End-of-line inspection technology – such as checkweighers, metal detectors, X-ray systems and vision systems – have been a standard part of food production lines for years. But the data used to make the binary decision of whether a product is good or bad is typically discarded immediately afterwards.
Now, however, there is a golden opportunity to unlock valuable business benefits from this information. The increased connectivity of devices and data analysis tools makes it possible to uncover previously hidden value through real-time insights into production. The dramatic decrease in the costs associated with data collection, storage and analysis – as well as tools such as machine learning – mean this 'fast-moving' data can now be captured and analysed like never before.
Capturing each measurement made by a checkweigher, for example, enables trends to be identified before a problem becomes a major issue. If four filling machines are involved in producing bags of dried pasta and a fault means one bagger consistently produces a product that is overweight, when analysing the batch averages across the four machines, the high-level data does not suggest anything is out of range. However, if the individual weights are analysed, a pattern emerges – allowing the faulty machine to be identified and the issue resolved.
Similarly, in the case of a chocolate enrober coating multiple lanes of chocolate bars, if the flow rate is non-uniform across the belt, then the chocolate layer on the central bars will be thicker than that on the outer bars. Identifying and rectifying this issue allows for the coating to be run closer to the optimal level – ensuring all bars meet the required weight. Without this insight, the enrober will be adjusted to ensure the outer bars meet the minimum weight, resulting in a 'giveaway' cost of the additional chocolate on the inner bars. Data at a batch level will not resolve this variation – but data on individual weights will.
In addition, if there are missing wafers in some of the bars, they will be rejected by X-ray inspection technology as it will detect that they are solid chocolate. If one of the moulds or lanes has an issue, that particular lane might have a higher reject rate. But the overall reject rate may not give cause for concern, so the problem will not be addressed. If the process control system is supplied with statistics for each lane, however, the situation can be constantly monitored – and the alarm raised before any issue becomes critical.
The linking of a weigh price labeller to a vision system is an example of the power of linking factory equipment. By analysing the contrast of the print on the labels, a warning can be generated if the print head is failing. This enables preventative maintenance to be carried out, rather than waiting until the print quality falls below an acceptable standard – at which point an unplanned stoppage will be required.
As well as detecting foreign objects such as metal, stone or glass, X-ray inspection systems can be used to verify the integrity of food products. As with the checkweigher, frequent product rejects can indicate an issue with upstream equipment. However, X-ray inspection can probe deeper into the specific issue. Take, for example, a four-pack of yoghurts. If one of the filler valves has become partially blocked, one of the four pots will be repeatedly underfilled. This underfill may not be sufficient for a checkweigher to identify the overall product as being underweight. But, by measuring the mass of each pot individually using X-ray zoned mass inspection, the four individual masses can be reported to the supervisory system and any deviations from the production norms can be identified rapidly.
In a connected environment, the frequency of breakages in products such as boxes of biscuits can also be monitored and flagged up, with warning thresholds set lower than automatic reject thresholds. If a misaligned tool is causing a high proportion of biscuits to be broken when placed into the packaging, data from X-ray inspection technology can highlight the issue before customer complaints start flooding in.
In today's increasingly connected world, advanced data analysis is making it possible to identify potentially complex root causes of problems. The secret lies in harnessing this interconnected data to reveal unprecedented hidden value in the production line.
Dr Richard Parmee, founder and CEO of X-ray inspection technology company Sapphire Inspection Systems
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