Integrating Machine Vision into Existing Production Lines: Challenges and Tips

Machine Vision in Manufacturing

Machine vision refers to training machines to interpret visual information. Utilising sensors, image-processing software, and next-generation AI models to analyse real-time images, it is possible to integrate machine vision into manufacturing. By automating visual tasks with machine vision technology, organisations can achieve speeds and performance previously unattainable. 

From inspecting raw materials and counting parts to identifying defects, tracking specifications, and more, industrial machine vision enables higher levels of analysis at faster speeds compared to humans. However, upgrading existing production lines through machine vision integration is not an easy task.  

You need to design and implement high-throughput vision inspection systems that deliver accurate datasets for complex algorithms to analyse in real-time. All this while minimising the downtime required for implementation and maintenance, and also considering flexibility.  

More general-purpose systems provide quality data that can be fed into different models to complete multiple distinct tasks across the production line. This simplifies deployment by integrating the same hardware at different stages of the production process, or allows operators to combine functionality utilising a single machine vision system for various tasks. The added flexibility of machine vision in manufacturing is crucial, as the industry faces shorter product lifespans, and businesses must constantly adapt to new production processes. 

While there are challenges to overcome, the benefits of machine vision in manufacturing significantly outweigh any implementation difficulties. In this blog, we will discuss the reasons organisations are upgrading their production lines and integrating machine vision systems, the challenges of retrofitting existing infrastructure, tips for success, and the potential of advanced machine vision systems based on hyperspectral imaging technology. 

So can your legacy production lines keep up with the speed and precision that modern manufacturing demands? Or is it time for machine vision and integrating new technologies to start seeing what humans can’t? 

Why Integrate Machine Vision into Existing Lines? 

Technological advances, including the miniaturisation of sensors/compute and the ability to train AI algorithms on visual data, enable cost-effective, adaptable machine vision systems. These systems can be integrated into existing complex manufacturing environments to enhance operations or provide new functionality. Automating visual inspection to improve performance and speed without hindering aspects of the production line already performing well. 

Machine vision provides a fast, non-contact and non-destructive method of visual inspection for various applications, including: 

• Ensuring the quality of raw materials 

• Component sorting and part counting 

• Identifying defects 

• Checking that products meet specifications 

• Inspecting production equipment for wear and tear 

• QR and Barcode scanning for inventory management 

• Ensuring packaging is sealed and undamaged 

With machine vision, operators receive high-accuracy visual data that can help identify and track quality control issues back through the production process. Machine vision can provide the analysis needed to improve upstream processes and reduce waste. All this, while enabling consistent performance around the clock and the ability to work in hazardous environments. 

These benefits lead to a positive return on investment (ROI), improving quality while increasing throughput and decreasing costs from rejected products and recalls. Below is a table comparing a legacy production line to a modern production line based on machine vision integration. 

	Legacy Production Line	Modern Production Line
Inspection Process	Manual or basic sensor-based	Advanced machine vision systems
Automation Level	Limited due to relying on manual inspection	High levels of automation through integrating machine vision with other technologies
Flexibility	Focuses on fixed operations with little variability	Capable of fast, seamless changeovers and product customisation
Data Use	Little data collection	Visual datasets for advanced, real-time analysis
Downtime	Reactive maintenance and a higher likelihood of unplanned downtime	Monitoring equipment for visual wear and tear to optimise maintenance and minimise downtime
Changeover Time	Longer changeovers that often require significant manual adjustments	Improved, often automated, changeovers are achieved by transitioning between machine vision models tailored to the new product’s requirements

Key Challenges When Retrofitting Machine Vision into Existing Production Lines 

Common hurdles you need to clear in order to access these benefits include: 

• Developing vision inspection systems that deliver the accuracy and speed required. This consists of both hardware (gathering the visual data) and software (analysing the visual data) capable of automating the visual tasks for a specific production line to improve operations. 

• Ensuring this system can integrate into existing production line infrastructure and operate within that environment. This includes hardware factors such as its physical size and power requirements, as well as environmental factors such as lighting conditions and cleanliness. 

• Another critical aspect of ensuring the machine vision can operate in the production line is data management. The machine vision tool must be capable of performing rapid visual analysis, outputting data that is then fed into other systems. This includes whether the data is processed locally to minimise latency or must be sent elsewhere due to compute requirements. 

• Machine vision systems are not operating on their own, their outputs are fed into production control systems to perform tasks based on their analysis. Ensure any machine vision added to an existing production line is compatible with the existing systems. 

• Designing a flexible system that can observe multiple parameters and complete different tasks. This also requires the ability to train new machine vision models for future applications. 

• Acquiring the staff and skillsets needed to retrofit machine vision systems into existing production lines effectively while maximising performance and minimising disruption. 

• Gaining institutional buy-in to overcome the initial investment and convince key stakeholders of the long-term ROI from machine vision integration. 

Tips for Successful Machine Vision Integration 

Listed below are 5 tips to help overcome these challenges and deliver successful machine vision integration within existing production lines: 

1. Create a clear business case for your machine vision integration with specific goals and well-defined KPIs to measure progress. 

2. Assess your existing infrastructure and determine the limitations that any machine vision system must operate within. This typically includes working with machine vision and production line specialists to gather expert opinions. 

3. Design your machine vision system with future flexibility in mind. This could be scalability and implementing new sensors and data management infrastructure, or designing modular systems where parts can be upgraded without requiring an overhaul of the entire machine vision system. 

4. Work with well-respected vendors that provide comprehensive support for implementation and ongoing maintenance. 

5. Train staff to understand the new system, solve quick issues, maximise uptime, and get the most out of its new capabilities. 

While these tips help organisations considering machine vision integration, a great way of developing modern, flexible, high-performance machine vision systems is to look beyond basic visual inspection. 

Hyperspectral Imaging: A Game Changer for Modern Production Lines 

Hyperspectral imaging (HSI) is the perfect tool for new data-driven machine vision applications in the manufacturing industry. HSI views the world using a large number of spectral bands, revealing previously invisible details within an image. With 10s of spectral bands, instead of the three utilised by standard RGB cameras, HSI can observe signatures in reflected light to differentiate between materials and identify chemical or structural parameters.  

This enables more detailed spectral analysis to improve machine vision accuracy while also simplifying the analysis needed. For example, you don’t need to train complex machine vision software to identify and track different objects on a production line based on colour and shape alone. If each pixel already contains spectral data revealing the material present, you can quickly identify and differentiate between objects.  

The necessary information is right there in the data, rather than relying on sophisticated models that only have RGB pixel values to work with. This allows for lighter-weight machine vision models which utilise less compute and can be run locally. Additionally, with more data to go off, new models can be trained faster, leading to more flexible machine vision operations. 

Not only can HSI lead to significantly more accurate machine vision systems, but it can also enable entirely new applications across production lines. Examples include: 

• Identifying defects based on spectral analysis before they are visible to the naked eye or standard cameras for enhanced quality control. 

• Detailed analysis across various production processes to understand the causes behind common defects. 

• Counterfeit detection and quality control when receiving raw materials from suppliers. 

• Finding foreign objects in bulk materials. 

• Tracking manufacturing processes in real-time and adapting parameters or conditions to ensure the best possible results. 

While previously HSI cameras were large, delicate systems that only operated in fixed laboratory conditions, technological advances have led to the development of small, robust, and portable devices capable of operating in the field. 

The Living Optics Camera 

In this article we have explored some of the challenges with implementing machine vision in modern manufacturing and shown how utilising snapshot hyperspectral imaging can overcome these. 

• Minimizing disruption and streamlining deployment by utilising the same hardware (cameras, compute, etc.) in the development lab as the final product on the production line. 

• Reducing the bandwidth required for real-time imaging and analysis through the integration of edge compute and running machine vision models locally, on the device. 

• Relying on more detailed visual inspection techniques, such as hyperspectral imaging, and continually improving models for a variety of tasks on the production line. 

Based on never-before-seen snapshot technology, the Living Optics Camera represents a significant leap forward for hyperspectral imaging and is the ideal device for new machine vision systems in manufacturing.  

By capturing all of the data needed for a hyperspectral image simultaneously, the Living Optics camera returns high spectral resolution data at video frame rates. This translates to higher-accuracy machine vision systems that can keep up with the outputs required for modern manufacturing facilities. 

Contact our sales team to start developing your next-generation machine vision system based on real-time spectral analysis.

 

FAQs 

What should I evaluate before integrating machine vision into a legacy production line? 

Critical factors to evaluate when integrating a new machine vision system into a legacy production line include: 

• The existing layout and any constraints they impose (size, power requirements, etc.) 

• Lighting conditions 

• Compatibility with existing systems 

• Data management and potential networking needs 

• Potential downtime during implementation 

How can hyperspectral imaging improve inspection accuracy? 

With detailed spectral information across the image, HSI offers advanced inspection accuracy compared to simple RGB sensors that only rely on shape and colour. HSI returns data on the materials present as well as other chemical properties to reveal information not visible to the naked eye. 

What kind of support or training is needed for operators using vision systems? 

Operators using machine vision systems require targeted support and training to ensure they can effectively monitor and maintain the systems. This includes an understanding of how the vision system works, as well as hands-on instructions covering tasks such as starting and stopping the system, adjusting settings for different products, and responding to alerts or false positives. 

How can digital twin simulations help in planning machine vision integration? 

Digital twin simulations create a virtual replica of the entire manufacturing environment. This can help optimise camera placement, lighting configurations, and inspection angles without disrupting live production during machine vision integration.