There is some unpredictability in manufacturing operations you can prevent.
Utilize Statistical Process Control with Machine Vision
Gathering baseline data is crucial to be able to set limits and rules based on what is "normal." Once you define what is "normal" for your operation, you can quickly identify and mitigate deviations from it. When you are paying attention to how things change over time - from hour to hour, from week to week, and from year to year - you are able to take informed action to prevent waste and enhance your process. Using machine vision to automate data gathering and quality checks is a great way to do this - see why below.
Calibrate Periodically and Regularly
Verify that the standard you are measuring against and specified tolerances have not shifted from day to day or from shift to shift.
Eliminate Single Points of Failure
Provide redundancy for critical components. You can anticipate more problems or abnormalities when you have more than one way of monitoring a process.
Invest in a Built-In, Automatic System
An effective vision system for quality control is a long-term, automated solution that makes daily quality checks reasonable and efficient, and ensures they actually happen. Artemis Vision systems have built-in SPC controls and reporting, so manufacturers can view charts and data for real-time trend analysis or pull them up at the end of a shift to easily get information about things like reject percentages in a given time, units inspected per hour, etc. Results are stored with image capture from inspection, making future and retroactive reference and analysis easy. And calibration is built-in, so verification can happen as often as needed.
Our vision systems also eliminate the cost of gathering data because they dynamically bring all data points into SPC analysis, real-time. When you have a vision system with good repeatability, you don't have to constantly obsess over hitting magic numbers and whether or not you are off by 10 microns. Instead of spending hours generating complicated graphs, you can simply run the system for a while and see in graph form the smallest anomalies that have occurred. (For example, the image below shows a noticeable deviation in measurements over the course of a few hours.) This vastly speeds up process feedback loops.
As we plan for an unpredictable new year, taking stock of process control is one enormous positive step.