- \n
- Machine vision gives a timestamped verdict on every part.<\/li>\n
- That count can feed the OEE quality factor directly.<\/li>\n
- Crossed with machine state, it shows when rejects appear.<\/li>\n
- Inspecting and measuring are two different, complementary jobs.<\/li>\n<\/ul>\n<\/div>\n
What machine vision does on the line<\/h2>\n
ATRIA Innovation, an engineering and technology innovation company based in Zaragoza, Spain, describes how an inspection station works in Control de calidad por visi\u00f3n artificial<\/a> (Machine vision quality control, in Spanish): the product passes through the station, one or more cameras capture images, software processes them in real time and compares what it finds with defined parameters. Then, in the article’s words: “El sistema determina si la pieza es v\u00e1lida o presenta defectos.” (The system determines whether the part is good or has defects.) An automatic action follows: accept, reject or sort the product.<\/p><\/blockquote>\n
The ATRIA Innovation article also covers the usual applications: surface defects such as scratches, cracks, stains and deformations, dimensional checks, assembly verification, labeling and packaging, and code reading for traceability. It points out that the information generated can be stored for later analysis. That is the starting point here: inspection produces reliable quality data, part by part. The question is what the plant does with that data once the part has been rejected.<\/p>\n
From reject to OEE quality factor<\/h2>\n
OEE is the product of three factors: availability, performance and quality. The quality factor is the share of good parts in the total parts produced. On paper it is the simplest of the three. In practice it is often the least reliable, because it is fed by manual declarations at the end of the shift, scrap weighed in a bin, or figures that arrive days later from the lab.<\/p>\n
A vision station changes this: every inspected part gets a verdict, with a timestamp. If that count reaches the OEE calculation, the quality factor stops being an estimate. The real value appears when the reject is no longer an isolated number and sits on the same timeline as stops, short stops and machine speed.<\/p>\n
Two precautions before you start:<\/p>\n
- \n
- Define the denominator.<\/strong> Parts produced must be counted at the same point for every product: before the inspection station, not after it.<\/li>\n
- Handle re-inspection.<\/strong> If rejected parts get a second check and some go back to the line, decide whether they count as good first time. Classic OEE only counts parts that are right first time as good.<\/li>\n<\/ul>\n
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<\/figure>\nCrossing rejects with machine state<\/h2>\n
The camera knows a part is defective. It does not know what state the machine was in when it made that part. Put the two data sets side by side and patterns appear that neither shows alone. The table below lists typical situations; they are examples of how to read the data, not results from a specific plant.<\/p>\n
- Handle re-inspection.<\/strong> If rejected parts get a second check and some go back to the line, decide whether they count as good first time. Classic OEE only counts parts that are right first time as good.<\/li>\n<\/ul>\n
- Define the denominator.<\/strong> Parts produced must be counted at the same point for every product: before the inspection station, not after it.<\/li>\n