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!

𝗘𝘃𝗲𝗻𝘁 𝗙𝗿𝗮𝗺𝗲𝘀: 𝗧𝗵𝗲 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗟𝗮𝘆𝗲𝗿 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝗥𝗮𝘄 𝗧𝗮𝗴𝘀 𝗮𝗻𝗱 𝗔𝗰𝘁𝘂𝗮𝗹 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 Raw tag data tells you what happened at a given millisecond. But manufacturing decisions happen at a completely different level of abstraction. The real work starts when you transform tag level events into higher order structures. Things like: 🔹 Alarm lifecycle events, from raised through acknowledged to cleared 🔹 Batch events tied to ISA-88 procedural steps with start and end times 🔹 Material transfers between units 🔹 Shift and schedule boundaries These transforms have traditionally lived inside industrial data historians or been hardcoded into SQL databases. They are the backbone of any serious manufacturing data analysis. And here is the thing most people overlook: without these event structures, your ML models have nothing meaningful to train on. You are feeding a neural network raw temperature readings and wondering why it cannot predict batch quality. Node-RED is already streaming data from the manufacturing floor, which makes it a natural fit for transforming that data into time framed event structures. That is the idea behind node-red-contrib-event-calc. The message format is flexible enough to accommodate even complex event hierarchies. And it is not opinionated about the rest of the stack. The streaming data source can be MQTT, OPC UA, or NATS, and the target can be any time series database with deduplication support like QuestDB. What event structures are you building in your stack? Curious what others are solving with open tooling. #manufacturingdata #industrialautomation #nodered #questdb #eventframes #isa88 #mqtt #unifiednamespace #iiot #timeseries

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!

𝗠𝗘𝗦 𝗮𝗻𝗱 𝗜𝗼𝗧 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Critical Manufacturing details how its #MES, Connect IoT and IoT Data Platform software can untangle shop floor #data to turn raw equipment and process data into #Industry4.0 intelligence. Key points address in this article include: • Why viewing MES not just as a monitoring tool but a data contextualizer is critical to #digitaltransformation, as it provides meaning to disparate machine and #sensor data.   • How integrating control and #analytics ensures visibility without losing real-time action capabilities.   • With advanced data correlation capabilities, manufacturers can link process deviations to specific products, enabling predictive #quality and operational optimization. https://lnkd.in/edDvDWBQ

During a recent trip to Dallas, where I engaged with various manufacturers, a recurring question emerged: "What software is best for digital transformation?" As you might expect, the answer is not straightforward—it depends. To help clarify this complex topic, I've synthesized my thoughts and experiences to guide those embarking on their digital transformation journey. 🔍 Key Questions to Consider 🔍 Do You Have an Existing SCADA System? If yes, take advantage of existing platforms like Ignition by Inductive Automation , FrameworkX from Tatsoft, or InTouch from AVEVA. These systems provide great connectivity to the plat floor and up to Business applications and come complete with historisation and visualization capabilities. Are you starting from Scratch? For plants without a SCADA system, installing Ignition, FrameworkX, or InTouch can be incredibly beneficial. All three are broad-based platforms that can be adapted to really solve lots of problems in manufacturing businesses. I don’t think you can go far wrong with any of them. And of course, a key question is, is there support for those systems local to you? Do you have a lot of manual interaction your process? If your process more manual data collection can rely heavily on operator input, Tulip Interfaces stands out as a top choice. It's particularly effective in environments with manual workstations and limited machine data. You will need to leverage integrating Node-Red for data collection and contextualisation, and if managing multiple edge devices may necessitate using FlowFuse for orchestration. Are you looking at multiple plants? When scaling across multiple plants, Litmus is my immediate thought. It is tailored for large-scale deployment, capable of connecting siloed industrial data sources and integrating them into usable formats for business analysis and cloud-based applications. Once you have the data available you can look at how to extract the value, and it does work very well with Tulip, but it is also heavily used to get data into cloud platforms and analyzed by business analysts, and often not by machine learning and AI systems. 📊 Advancing to Modeling and Analytics 📊 As your digital transformation progresses, integrating advanced modeling tools such as Flow Software Inc., HighByte, or MaestroHubs can significantly refine data utilization and enhance operational efficiency. Can I do this with Free Software? Not easily and probably not. I love Node-Red, Mosquitto, Grafana, and Timescale for niche applications, they can present challenges when scaling. These tools are best used for proof of concept or to augment specific capabilities within a larger framework. What's your go-to IIoT platform? How do you navigate these complex decision-making processes? Are there any platforms I haven't mentioned that you find indispensable? #DigitalTransformation #IIoT #Manufacturing #Industry40 #SCADA #DataManagement #OperationalTechnology #IoTPlatforms #SmartManufacturing

SUCCESS! Machine monitoring is a pivotal component in modern manufacturing, enabling real-time oversight of equipment performance and operational efficiency. By collecting and analyzing data from machines, manufacturers can enhance productivity, reduce downtime, and make informed decisions that drive continuous improvement. Importance of Machine Monitoring: 1. Automated data collection eliminates manual entry errors and provides immediate insights into machine status, utilization, cycle times, and operator performance. This real-time visibility allows for prompt responses to issues, minimizing disruptions. 2. Enhanced Operational Efficiency: Monitoring systems identify bottlenecks and inefficiencies, enabling manufacturers to optimize processes, improve machine utilization, and increase overall equipment effectiveness (OEE). 3. Predictive Maintenance: By analyzing parameters like vibrations, temperature, and pressure, machine monitoring facilitates predictive maintenance strategies, reducing unplanned downtime and extending equipment lifespan. 4. Quality Assurance: Continuous monitoring ensures machines operate within specified parameters, maintaining product quality and reducing defects. This leads to higher customer satisfaction and reduced waste. MachineMetrics is a leading provider of machine monitoring solutions tailored for machine shops. Their platform offers several key benefits: • Automated Data Collection: MachineMetrics’ system seamlessly integrates with various machinery to collect data without manual intervention, ensuring accuracy and timeliness. • Real-Time Analytics: The platform provides real-time dashboards and reports, offering insights into machine performance, utilization rates, and production metrics. • Predictive Maintenance: By analyzing machine data, MachineMetrics can predict potential failures, allowing maintenance teams to address issues proactively. • Enhanced Decision-Making: With comprehensive data analytics, machine shops can make informed decisions regarding process improvements, resource allocation, and capital investments. MEC (Mayville Engineering Company, Inc.), a leading U.S.-based contract manufacturer, sought to improve machine uptime and efficiency. By partnering with MachineMetrics, they achieved: • 15% increase in uptime • 20% increase in efficiency • Return on investment within 90 days Morgan Olson, a leading walk-in van body manufacturer, transitioned from a paper-based tracking system to MachineMetrics’ automated data collection. This shift led to: • 20% boost in machine utilization within months • $600,000 savings in capital expenditures • 50% reduction in waste Video filmed at IMTS - International Manufacturing Technology Show Graham - Eric - Ben - Tim - Brady - Bill - John - Morgan - Henry #MachineMetrics #IMTS

When a machine crashes, the clock starts immediately.... The pressure to figure out what happened and which parts are affected is real. And if your only tool is memory, a spreadsheet, or a manual log, you're already behind. Hamilton Company attacks this problem with MachineMetrics. Their maintenance and engineering teams use our platform to cut through the chaos after a machine event and take action: ✅ Timeline-based diagnostics — see exactly what happened and when ✅ Part-level segregation — know which parts were running during the crash, not just which machine was down ✅ Instant historical playback — no digging through logs or chasing down operators for context ✅ Alarm correlation across parts — because the same alarm code can mean very different things depending on what was running The shift from reactive firefighting to structured, data-driven action doesn't require a new team. It requires better data access and a platform to drive it with. 👇 Don't take my word for it, just watch the video! #Manufacturing #MachineMonitoring #MES #MaintenanceEngineering #MachineMetrics #ShopFloor #ContinuousImprovement