Introduction to Machine Vision (MV) Technology in Packaging
In the dynamic manufacturing industries, packaging is the very first impression of a product and the brand. Any discrepancy in the product packaging – be it a defect or incorrect labelling – that goes unnoticed, may prove disastrous for the product. Their-in lies the opportunity for machine vision technology to play a powerful role in improving packaging process and outcomes. Machine vision technology provides real-time analysis of information derived from captured images to perform complex inspection tasks. But it is easier said than done. This article describes the impact of machine vision technology, its key applications and how machine vision technology is revolutionizing the packaging industry.
The Impact of Machine Vision Technology in the Packaging Industry
Defects unnoticed by the human eyes can be spotted in less than a second with machine vision solutions. This gives a better choice for an inspection process that is fast, detailed, and tirelessly performs 100% online inspections. This results in improved product quality, higher yields, and lower production costs, as well as generating automated reports.
Higher rate of accuracy compared with human inspection, resulting in fewer errors.
Production costs are lower with fewer reworks, saving cost by reducing waste and manual efforts in turn.
Keeping flawed products off the shelves, high-quality products, and fewer errors strengthen the brand integrity in the long run.
Improve your bottom line by utilizing employees in other areas of the production line without manual inspection team.
Evolving in the competitive environment of fast-moving consumer goods, the challenge of ensuring a reliable, 100% quality inspection and optimizing cost.
Inspection systems make it possible to demonstrate reliable quality and assurance processes in place to satisfy your customers.
Machine Vision and Its Applications
Today one can find machine vision-based systems for addressing
virtually every known product. Sorting applications based on
geometric property analysis; size and shape. Grading applications
typically entail separation based on photometric properties; such
as appearance, color, and surface. While there are many applications of machine vision in the packaging side, varying widely depending on the industry and production environment. A few typical applications include:
- Component Presence
Checks the presence/absence of components or parts of an object
- Dimensional Quality
Checks the dimensional accuracy on objects against geometric tolerances
- Structural Quality
Checks the missing or presence of foreign components on an object
- Operational Quality
Operation quality relates to the verification of accurate operations of products
- Packaging & Labeling
Controls the alignment, placement of labels, ensures accuracy in packaging by identifying incorrect labels
- Color Monitoring
Refers to measurement and control of a product’s intensity and spectrum of the inspected object’s illumination
Detecting Packaging Defects
The packaging industry is facing significant challenges. Personalization, instead of industrial mass production,
is one of the most common trends in almost every application area. Regardless of the sector where the packaging
is undergoing, be it medical, food, cosmetics, or other industries, they all require machine configurations capable
of changing and adapting quickly.
The role of machine vision technology opens up new opportunities for the packaging industry and shows
how it can be used for fast and reliable inspection of products. This technology provides you the best
automated visual inspection systems for detecting packaging defects with a high performance, customized
deep learning model delivered to the customer to identify samples with defects.
Machine Vision Solution for Packaging
Every problem statement is unique, and we provide custom solutions for every customer’s need, ensuring
Perfection and end-user satisfaction. This approach enables us to offer underlying technologies on
packaging systems to address the specific needs and deliver the best fit for the application. A machine
vision system consists of several critical components, starting from the sensor (camera) that captures
a picture for inspection to the vision appliances that render and communicate the result, so it is essential
to understand how the machine vision system works reliably to generate results.
How does machine vision technology work?
Image Acquisition
The system automatically captures images containing required information through cameras.
Processing
Analyze the captured image using custom algorithms,to enhance the elements that have specific importance to the process.
Feature Extraction
A set of critical features are identified and quantified in the image to send the data to a control program.
Action
Gaining insights from the model’s result.
Adding Deep Learning Technology to Machine Vision Solutions
Deep learning techniques are all set to transform businesses, in ways never seen before. The use of sophisticated, multi-level deep neural networks is giving inferences, insights, and decision-making processes, as advanced as human cognition to the businesses. In the case of images, deep learning requires computing power and involves feeding a large amount of data through multi-layered neural network architecture to create a model that can classify the objects contained within the image, having done through a superior learning ability to make better use of datasets for feature extraction. In such applications, deep learning helps machine - vision systems to cope with the range of acceptable part appearances.
Read more about our deep learning methodologies for various applications
Conclusion
Manually inspecting tangible defects is not always reliable. It is also labor-intensive. To overcome
these limitations, automated inspection uses image processing techniques that are potent enough
to significantly improve quality and increase production rates in the industry by delivering a
high performance customized deep learning model for identifying packaging defects in real-time.
Email: marcom@pathpartnertech.com
Further Reading
- 4 ways Artificial Intelligence (and Deep Learning) is transforming automotive industry
- Industrial IoT, transforming the way modern warehouses operate
- How to Integrate Industrial Robots and Machine Vision in Manufacturing: Beyond Industry 4.0
- Developing Deep Learning Algorithms on an Embedded Platform
- Build next-gen product with computer vision
- A brief analysis of Indoor Positioning market
- The Evolution of Smart Home
