Juni 03, 2021

New technology is changing the machine vision business model

Kakeibo is the Japanese art of saving money. The concept, invented in 1904, involves keeping a budgeting journal to plan what you’ll save, spend and how to stick to it. To save money implementing a machine vision system for quality assurance (QA), it can be even simpler. Here Zohar Kantor, VP Sales & Project Management of Autonomous Machine Vision expert Inspekto, compares the costs of traditional and Autonomous Machine Vision.

Machine vision has always been a necessary evil. While manufacturers require visual QA to detect defective products, it is an expensive, complex and expert-dependent process.

The cost of traditional machine vision

Once the manufacturer has decided to implement visual QA at any point on the production line, they require a systems integrator to build a tailored solution.  Because of the associated costs, industrial QA managers must consider each point in the production line, to decide whether the investment is justified

The systems integrator will create Proof of Concepts, develop a possible solution, test it, optimize it, and eventually come up with an installation. The integrator must purchase the necessary components at expense to the manufacturer, including cameras, lenses, lighting, housing and communication and more. Assuming a solution is applicable to the targeted point on the production line, the integrator then builds it.

Not only is the manufacturer charged for the professional services of an integrator, but there are additional costs of wait-time till the solution is ready, and of downtime while it is installed, tested and commissioned. For a single inspection task on a pre-defined product, the manufacturer is typically faced with a bill in the range of €20,000 to €150,000.

As well as the capital expenditure to get a system up and running, the manufacturer should consider operational expenditure. If there is an environmental change in the plant or the manufacturing line is modified, the manufacturer will have to call on the systems integrator to adjust or redesign the system – usually after defects have gone unnoticed for some time. Re-usage of the solution at any other point in the plant is usually out of the question.

These costs, combined with the solution’s complexity and long timelines for implementation, mean that for many manufacturers it is possible to implement visual QA solutions only at major junctions on a production line in most cases end-of-line inspections, which increases scrap levels and reduces productivity and yield. As one plant manager put it – the end-of-line visual QA test protects the customer; while the many inline visual QA tests protect the plant.

The tides are turning

Autonomous Machine Vision technology offers manufacturers a cost-effective alternative. By opting for a small, standalone, self-learning system that can be installed in minutes by the manufacturer’s own personnel, the manufacturer can cut costs in testing, professional services and hardware by 90 per cent.

Autonomous Machine Vision systems can be installed in minutes or hours, instead of weeks or months. This reduction in time and cost means that manufacturers can implement the system at any point on the production line, enabling Total QA, where visual quality assurance is universal upon the production line. Even better, a self-learning system can be easily and independently moved from one point on the production line to another and self-adjust to a new environment or to new products.

On top of that, Autonomous Machine Vision savings go far beyond the far lower cost of any single QA location. It impacts the whole manufacturing paradigm, ensuring every step on the production line complies with specification, and disposing of scrap components before they’re combined with good components to make a defective product.

With this in mind, Autonomous Machine Visions systems savings are far greater than 90 per cent. Manufacturers don’t need to turn to the Japanese art of Kakeibo to save money – they simply need to turn to Autonomous Machine Vision.

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Vice President of Public Relations