MENU

Visual inspection AI ‘reinvents’ manufacturing quality control

Market news |
By Rich Pell

The platform, Visual Inspection AI, is a purpose-built solution to help manufacturers, consumer packaged goods companies, and other businesses worldwide reduce defects and deliver significant operational savings from the manufacturing and inspection process. It is designed to help address the billions of dollars in cost to manufacturers each year resulting from defects in such products as computer chips, cars, and machinery.

The platform, says the company, was built to meet the needs of quality, test, manufacturing, and process engineers who are experts in their domain, but not in AI. Using Google Cloud’s computer vision technology, Visual Inspection AI automates the quality control process, enabling manufacturers to quickly and accurately detect defects before products are shipped.

By identifying defects early in the process, says the company, customers can improve production throughput, increase yields, reduce rework, and reduce return and repair costs. Visual Inspection AI operates across a wide range of industries and use cases, potentially saving manufacturers millions of dollars at each facility.

Based on pilots run by Google Cloud customers, Visual Inspection AI can build accurate models with up to 300 times fewer human-labelled images than general-purpose ML platforms, allowing it to be deployed quickly and easily in any manufacturing setting. In addition, Visual Inspection AI customers improved accuracy in production trials by up to 10x compared with general-purpose ML approaches.

And, says the company, unlike competing solutions that use simple anomaly detection, Visual Inspection AI’s deep learning allows customers to train models that detect, classify, and precisely locate multiple defect types in a single image.

“AI has proven to be particularly beneficial in helping to automate the visual quality control process for manufacturers – a particular pain point felt by the industry,” says Dominik Wee, Managing Director Manufacturing and Industrial at Google Cloud. “We’ve been delighted by the strong interest in Visual Inspection AI, and we look forward to supporting more organizations as they continue to find innovative new ways to deploy AI at scale.”

Mandeep Waraich, Head of Product for Industrial AI at Google Cloud adds, “We’ve been listening to the specific needs of the industry, and have brought the best of Google AI technologies to help address those needs. The outcome is an AI solution that, built upon years of computer vision expertise, is purpose-built to solve quality control problems for nearly any type of discrete manufacturing process.”

Google Cloud Visual Inspection AI is offered as featuring the following benefits:

  • No special expertise is required. Quality, test, and manufacturing engineers can use the solution without any computer vision or AI subject-matter expertise. An intuitive user interface guides employees through all of the necessary steps.
  • Engineers can get started quickly and build more accurate models. Machine learning models can be trained using as few as 10 labelled images (vs. thousands) and will automatically increase in accuracy over time as they are exposed to more products.
  • Full edge-to-cloud capability: Inspection models can be downloaded to machines on the factory floor and run autonomously at the edge, whether it be for data governance reasons or to improve latency. At the same time, Visual Inspection AI is fully integrated in Google Cloud’s portfolio of analytics and ML/AI solutions. This enables manufacturers to combine insights from Visual Inspection AI with other data sources on the shop floor and beyond, for instance to identify root causes of quality problems or to cross-reference with supplier and customer data.
  • Problems are resolved faster. Not only does the solution flag a defective component, but also Visual Inspection AI can locate and identify the specific defect within each part, which reduces the time spent by engineers to diagnose problems, rework parts, and implement process improvements.

Google Cloud Visual Inspection AI, says the company, can address a wide range of use cases, including:

  • Automotive manufacturers: A typical vehicle factory produces around 300,000 vehicles each year, and up to 10% of them may have parts that underwent rework or replacement during the manufacturing process to address some type of production defect. By automatically identifying defects in paint finish, seat fabrication, body welds, and end-of-line testing of mechanical parts, Visual Inspection AI could save automakers more than $50 million annually per plant.
  • Electronics manufacturing services (EMS): Of the 15 million circuit boards produced each year in a typical EMS factory, as many as 6% may be reworked or scrapped during the assembly process due to internal or external quality failures, such as soldering errors or missing screws. Reducing rework and material waste can save such a facility nearly $23 million each year.
  • Semiconductor production: A chip fabrication plant that produces 600,000 wafers per year could see yield losses of up to 3% from cracks and other defects. Implementing Visual Inspection AI can reduce production delays and scrap, saving up to $56 million per fab.

For more, see the case study: “FIH Mobile automates smartphone manufacturing with Visual Inspection AI.”

Google Cloud

Related articles:
Optimizing AI for industrial robotic inspection
AWS unveils five industrial machine learning services
Automated vehicle inspection technology uses AI, mobile phone


Share:

Linked Articles
Smart2.0
10s