errors or harm. Problems occur when multiple layers of defense fail at the same time. In Procurement, quality and risk management require a layered approach. No single measure is enough to stop risks like supplier failures or product malfunctions. Thinking about Procurement, find here a reflection on the six dimensions of a quality defense system: 1️⃣ 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 & 𝗖𝘂𝗹𝘁𝘂𝗿𝗲 starts at the top. Leaders set the tone, embedding quality as a shared priorities for teams and suppliers alike. Without this commitment, the first layer of defense crumbles, leaving gaps for issues to pass through. 2️⃣𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘀𝗲𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 make results predictable and reduce risks. Having a good handle on process metrics and controls mitigate possible weaknesses and entry points for issues. Weak processes are like holes in the systems, creating a pathway for failure. 3️⃣𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗿𝗶𝗴𝗵𝘁𝘀 have a clear purpose. They enable quick decision-making based on well-defined roles & responsibilities. Slow or ambiguous decision processes allow small issues to escalate into large problems. 4️⃣𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 like those built on ISO 9001 ensure issue prevention, detection and continuous monitoring. Having this layer in place reinforces consistency, accountability and governance of operations. 5️⃣𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 & 𝗽𝗲𝗼𝗽𝗹𝗲 make quality happen. Clear roles, training, and collaboration tools empower effective actions. When everyone understands their accountability and purpose, risks are identified and resolved faster. 6️⃣𝗧𝗲𝗰𝗵 & 𝗱𝗮𝘁𝗮 provides the tracking mechanisms and insights to stay ahead. Relevant data, predictive analytics and performance metrics help teams to monitor risks and address issues before they escalate. Each of these six layers adds a critical line of defense. If one fails, the next must catch the issue before it impacts the customer. ❓How strong are your procurement defenses. ❓Where do you see gaps, and how can you strengthen your layers. #procurement #qualitymanagement #swisscheesemodel