Quality professionals in manufacturing live with the nightmare of discovering significant product defects and being unable to quickly identify the root cause. The emergence of deep learning AI is proving to be an ideal option to identify and prevent such a failures.

Deep learning—the method whereby a computer engages multiple levels of “neural networks” to mimic how the human brain solves problems—burst onto the scene just a few years ago. For the companies already employing it, the technology is proving to be a substantial leap forward in quality assurance and process improvement.

Before the emergence of deep learning, automation controllers could only operate within the strict confines of highly limited, rules-based logic. Full-fledged automation was dependent on the programmer’s potential to foresee challenges and predict scenarios. There was little flexibility to react to evolving circumstances or adapt to demands.

Now, deep learning allows a computer to proactively grow its knowledge base both by extrapolating new information from relevant inputs and by creating, in a sense, its own algorithms for doing so. The latest computer systems connect with other information sources and draw inferences for problem-solving from outside of themselves.

A closer look

Matroid, Inc., is capitalizing on this new generation of computing to transform manufacturing with its Advanced Computer Vision and AI-Driven Object Detection systems.

Generally, computer vision uses imaging technology to capture images. It then employs computer-based criteria to interpret, recognize, analyze, and derive conclusions from those images.

As a leading computer vision platform and solutions company, Matroid uses deep learning to take computer vision to the next level. It simplifies communications between data scientists, programmers, and technical processes to create an active system for automated defect detection & classification, assembly process monitoring and continuous cycle-time analysis. It can search archived or monitor live visual media in milliseconds and at scale.

Computer vision paired with deep learning technology is already well on its way to becoming a prominent aspect of many manufacturing AI systems. The high level of interest is due to its ability to integrate cost-saving efficiencies that are difficult to replicate, even with an increased workforce.

Take for example robotic welding systems, which have historically required a human inspector to confirm the quality & integrity of welds as workpieces move through the production line. However, this approach subjects manufacturers to the risk that a problem could become widespread before it is identified and corrected, increasing the potential business impacts resulting from it.

With deep-learning computer vision from Matroid, not only can each weld be inspected to meet a minimum standard, but its condition can be further classified, just as a weld engineering expert would. The technology can evaluate porosity, spatter, burn-through, and other factors.

Most importantly, the visual inspection can be done earlier in the production process so that the engineers responsible for the robotic systems can make adjustments in close to real-time, minimizing the impact. In addition, the valuable digital record created over time provides long-term analytics to aid with continuous improvement initiatives.

Deep-learning sense

The benefits of these AI advancements go well beyond welding applications, though. Deep learning and computer vision can enhance business and drive value across quality, operations, and even safety.

State-of-the-art computer vision solutions streamline manufacturing or monitoring processes, establish continuous improvements, and accelerate the exploitation of those improvements. The data they provide empowers manufacturing teams to capture actionable insights, aid in implementing zero-defect initiatives, and craft “work-to-zero” safety strategies.

Deep-learning AI can also be especially beneficial even in processes requiring a high degree of both precision and repeatability. When manufacturing processes for aerospace and automotive components, metal forming and assembly, 3D printing, bioengineering, and similar industries rely on manual production or even manual oversight of traditional machine vision systems and manual visual inspection, the margins for error run high.

Reliance on a human visual inspection component introduces challenges like fatigue, repetitive motion stress, a lack of 360° vision, and inherent subjectivity. Traditional machine vision systems also have to account for their passive nature and poor capacity for detecting complex defects that might vary in shape, size, color, texture, or placement.

The limitations of these tools mean a system may be unable to recognize even a significant problem. Blind spots can also result from initial programming issues or operator errors. Rules-based algorithms incapable of handling variability, such as differences in lighting, colors, or product positioning, only add to the problem.

Solving recurrent challenges

Fortunately, Matroid is computer vision made simple. Matroid gives the complex technology of deep learning and computer vision an easy-to-use and functional interface that industry experts can master quickly. Matroid monitors input from cameras (video / image) and provides real-time alerts and analytics to detect defects, objects, and events of interest.

For added convenience, the results are also accessible through any direct input, such as production systems, QMS, and mobile devices. It is a powerful tool for manufacturers who seek to identify trends and patterns in a manufacturing system to fine-tune precision processes, reduce errors, and improve productivity.

This means it not only can detect inefficiencies and capture weak points in a precision manufacturing process or staff-operated assembly line but can also anticipate problems or offer solutions to line imbalances that lead to costly idle time. Manufacturing managers and team leaders can count on having the data necessary to plan strategically for optimal production flow, circumventing problems and potential bottlenecks.

Matroid’s system is camera agnostic, allowing it to work seamlessly with existing vision systems. Connect any image-or-video-based camera, including new or legacy cameras of any resolution or spectra. Once the cameras are set up, the target process is demonstrated to the cameras, and the No-Code software is a simple point-and-click experience. The method is similar to teaching a new employee.

Matroid users enjoy better defect detection & classification, adaptability to specific use cases, and the facility to use image or video-based cameras. Quality and flexibility sets Matroid apart from companies that can only offer anomaly detection and not precisely classify the defects or handle video data, which is critical for process monitoring and SOP verification.

An added feature of Matroid is its people anonymization option that allows for the filming of personnel without compromising privacy. When implemented, the resulting videos can be converted into training videos.

With Matroid’s software, your organization can efficiently extract and process relevant camera information, facilitating timely data collection and analysis. The Matroid system operates 24/7, providing continuous monitoring and real-time insights into machine and personnel activities without disruption. It also produces video summaries and can extract short clips of cycle-time analysis of manual operations. All of the data is presented via a user-friendly and easy-to-understand format to support data-driven decisions that enhance operational efficiency and improve product and personnel outcomes.