Process Reliability Analysis

How could we increase process reliability and tool-life by selecting 'feeds and speeds' based on an improved understanding of the fundamental process physics? What relative roles do thermal and mechanical loads play when it comes to predicting tool-life? The significant effect of cutting speed on tool-life has long been well understood to be the result of increased cutting temperatures at increasing speeds (i.e., Taylor's law, established back in the early 20th century). However, wear that occurs due to mechanical loading introduced by cutting parameters such as feed rate and depth of cut has proven to be more challenging to predict, as there are many different variables and complex wear mechanisms at play. Last week I proudly shared by Ph.D. student Avery Hartley's and undergraduate researcher Jenna Money's recently published paper: https://lnkd.in/ghWWGpqE This week, I wanted to share a summary figure of this paper to better illustrate the key findings of our work. As you can see in the central part of the figure, the fatigue strength of the tungsten carbide (WC) tool material decreases according to a power law with increasing fatigue cycles (log x-axis). Notice that there is a mean fatigue strength (black lines), as well as substantial standard deviations to this strength (green and red lines). This is a common feature for brittle materials, which are highly sensitive to local microstructural flaws. Our most relevant finding was that the fatigue loads imposed due to serrated chip formation (blue data points) vary dramatically with feed rate. As can be seen in the two image sequences (top: 100 microns feed; bottom: 22 microns feed), the higher feed rate has approximately twice the amount of peak displacement (~elastic stress) and variance across a given chip formation cycle. What does all of this mean for machinists trying to optimize 'feeds and speeds'? As illustrate by the red graphic on the far right, the chance that the cutting edge may experience fracture/chipping increases with time (y-axis) but is more than 4x higher at 100 micron feed rate than at 22 microns after a minute of cutting. Therefore, in order to decrease the chance of 'random' edge failure, a lower feed rate along with increased depth or cut, as well as some tailored cutting edge preparation, could substantially increase process reliability. Let me know what your thoughts are!

💡 "How can you ensure your process is fit for purpose?" Imagine you’re an engine manufacturer relying on precision for piston rings. Even a small deviation could mean the difference between a high-performance engine and a catastrophic failure. That’s where a Six Pack Analysis in Minitab comes to the rescue. Let me show you how! 🚗 Case Study: Evaluating Piston Ring Quality In this real-world scenario, quality engineers set out to assess the capability of their forging process. Here’s what they did: 1️⃣ Collected Data: 25 subgroups of 5 piston rings each Measured their diameters Specifications: 74.0 mm ± 0.05 mm 2️⃣ Objective: Verify if the process produces piston rings within specification limits. Check if the data assumptions for normal capability analysis hold true. 3️⃣ Method: Using Minitab, the team performed a Normal Capability Six Pack Analysis, generating six critical insights: Stability through X-bar and R Charts 🟦 Process distribution and specification fit via Histograms 📊 Normality check with Probability Plots ⚡ Key capability indices like Cp, Cpk, Pp, and Ppk. 🔍 What Did They Learn? The Six Pack Analysis revealed whether the forging process was capable of consistently meeting the tight specification limits. It also pinpointed areas to improve stability and centering to optimize process performance. 🛠 Takeaway: The Six Pack isn’t just for fitness—it’s a powerful tool to diagnose and improve your process health! Whether you’re in manufacturing, healthcare, or tech, understanding your process capability can save costs, improve quality, and enhance customer satisfaction. 📢 Ready to give your processes a health check? Let me know how you assess capability in your work, or drop a comment if you'd like more examples like this one!

FMEA: Empowering Risk Mitigation and Process Excellence In a rapidly evolving industrial landscape, where quality, reliability, and efficiency are paramount, Failure Mode and Effects Analysis (FMEA) stands as a cornerstone methodology in ensuring robust systems and processes. What is FMEA? FMEA is a structured, systematic approach for identifying potential failure modes in a process, product, or system and analyzing their impact. By evaluating the severity, occurrence, and detectability of these failures, teams can prioritize actions to mitigate risks before they escalate into costly problems or safety hazards. The process involves the following key steps: 1. Defining the Scope: Establishing the boundaries of the analysis – whether it’s for a product design, manufacturing process, or service delivery. 2. Identifying Failure Modes: Brainstorming and listing all possible ways a component or process could fail to perform its intended function. 3. Assessing Risk: Using a Risk Priority Number (RPN) to quantify and rank risks based on severity, likelihood of occurrence, and ease of detection. 4. Implementing Mitigations: Developing and applying corrective actions to address high-priority risks, reducing their impact and frequency. 5. Monitoring & Updating: Continuously refining the analysis to reflect changes in design, process improvements, or new insights. Why Does FMEA Matter? Proactive Problem Solving: FMEA allows organizations to address issues during the design or planning phase, reducing downstream costs and delays. Enhanced Safety and Compliance: By anticipating and mitigating risks, FMEA ensures adherence to industry standards and protects stakeholders. Improved Customer Satisfaction: Delivering reliable products and services builds trust and strengthens brand reputation. Cross-Functional Collaboration: FMEA fosters teamwork across departments, leveraging diverse expertise to uncover hidden risks. Applications Across Industries Manufacturing: Identifying process bottlenecks and ensuring quality in production lines. Automotive: Enhancing the reliability and safety of components, from engines to electronics. Energy: Ensuring the durability of systems in power plants and renewable energy projects. FMEA in the Era of Digital Transformation As industries embrace Industry 4.0 technologies, FMEA is evolving alongside. Tools like AI, IoT, and big data analytics are enhancing FMEA's predictive power, enabling real-time monitoring of systems and rapid identification of potential failures. For example, predictive maintenance systems can integrate FMEA findings to preempt equipment failures, reducing downtime and extending asset life. Similarly, AI-driven algorithms can analyze historical data to refine risk assessments, making FMEA more dynamic and precise. #FMEA #RiskManagement #ContinuousImprovement #QualityAssurance #OperationalExcellence #LeanManufacturing #Engineering

6 Steps to Process FMEA 🎯 What is PFMEA? Process Failure Mode and Effects Analysis (PFMEA) is a structured methodology used to identify, assess, and mitigate potential risks in manufacturing and service processes. It helps organizations enhance process reliability, reduce defects, and improve overall quality. Let’s break down the 6 key steps of PFMEA with an example from an automotive assembly line where a robotic welding process is used to join car body panels. 🔹 Step 1: Planning and Preparation Objective: Analyze the welding process to reduce defects and ensure strong weld joints. Actions: Assemble a cross-functional team (engineers, quality experts, operators), define the scope, and collect relevant process data. 🔹 Step 2: Structure Analysis Example: Create a process map showing each step of the welding process—positioning panels, applying heat, forming welds, and cooling. 🔹 Step 3: Functional Analysis Example: Identify key functions such as: Proper alignment of panels before welding Correct temperature and pressure application Adequate cooling time to ensure joint strength 🔹 Step 4: Failure Analysis Potential Failure Modes: Misalignment of panels before welding Incomplete welds due to incorrect heat application Weak welds due to insufficient cooling Effects: Structural weakness Higher rejection rates Safety hazards 🔹 Step 5: Risk Analysis Each failure mode is rated based on: ✔ Severity (S): Impact on product quality & safety (e.g., misalignment = 9/10) ✔ Occurrence (O): Likelihood of failure happening (e.g., incomplete weld = 5/10) ✔ Detection (D): Probability of detecting failure before shipping (e.g., weak welds = 6/10) ✒️ Risk Priority Number (RPN) = S × O × D Higher RPN values indicate critical risks that need immediate action. 🔹 Step 6: Optimization Corrective Actions: ✅ Install precision fixtures to prevent misalignment ✅ Implement automated sensors for heat monitoring ✅ Improve cooling process using optimized cycle times 💡 Why is PFMEA Important? By following these steps, the manufacturer reduces weld defects, improves product quality, and ensures safety—preventing costly recalls and production downtime. 📢 Have you used PFMEA in your industry? What challenges or successes have you experienced? Let’s discuss in the comments! ====== 🔔 Consider following me at Govind Tiwari,PhD. #PFMEA #QualityManagement #RiskManagement #ProcessImprovement #Manufacturing #ContinuousImprovement #quality #iso9001 #qms