Key Takeaways
Edge learning provides a practical, scalable AI-based solution for automating manufacturing and logistics applications. Here’s how:
- It changes automation by processing data on-device, reducing complexity, and enabling quick deployment.
- It requires little training and no prior AI expertise because it uses pre-trained algorithms to deploy.
- Applicable across industries, it betters quality control, cuts costs, and improves efficiency with smart, camera-based solutions.
What is edge learning?
Edge learning is an AI technology that place “at the edge” of where the data originates – on a smart camera, in machine vision applications. Leveraging a set of pre-trained algorithms, the technology is simple to set up, needing less time and fewer images to train compared to other AI-based solutions, like deep learning.
Edge learning is an easy solution for beginners and experts alike. Manufacturers can deploy this as a simple way to automate their lines. Even experienced engineers who use machine vision but lack specific AI expertise can deploy seamlessly. Being both extremely capable and easy to use, edge learning can automate a range of applications across the factory and industries.
What are the benefits of edge learning?
Edge learning makes machine vision and automation easy. No need for specialized machine vision or AI knowledge – line engineers can train the technology using their existing knowledge of required tasks. The solution only needs a handful of sample images to learn the difference between good and no good parts. It requires no prior AI or machine vision experience. Read on to learn more about the benefits of using this powerful technology.
How does edge learning work?
Using a single, smart, camera-based solution, edge learning can be deployed on any factory line within minutes. This type of solution integrates several components including machine vision hardware, rule-based tools, and AI capabilities.
Machine Vision Tools
Rule-based vision tools are well-suited for specialized tasks, such as location, measurement, and orientation. For the purposes of edge learning, they are combined in ways specific to the demands of factory automation, eliminating the need to chain vision tools or build complex logic sequences when training the system.
These tools provide fast preprocessing of images, extracting density, edge, and other feature information for the purposes of detecting and analyzing manufacturing defects. By identifying and clarifying the relevant parts of the image, these tools reduce computational load, compared to traditional deep learning approaches.
AI Capabilities
Instead of using rules created by human programmers, AI learns by example, building a neural network and devising effective pass/fail thresholds from labeled examples of acceptable and unacceptable parts. In essence, it mimics the way humans learn.
AI capabilities can have large training requirements. Edge learning, on the other hand, takes advantage of the fact that factory automation images have specific structural contents, and so pre-trains its algorithms with that domain knowledge. Not starting from scratch results in a less learning-intensive application.
What is edge learning used for?
Manufacturers and logistics companies can deploy edge learning to address a wide range of challenges across all industries, from automotive and electronics to consumer packaged goods and logistics.
Detect and classify imperfections and differentiate flaws from acceptable anomalies.
Inspect resistors and classify them as “NG” (damaged or scratched) or OK.
Classify and separate products based on size, color, and visual characteristics.
Prevent equipment damage or processing delays by evaluating the hygiene of trays or items stuck within cross-belt sorters or conveyors.
A common barrier to deploying AI in factory automation is the perceived level of complexity. Today, advances in AI technology, like edge learning, are changing that narrative.
Edge learning is a game-changing technology that’s both extremely capable and simple to deploy. It can automate a variety of tasks, without prior AI experience or technical expertise. From part inspection to sortation and character reading applications, edge learning is the easy answer for bringing automation to the factory floor.
Research and information regarding AI manufacturing and procedures described above were sourced from the following:
Rapp. (2022, January 7). Artificial Intelligence in Manufacturing: Real World Success Stories and Lessons Learned. Retrieved from: https://www.nist.gov/blogs/manufacturing-innovation-blog/artificial-intelligence-manufacturing-real-world-success-stories
Dr. Fujimaka. (2020, December 7). Removing Barriers to AI Adoption in Manufacturing. Retrieved from: https://www.automation.com/en-us/articles/december-2020/removing-barriers-to-ai-adoption-in-manufacturing