Improving Quality Control in the Manufacturing with Machine Vision

Machine vision (MV) systems in manufacturing join the power of sight with data analytics to drive a level of efficiency and accuracy once thought impossible. Near-perfect levels of quality control represent just one of many results associated with machine vision. Moreover, MV systems are increasingly viewed as indispensable for any process requiring inspection.

Quality control brings consistency and accuracy to manufacturers , where many challenges can derail even the most precisely planned, tech-driven processes. When things go wrong, quality control provides a much-needed safeguard — the opportunity to spot and resolve errors before products reach customers. This limits the likelihood of customers receiving faulty items — and it can have huge implications for long-term customer trust and satisfaction.

Quality control initiatives have frequently failed to live up to their full potential. Given the rapid pace of operations that are always increasing due to heightened consumer demands, it’s easy to see why defects are missed. Traditional quality control often involves manual processes, where the human eye can quickly become fatigued and fail to catch errors that customers will no doubt notice. In addition, manual processes are highly inefficient, often prompting significant bottlenecks in production, along with high labor costs.

The good news? Cutting-edge technology promises to resolve these issues. Increasingly, modern QC systems incorporate machine vision, with sensors, smart cameras and advanced software algorithms working together to inspect items at a rapid pace. Integrating machine vision can be challenging, but the payoff can be significant with substantial improvements in both efficiency and customer satisfaction.

Understanding Machine Vision

By allowing computer systems to mimic human sight, machine vision uses sophisticated cameras, lenses, lighting, and advanced software to perform real-time, in-depth analysis of objects that come into its field of view. Images are captured from a specified area and converted into digital signals, which are then processed by the computer system. Processing can involve filtering to enhance certain features or limit distortions known as noise, and from there, feature extraction can be used to reveal specific, desired characteristics to which all other images are compared. By integrating machine learning and even deep neural networks, machine vision systems uncover actionable insights that refine processes, increase accuracy, and enable predictive capabilities with precision that only this technology can provide.

Advanced algorithms make it possible to analyze previously processed images, comparing them against standards while also revealing noteworthy patterns across high volumes of products. These insights can play heavily into decision-making strategies but may also trigger automated responses, promoting a smart supply chain that incorporates quality control safeguards once thought limited to the human eye.

The Role of Machine Vision in Quality Control

While machine vision can serve many functions in the modern supply chain, much of its appeal centers around quality control. By leveraging processing strategies (such as feature extraction) along with high-level algorithms, MV systems can automate many aspects of the inspection process, which would otherwise be handled by traditional inspectors or quality control specialists.

Enabling high-speed inspection, MV-oriented quality control systems ensure that all products are examined while also limiting the bottlenecks that naturally arise when high-speed production lines are combined with manual inspection processes. These systems also improve accuracy and attention to detail, verifying precise dimensions and angles that can influence functionality in not only the automotive and electronics sectors, but also in fields like aerospace, medical device manufacturing, consumer goods, and any other industry where precision specifications are critical to performance and quality.

How Machine Vision Improves Quality Control Processes

Machine vision’s image processing and high-resolution cameras bring a highly standardized approach to the inspection process. MV systems can detect minute differences in products or packaging that may not be immediately evident even to the most detail-oriented employee. While there is some room for interpretation when humans oversee inspections, these nuances disappear as machine vision enters the picture, eliminating human error on the production line.

Instead, strict quality control benchmarks reveal acceptable ranges for a variety of features — and the moment products fail to adhere to these, they trigger an alert by the MV system for additional attention. AI-powered deep learning models drive ongoing improvements in accuracy, enabling these systems to continuously analyze visual data and optimize inspection processes. Standardization ensures uniformity in MV inspections and ultimately, in product quality, ensuring that products align with expectations.

Advancements in MV Technology

Machine vision technology is advanced in its own right, but these solutions have improved dramatically in the past few years. The use of previously mentioned neural networks, for example, has proven transformative, allowing for dramatic improvements in analysis and interpretation.

The Association for Advancing Automation (A3) highlights a promising example of machine vision’s potential for detecting defects: Ericsson and ABB Robotics have joined forces to create a 5G-enabled MV system, capable of producing high-quality footage in real-time.

This 5G-oriented solution promotes swifter and more accurate defect detection, reflecting, as Ericsson highlights, the increasing need for solutions that are “accommodating of different processes and production models.”

Collaborative robots — already heavily used in picking and packing — will also play an important role in shaping the MV solutions of tomorrow. These robots can move products to dedicated inspection areas to increase efficiency. They can also separate items deemed defective from other objects on the production line. Integration with advanced AI can prompt rapid reconfiguration and ensure precise handling among robots on the production line.

Applications of Machine Vision for Quality Control

Machine vision can influence many aspects of quality control, bringing greater efficiency and accuracy to every corner of the modern supply chain. These systems are already heavily used across several sectors, optimizing quality control in industries ranging from electronics to food and beverages and pharmaceuticals. These sectors all use MV systems differently, but these use cases are especially common:

Inspecting incoming materials. Raw materials must meet stringent standards before they can be used in production. Machine vision systems can inspect these materials for weaknesses, addressing issues quickly before valuable resources are wasted creating or assembling products that will be unable to meet strict quality thresholds.
Revealing product defects. As one of the most recognizable use cases for machine vision in quality control, defect detection involves examining products for problems. These involve not only surface issues such as dents, dings, or scratches, but also warping or color inconsistencies. Furthermore, these systems may determine whether there are missing parts — or whether any other mistakes have occurred during the assembly process.
Packaging inspection. In addition to inspecting products and their components, machine vision systems can verify that these items are packaged correctly prior to shipping. This can ensure that packages are properly sealed, thereby reducing the potential for damage in transit. MV-enhanced inspections can also reveal labeling accuracy, label placement, the presence or absence of required warning or compliance labels, barcode accuracy and more.
Production line monitoring. While many machine vision systems focus on specific products or components, the processes that underscore their assembly also warrant attention. This is where production line monitoring can prove valuable, revealing. not only which products are damaged and when, but also which areas in the production line are most likely to cause quality control issues.
Predictive maintenance. Although frequently associated with product inspections, MV-enabled systems also enable predictive maintenance, which aims to extend the lives of high-value assets by determining when equipment requires attention. This prevents early problems from spiraling out of control and can therefore greatly improve the return on investment for high-cost assets.

Overcoming Challenges

Machine vision systems allow enterprises to overcome persistent quality control challenges, including, most notably, the time-consuming nature of manual inspections. Instead of relying exclusively on the human eye, machine vision can replace this, allowing automated solutions to detect problems that can later be examined in more detail by employees. This increases the likelihood that defects will be caught and addressed early on — and the long-term impact could include dramatic improvements in customer satisfaction and production line efficiency.

The wealth of data produced by MV systems cannot be discounted. This information supports not only immediate quality control efforts, but also long-term strategies involving data-driven decision-making. Over time, information collected by these systems could promote predictive maintenance and even help organizations address problematic patterns. Improved traceability is also to be expected in response to this increased volume of data — and this could have positive implications from a compliance standpoint.

Moving Forward with Machine Vision Solutions

As cutting-edge solutions — such as deep neural networks — continue to make their mark in manufacturing, warehouses and throughout the supply chain, MV systems will continue to improve. Deep learning has greatly refined image processing in recent years, with additional improvements expected. Meanwhile, advances in camera technology will produce higher-resolution images so that even the most minuscule defects can be detected.

Moving forward, MV systems will also become more adaptable, adjusting to accommodate a wider range of settings and situations. And with continued improvements in processing and analytic capabilities, the return on investment in machine vision systems will likely continue to increase.

The impact of Industry 4.0 cannot be overstated. Representing an era of digital transformation, the Fourth Industrial Revolution promises to build cutting-edge digital solutions into once-manual industrial processes. As mentioned, machine vision significantly improves data collection and analysis to promote real-time insights. This can be integrated with artificial intelligence to promote smart manufacturing and a smarter supply chain.

Now is the time to join the machine vision revolution. If you’re eager to explore this exciting technology, look to Peak Technologies for insight. As a certified Zebra machine vision integrator, we’re pleased to offer custom solutions. Our goal: to help you leverage the power of machine vision. Get in touch today to learn more about our end-to-end solutions.

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