From inspection to automation: The role of machine vision in modern manufacturing
Author: Prosenjit Banerjee | Principal Data Scientist | Machine Vision and Conversational AI
Machine vision is revolutionizing the way companies manufacture. It transforms supply chain and logistic systems into smart cyber-physical systems that can extract vital information remotely through images and videos. Until recently, the major applications of machine vision in manufacturing were on condition or process monitoring systems. In the early days, the motive was to reduce dependency on human operators by installing a camera strategically in a manufacturing unit to capture continuous events or sequences as images/videos. These unstructured data were processed with state-of-the-art machine vision algorithms to infer the criticality of events in real time and augment knowledge to an operator in his decision-making process. This led to the development of automated optical inspection (AOI) systems which could detect defects of manufactured units in a production line non-intrusively, removing the faulty units to land in delivery. The introduction of ethernet server-based systems enabled vision sensors to stream / store, process, and infer from large volumes of data. It was now possible to interconnect multiple vision systems and make quicker inferences, enabling faster decision-making. Suddenly, the production lines became faster, with informed or scheduled downtimes and significantly fewer manufacturing errors. Simultaneous advancements in camera / vision sensor technology and machine vision algorithms scaled the adoption of vision sensors towards other related events in manufacturing like situational awareness in factory floors, regularize human intervention in critical or hazardous areas, maintaining safety standards in production like adherence to helmets, gloves, PPE kits, detect an early outbreak of fire, prevent accidents and better asset management. The growth and adoption of cloud technology and IoT fostered investment by the manufacturing industry toward digital platforms. With the history of production knowledge and decision-making, replicating or repurposing manufacturing systems became the need of the hour.
The concept of digital twins was introduced as an analytical environment for closed-loop decision-making for any known entity in a value chain, thereby connecting decisions from the strategic to the operational level. Machine vision has supported the realization of digital twins, from creating virtual environments for production to the virtual commis- sioning of machines. Machine vision generates compelling business value among early adopters. However, as Gartner states, the highest-value machine vision solutions are hard to adopt, replicate and scale. McKinsey pegs the number at 72%, where organizations have tried to adopt yet met with less success. How do we then harness the power of machine vision applications? How do we architect adaptable and scalable machine vision solutions? Machine vision has always delivered significant value within uniform parameters or working conditions. The difficulty in scaling machine vision solutions in some sectors may be attributed to the lack/absence of standardization in their processes. This may require additional investments where legacy systems may have to be upgraded to meet required standards. Other roadblocks towards machine vision adoption are witnessed in data privacy, where data about critical processes were absent in model building.
Exceptional progress in vision feature engineering coupled with steep rise in deep learning and camera sensor technology, have opened a plethora of opportunities across the manufacturing and supply chain, which earlier had existed only in theory.
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