For decades, automation using machine vision has been one of the most popular ways for manufacturers to increase their margins. Today, automation via Artificial Intelligence (AI) technology is transforming manufacturing’s ability to improve their business operations.

At its most basic, AI enables machine and computing systems to learn from data and examples in order to predict outcomes. But even as this game-changing technology advances and becomes more user-friendly, many manufacturers still struggle to take full advantage of it, largely due to perceived challenges involving cost, startup time, required expertise, and the reliability of results.

A successful deep learning project can generate cost savings, as well as yield improvements and a better understanding of your own manufacturing process. While there are initial, direct costs associated with implementing a deep learning solution,the direct and indirect benefits are substantial.

Cut Costs and Reduce Overhead

Manufacturers willing to take the risk of replacing outdated work practices will be rewarded with less overhead. Manual inspection is dominated by labor costs, incurred yearly, and include staff turnover and re-training expenses. Human inspectors are frequently superior to automated solutions when they are paying undivided attention. But most operators can only focus for 15-20 minutes, resulting in inconsistencies during a shift or between production lines. When computing payback on an AI project, many manufacturers are surprised to learn how quickly their yield and throughput improve.

Quicken Implementation

Contrary to what many people think, easy-to-use AI software designed for factory automation can actually quicken time to market. Consider the time and effort involved to accurately program and maintain complicated machine vision applications: the defect libraries, the exceptions to account for, and filters can become immense over time. Instead of writing algorithms or programming complicated rules for a computer, AI teaches the same system to learn from data sets and make decisions based on those examples. An AI application can be implemented, tested, and refined in a matter of weeks.

Improve Analytics and Upstream Process Control

An AI solution that documents inspection results provides reassurance for its user, as well as the ability to retroactively check inspection images and decisions in the event of future failures. Once a final inspection station has been successfully automated, it’s often possible to migrate the inspection steps upstream to in-line inspection. This cuts costs by identifying defects before expending time or adding additional value to bad parts.

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