Aftermarket sensor installations can be difficult to standardize, difficult to install, and are subject to degradationUNRELIABLE
Where do you place the sensor, and what happens when it gets bumped out of place? Can your customer install and tune the sensor themselves?INACCURATE
Imagine you’re trying to learn a new tune on the piano, but the sheet music only has one note out of every ten.EXPENSIVE
Installation time and costs of sensors add up dramatically.
In 2020, our data science team launched a program focused on the application of high-frequency machine data for predictive maintenance with the goal of accelerating predictive analytics use cases for machine tools. Our team discovered a way to collect data at 1 kHz directly from the control of CNC machines without using sensors that can immediately be used as inputs to time-series or machine learning models to predict machine failures.
Supercharge your predictive analytics applications with high-frequency machine data to diagnose, predict, and avoid failures on your manufacturing equipment. No sensors required.
Understanding why issues have occured on machines in the past has been a notoriously difficult task. While low-frequency data can accomplish condition-based monitoring, Trying to do condition-based monitoring on say, spindle load, requires higher fidelity.
Collecting high frequency data from the control delivers unprecedented visibility and fidelity into the health and condition of the machine, enabling diagnoses that were once impossible.
Pushing cutting tools too hard leads to high tooling costs and frequent downtime for changeovers. Not pushing machines may reduce tooling costs at the expense of throughput.
An increase in tool wear due to differences in material, environment, and cutting can be detected, allowing you to run your tools to full life every time.
When tools are compromised, they don’t do their job right. This causes problems like bad finishes and incomplete cuts on parts that must then be scrapped once they make it to QA.
Reduce these issues with tool chatter detection. Predict when parts go out of spec before they need to be scrapped or when parts will soon become out of spec.
Expensive tools that snap when they become over-stressed, breaking not only that tool but even those further down the line.
Detect when initial stress fractures are occurring in tools, and alert the operator to take them out of commission before it gets worse.
Leveraging high-frequency data on spindle load to determine when the tools will fail, BC Machining can now detect a tool failure with 99% confidence up to 40 minutes before it fails.
We are actuating the machine to perform a Feed-Hold when this algorithm is triggered to stop the breakage from happening.