Simply put: It is a messy, tedious activity to acquire, parse, and clean data for analysis in a factory.
When tools break, it can be costly. A tool can become damaged yet still make parts that seem to spec, but those parts end up often getting scrapped. Subtle anomalies in machine load, torque, acceleration, and spindle speed can cause parts to be made outside of required tolerances.
So what’s the solution? Predict when problems are going to occur before they cost you time and money. Easy right? Not exactly. In fact, most solutions available in the market today are generally un-scalable, unreliable, inaccurate, and expensive. Long story short, today’s solutions don’t really cut it.
That’s why MachineMetrics wanted to deliver something better. Sign up now and learn how you can start leveraging high-frequency machine data to diagnose and predict various types of failures on your machines (or even stop them from happening in the first place!).
- What is high-frequency data and why it's a game-changer for predictive analytics
- How to optimize your tooling usage to reduce tooling costs
- How to detect when a tool is about to break so you can avoid downtime
Head of Data Science, MachineMetrics
Lou Zhang is Head of Data Science at MachineMetrics. Lou has extensive experience with both the manufacturing industry and with developing predictive algorithms for time-series data. Prior to MachineMetrics, Lou conducted research with NIST and AMT.