How the Latest Advancements in Artificial Intelligence Can Impact the Manufacturing Process
Is computer vision AI the future of manufacturing? Find out.
Human errors in manufacturing can result in catastrophic and costly events, with reports showing that unplanned downtime in manufacturing as a result of human error is 23% – higher than most other industries. Adam Bennett, Enterprise Sales Manager at Matroid, discusses how AI advancements like CV AI could greatly improve the manufacturing process.
At a time when manufacturers face obstacles that are seemingly out of their control, like inflation and supply/demand issues, reducing human error is one area they can improve by implementing Computer-Vision Artificial Intelligence (CV AI).
And companies are taking notice of how impactful and effective AI can be to their bottom lines. According to a recent survey by Deloitte, over 80% of companies believe AI has and can make a “practical and visible impact,” while almost 30% of companies surveyed consider AI to be an asset to their organization already.
Let’s look at what CV AI is and the areas within manufacturing where it can help companies save money and avoid costly mistakes.
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What Is Computer Vision AI?
CV AI leverages the latest in deep-learning technology, which is based on artificial neural networks such as convolutional neural networks (CNN) and recurrent neural networks (RNN).
It’s essentially a computer processing and making sense of visual data. CV AI learns from the visual data collected to provide predictions and actionable insight on images, videos, and live streams. Camera agnostic, CV AI detects defects, objects, anomalies, actions, and events and localizes these detections.
How Can CV AI Help Manufacturing?
The benefits of using CV AI in manufacturing are vast and not limited to just the assembly line. It can conduct inventory tracking, collect data analytics, improve safety ratings, and enhance security efforts – all areas that are critical to a successful manufacturing process.
But to understand the impact of CV AI, let’s look at how specifically it can improve the process:
- Detection: CV AI enables camera systems to detect/classify objects, defects, anomalies, people, actions, and events – even down to a microscopic scale – that vary in shape, size, color, texture, and more, reducing the challenges of catastrophic and costly events.
- Deeper understanding: It’s no longer good enough to detect pass/fail when inspecting things. CV AI can label precisely the type of detection, such as a weld defect that may involve porosity, spatter, or burn-through. If a welding expert is informed of these detections, they – or a machine – can tune the automated operation faster rather than making adjustments many cycles later.
- Collaboration: CV AI helps manufacturers get to the root causes of various challenges faster. It creates collaborations between cross-functional teams by allowing key players to detect and manage defects in real-time.
- Safeguarding knowledge: With employee turnover a concern, CV AI learns and retains visual inspection requirements in complex production environments, similar to a human QC inspector. This can help eliminate the risk of valuable knowledge leaving when someone does. Retaining that tribal knowledge is also impactful when onboarding new employees, as it reduces weeks and even perhaps months of training time.
WHERE DOES CV AI SAVE MANUFACTURERS MONEY
How are these enhancements converted into money-saving results? CV AI has been proven to impact:
Rework cost savings: CV AI empowers manufacturers to apply advanced inspections in areas with unstructured environments, where digital traceability was lacking, and previously only highly trained operators could inspect. By inspecting critical quality control points, providing digital traceability, and allowing for rework to be done early in the process where needed, CV AI has been shown to save millions in yearly rework costs.
Efficiency gains: CV AI goes beyond traditional machine vision and can do more than detection of defects or objects. It can also detect people, their actions, and even events in images and videos. Manufacturers are finding significant gains by leveraging CV AI as a continuous improvement tool to improve line balancing and labor utilization in manual assembly processes. The constant capture provides insights into anomalies and weekly, monthly, or seasonal trends/patterns. Companies can more accurately understand production costs, determine kaizen, and have reported more than 15% shared efficiency gains.
Scrap loss savings: Scrap in manufacturing is a significant and costly issue, as too much-rejected material can negatively impact a company’s profit margin. Inspection and monitoring in production can reduce a company’s scrap and save millions of dollars in a normalized annual loss.
How to Know If You Need CV AI?
To understand if CV AI could be beneficial to your manufacturing process, you should ask yourself these questions:
- Are the challenges faced in your manufacturing process visible by some form of camera technology or any spectrum that provides image or video?
- What aspects of your operations can image or video detections and analytics bring value, i.e., QA/QC, manual labor utilization, safety, security, etc.?
- What insights do you want to gain from CV AI?
- What systems will CV AI integrate into so that action can be taken because of detections and analytics, i.e., machine, MES, or ERP?
- Is there a one-off need, or can this span across the operations?
- Does the platform empower your SMEs to easily build their own detectors without needing to be data scientists?
- If your current team members moved on, can others easily pick it up, i.e., QC inspectors or industrial engineers?
By looking at these areas of your manufacturing process, you can gauge how and where CV AI will be most beneficial.
See More: How to Increase Productivity in Manufacturing Operations with Digital Technology
Where to Start
If you’ve decided that your organization’s manufacturing process can benefit from CV AI, there are simple steps you can take to do to get started with this technology and that you’ll want to consider to ensure that it’s a good fit for your organization:
- Consult with a CV AI platform company that can review your interests, applications, and goals for using CV AI.
- Consider a PoC trial to learn how the technology can help and integrate into your systems and operations.
- Ensure that it compliments your operations by allowing your cross-functional departments to build, test, deploy, and manage pre-made and custom detectors for their needs without needing to be a degreed Data Scientist.
- Ensure suitable deployment modes for your needs, such as Cloud (public/private), On-prem, and edge
- Confirm that the CV AI platform is camera technology agnostic so that it can work with any technology. Application needs vary throughout operations, and different imaging technologies and resolutions may be needed.
- Use an application that can integrate into your current systems, i.e., PLCs, SCADA, MES, WMS, ERP, Databases, Security, etc.
- Partner with proven talent with a track of deep-learning AI. Industry-leading engineering is what is empowering the greatest gains for manufacturers utilizing this technology.
What example can you think of where AI greatly improved a manufacturing process? Share with us on Facebook, Twitter, and LinkedIn.
Image Source: Shutterstock
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