Using Software for Manufacturing Data Analysis

Integrating Tool Design and Tolerance Analysis in Early Product Development   In my manufacturing producibility role, I work closely with tool designers to balance design requirements with build feasibility. A key part of this collaboration involves reviewing designs and tooling to ensure that locating features are properly identified and addressed. Ideally, the tooling should represent the next assembly and its interfaces.   Because tooling is a long-lead item, it must be designed and contracted for build very early in the product development cycle. Often, tool designers are provided only basic information—such as common datums and locating points—contained in a design coordination model.   Using this coordination model, tool designers conceptualize the tooling. Drawing on their experience, they create tooling designs based on the reference points and planes defined in the model. Meanwhile, structural designers are simultaneously developing the components that will be assembled, also using the same coordination model.   Tooling tolerances typically fall within ±0.003 inches, or about 30% of the component tolerance. Since many parts are not finalized when tooling design begins, full tolerance analysis may not be feasible. However, conducting tolerance analysis as early as possible can help identify requirements that reduce tooling costs or even eliminate the need for certain tooling.   Because design limits are defined through aero document and the coordination model, it’s possible to establish points that represent component features. These points can be used in tolerance analysis studies—provided the right analysis tool is available.   When using the right tolerance analysis tool design changes are no problem. Just update the analysis model and tolerance analysis can be recalculated. Also, by using points to represent features, multiple variants can be created and toggled on or off to validate different design concepts.   When critical requirements are identified, validation measures can be added to the model ensuring compliance. As I’ve discussed previously, virtual conditions and assembly orientation can also be analyzed.   Using Dimensional Control Systems (3DCS) software, tolerance analysis for tooling, components, and assemblies can be conducted early and with limited data. 3DCS uses points to represent features, size, and tolerance attributes. Through assembly simulation, model variants, and validation measures, 3DCS enables early-phase tolerance analysis that supports informed decision-making during product development.   Early integration of tooling and tolerance analysis is essential for manufacturability and cost efficiency. By leveraging coordination models and 3DCS, teams can simulate and validate designs—even with limited data—ensuring alignment between design intent and production capability from the start.

💡 "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!

Machine data is only part of the equation for digital operations; don't forget about the people, materials, and flow! I recently started experimenting with Tulip and AWS IoT SiteWise to better contextualize machine data, operator feedback, and context, as well as other operational data sources such as planning and scheduling. It's not enough to know the CNC Mill has a spindle speed of 4,000 RPM... The typical set of follow-up questions from most plant managers include: * Is it supposed to be running? * What work order is it running? * Who is running the machine? * Do they have what they need? To answer these questions, it's vital to contextualize machine data from the PLC alongside operator input and systems data (ERP, PLM, etc.). Otherwise, you only get half the picture of the state of operations. Tulip Integration: Connector Function: I experimented with using a Tulip Connector Function to write data to IoT SiteWise to add the operator context. I was also able to use the same Connector Function to query recent metrics from SiteWise. Tables API: For alerting, I was able to use a Lambda function to write data to Tulip via the Tulip Tables API. This data could include alerts on maintenance or quality as well as insights for the shop floor supervisor. Future Considerations: Adding more predictive analytics to this simple stack could build upon this feedback loop. Tools such as TwinThread could add to the compelling value proposition. Cost Notes: I assumed 100 machines per plant sending 5-10 data points per minute (More frequent data would be processed at the edge). * The cost for API Gateway and Lambda is pretty negligible. * The IoT SiteWise cost comes to about $1 - 1.5k per month but can vary based on data transformation and integration with other services. Overall, closed-loop feedback systems like this could really enable true OEE... and by that I mean Overall Employee Engagement and Overall Enterprise Effectiveness. ;) Let me know what you think and how you've explored closed-loop feedback systems in manufacturing. If there's interest, I can publish architecture details and the Tulip Connector details too!

Cycle Times Don’t Lie and Flying S Inc Unlocks Truth! Almost five years ago, I stepped into Flying S to talk about their first Matsuura Machinery USA, Inc. investment. Back then, automation was already humming along, pallet changers were multiplying, and the iconic “blue” machines were claiming their territory. But with growth comes a new challenge… Information OVERLOAD! Automation is powerful, but without data, it’s like flying blind. Recently, Flying S turned to Datanomix DataXchange to make sense of everything happening on the floor. When are machines running? Why do they stop? Is it an operator issue, an inspection bottleneck, a robot glitch, or simply Monday morning blues? These are the details that turn “surviving” in manufacturing into thriving. Kyle Myers laughs and admits he’s a data nerd…and so am I. The truth is, numbers don’t lie. Instead of “I think the pallet changer was acting up,” DataXchange shows exactly what happened. It takes gut feeling out of the equation and replaces it with clarity. Cycle time accuracy is one of the first big wins. CAM software gives you an estimate, but the shop floor reality is often different. DataXchange reveals the real cycle times, giving programmers, machinists, and quoting teams a true baseline. That translates into better lead times for customers, smarter planning for managers, and even a little healthy competition among machinists trying to beat their own best runs. Flying S has gone from a couple of MATSUURAs to multiple buildings and a lineup that spans compact MX-850s to monster MAM-72s. That variety means data becomes even more crucial. Smaller parts may finish in under an hour, but when a single aerospace component runs for days, or even weeks, you’re in a whole different league. Data exposes opportunities. If two tools eat up 80% of a 24-hour cycle, why not add redundancies or optimize tool paths? If inspection wait times slow things down, shift resources. With graphs and insights just a few clicks away, Flying S can fine-tune its strategy on the fly. Kyle says DataXchange was the easiest software rollout he’s ever done. Plug it into the controller, let it auto-collect, and you’re live. No endless operator inputs, no wasted time. And if questions pop up, customer support responds quickly. But perhaps the best part is cultural. Managers use the 30,000-foot view to set goals. Engineers drill down into graphs to tweak processes. Machinists use it as a scoreboard, shaving seconds without losing quality. Everyone, from leadership to the floor, has a clearer picture of what’s happening and how to improve. Flying S has always been a powerhouse in aerospace manufacturing, but now they’re combining world-class machining with world-class data strategy. And that’s what the Fourth Industrial Revolution is really about. Not just machines and automation, but the data that tells the truth. Greg Lisa Andrew Billy Kelly Yuto Ryan Logan Dante Peter

Are you in the manufacturing industry? Your products have to be tested to fulfill SLA. I think it's a good idea to incorporate AI into your operations. Embracing technology is not just a trend; it's a strategic evolution that can optimize your processes and enhance your returns on investment. Here are three ways AI can assist you in quality control within manufacturing: 💡 Use Case: Visual Inspection with Computer Vision AI-powered cameras and computer vision models are used to detect defects in products on the production line—such as cracks, misalignments, or surface anomalies. Example: A car parts manufacturer deployed AI vision systems to inspect brake pads. Traditional inspection missed micro-cracks that led to safety recalls. The AI model was trained on thousands of defect images and deployed on the line, instantly flagging faulty items. Impact: 🥊 Reduced defect rates by 40% 🏎️ Increased inspection speed by 3× 🗃️ Improved regulatory compliance Use Case: Predictive Maintenance for Equipment Quality Machine learning models predict when a manufacturing machine is likely to fail or degrade, which helps maintain product consistency and prevents defect-prone operation. Example: A steel rolling plant used sensor data (vibration, temperature, acoustics) to predict mill misalignments that were causing warped sheets. AI alerted technicians hours before quality dropped. Impact: 🗑️ 25% decrease in production waste 💪 30% increase in uptime 🔎 Improved consistency across batches Use Case: AI-Driven Root Cause Analysis AI analyzes production data across various stages to identify the root cause of recurring quality issues that human teams struggle to pinpoint. Example: An electronics assembly line faced sporadic soldering defects. An AI system correlated the defects with temperature shifts in a nearby process that wasn't being monitored as a quality variable. Impact: 💊 Reduced quality incidents by 50% ⏳ Accelerated RCA from days to hours 🛠️ Enabled proactive process adjustments Harnessing AI to tailor solutions to your specific needs can revolutionize your manufacturing processes. #AI #Manufacturing #QualityControl