Beyond robotics: the real ways AI is transforming manufacturing

Access an overview of AI’s role in helping manufacturers improve product quality, equipment maintenance, design processes, and more.

Autonomous, vision-assisted machinery, collaborative robots, and intelligent production lines: These are the showcase examples of AI being put to work in the modern manufacturing plant.

Scratch a little deeper and you’ll find many other instances of AI being deployed, with the lofty goals of generating production efficiencies, minimizing unplanned downtime, and ensuring the highest possible product quality.

In this blog, we’ll explore four of the more immediate examples and the practical benefits they deliver.

Manufacturing with added intelligence

Improving loss prevention

1. Streamlining data collection

One of the great promises of AI is the ability for manufacturers to make rapid, data-driven decisions. However, such potential can be limited by existing processes for generating and collecting data, particularly as most equipment, facilities, and staff are not sufficiently prepared to collect or analyze the data and provide actionable insights. .

However, AI provides the answer through a series of innovations that, in coordination with smart devices and intelligent terminals, help rationalize the way data is gathered at the point of origin.

For example with cameras used to observe a production process, AI can analyze the gathered data, draw insights, and determine when inventory levels become critically low. As a result, procurement teams don’t need to spend time tracking and ordering critical materials and components. AI is also ideal for identifying a user’s intentions and simplifying systems. Combining this intelligence with smart devices means superior results and fewer errors, alongside new insights that were previously too time-consuming to unlock.

Understand shifts in customer preferences

2. Enabling true predictive maintenance

Unplanned downtime is an obvious concern for manufacturers, impacting productivity, client deliverables, and the overall bottom line. The desire to minimize interruptions has resulted in the industry evolving through three phases of maintenance:

  1. Reactive maintenance: Fixing machine components as they fail.
  2. Preventative maintenance: Completing planned maintenance on schedules that take previous failures into account.
  3. Predictive maintenance: Anticipating potential failures and enabling fixes before problems occur.

With its ability to analyze vast datasets, AI enables more informed decisions when it comes to keeping equipment working optimally. Instead of responding to a potential failure by replacing suspected components, manufacturers can work from a more holistic view of cause and effect – and take corrective action with confidence.

Optimizing inventory management

3. Transforming product design

AI has a profound impact in the area of generative design. In this process, manufacturers feed detailed design information – including various restrictions, such as performance requirements, loading conditions, material types, available production methods, and budget and time constraints – into generative design software models. These are then used to explore every possible variation and to  identify the most suitable solution.

The next step is to create prototypes, and to test these using AI and machine learning (ML). With Quadro Virtual Workstations, engineers and designers can visualize the design using CAD and rendering applications – and make improvements using trial and error methods to determine the ideal product arrangement .

Alongside real-time problem solving, this approach is delivering a number of significant advantages to manufacturers:

  • The AI model is completely objective, and doesn’t default to what a human designer would consider a logical starting point.
  • As a result, no assumptions are made, and each design is tested according to actual performance against a wide range of manufacturing scenarios and conditions.
  • The resulting outcomes can save significant cost in terms of product recalls, repairs, and lost business.
Optimizing inventory management

4. Improving quality control

Workflows utilizing AI and computer graphics, when integrated with a cloud-based data processing framework, can pick up minute details and errors far more reliably than the human eye. That’s why Quadro Virtual Workstations running on Google Cloud are increasingly being used to observe manufacturing processes and identify issues, such as:

  • Microscopic cracks in manufacturing equipment.
  • Inconsistent machine movement.
  • Other minor defects that can compromise product quality.

Then there’s speed. Whereas numerous people are needed to spot defects in a factory, AI algorithms can take half a second to inspect a part – and with far greater accuracy – thereby freeing individuals up for more rewarding tasks.

The technology also works well with additive manufacturing. Using high-resolution cameras to record the printing process layer by layer, AI can detect pits, streaks, divots, and other patterns that are not visible to the human eye. AI can learn from this to identify defects in the product or the process itself.

Realize the Power of Two

The manufacturing sector is the perfect fit for the application of AI. Making the most of this potential, however, requires the Power of Two. That’s the power of NVIDIA® GPUs and the Google Cloud AI platform working in partnership to inspire breakthrough performance.

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