Modernizing manufacturing with machine vision

What is Machine Vision?

The term “artificial intelligence” (AI) describes a software program that can sense, reason, and adapt its actions based on data—similar to the human mind, but with a more specialized form and function. Meanwhile, “machine vision” is one application of AI. It involves a combination of hardware (think of this as the “eye”) and software (the “brain”). When the eye and brain work together, AI can “see” and process events, objects, or information using machine vision.

This ability to react to dynamic, real-time data is beneficial for inspecting objects or documents. However, it can also be used for out-of-the-box applications such as guiding autonomous vehicles, tracking faces or movement in secure areas, and finding visual data patterns.

Automating Quality Control in the Manufacturing Industry

Product quality control is one area of industry in which machine vision adds significant value. Generally, manufacturers must put out a high volume of products in a short time-frame, creating a challenge: How can companies ensure product quality in a time and cost-effective manner? The answer is machine vision, tailored specifically for product quality assurance.

Several well-known companies already using machine vision for product quality assurance include the BMW Group, Canon, and Audi. These manufacturers leverage machine vision to spot blemishes in their products—some too subtle for the human eye to perceive—faster than the human mind can replicate. 

However, that’s not to say that humans are completely removed from the process: for example, at the BMW Group’s Dingolfing plant, AI works together with employees. When machine vision finds that a vehicle fresh off the manufacturing line fails to match perfectly with its order specifications, it alerts the humans staffing the final inspection team. These employees then judge whether the alert requires action. In this manner, machine vision allows employees to focus on the most urgent, high-value tasks.

New to Machine Vision? How Manufacturers can Take Advantage

Manufacturers can choose to implement machine vision along a sliding scale, from “no human involvement” at one end to “extensive human involvement” at the other. Some manufacturers may opt for minimal human involvement, trusting AI to sift through a high volume of products with computer precision and accuracy. Other manufacturers may prefer to include employees in the process, ensuring that all products are “human-approved” before reaching consumers. 

Manufacturers looking to explore AI or machine vision for their business have two options: (1) hire an in-house AI development and data science team, or (2) outsource to an AI consulting firm, like Synaptiq. In most cases, outsourcing will be the more cost-effective and timely option. Data scientists and AI experts are in high demand, and they take many years of education and many years on-the-job to train. They’re also hard to attract and retain, with many tempted by specialized, big data companies. AI consulting firms provide an established, field-tested alternative to a costly, hard-to-build in-house team.

At Synaptiq, we help businesses leverage AI to stay ahead of the competition. Rather than charging hundreds of thousands of dollars a year—as would an in-house team in terms of salaries alone —we start our engagements in the low tens of thousands, depending on the scope of your vision. And we have a proven track record of success: our case study catalog shows our excellence with past clients. If you are interested in modernizing your manufacturing line, Learn more about Synaptiq, or contact us directly.

Additional Reading


Will Robots Replace Me? Why You Shouldn’t Fear AI in the Workplace

It’s been an issue since the Industrial Revolution: technology moves quickly and, sometimes, it’s hard to keep up. Today, as artificial intelligence (AI) becomes ubiquitous in the workplace, it’s easy to wonder,
Will robots replace me?
The short answer is, “no, robots won’t replace employees” — at least, not in our lifetimes. But why is this the case? How can we trust this to remain so, with technology growing more advanced every day?

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