One of the challenges with machine vision is that when a system is deployed in the field, the system doesn't perform as well as it did in the lab, because an industrial environment introduces new variances and challenges to overcome.

Natural surface aberrations in the product, positioning variations, and external environmental changes are inevitable. These variables have interdependencies, and when a vision system's performance degrades, it can be difficult to determine the root cause.

Maintenance tweak tolerances to make production happy to reduce the false rejects, but this opens the risk of letting non-conforming product get to the customer.

An experienced Subject Matter Expert knows the key elements to making a vision system that is robust.

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Reshoring of US manufacturing to stabilize the supply chain and the increased requirements on production quality are driving rapid growth in automation. Tolerances are getting tighter, cost pressures are rising, and traceability requirements are making Machine Vision and Deep Learning increasingly common in all manufacturing industries.

US manufacturing runs lean and must automate to compete in the world market. In-house engineering teams are often focused on the urgent issues; they don't have the time to design and deploy a robust solution.

An experienced systems integrator can deploy in a shorter period of time and own the result of the solution.

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The era of Industrial 4.0 is creating an environment where automation needs to capture the physical world and transform it into networked digital data, not only to drive efficiency but also to provide information transparency. In an increasingly complex automation environment, today's Machine Vision solutions must also address the data intelligence for multiple stakeholders: Maintenance, Quality Assurance, Process Engineers, and Management.

Capturing data and images of inspection results for both rejected and good products is important for tracking performance. If the inspection results data is not collected, stored, and analyzed, there is no data to determine the root cause of why there are excess false rejects. There is no data to review when a customer receives a non-conforming product. There is no data for predictive analytics to raise a warning flag that inspection results are within 10% of tolerance/thresholds.

Experienced industrial software developers bridge the gap between the device and the data repository to make the Smart Factory paradigm possible.

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Problem

Impact / Root Cause

Future State / Solution

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Problem

Impact / Root Cause

Future State / Solution

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