|
|
|
You can get e-magazine links on WhatsApp. Click here
|
|
|
|
|
|
Automated quality control solutions using AI-based tech
|
|
Wednesday, 06 October, 2021, 08 : 00 AM [IST]
|
|
|
The machine vision systems used to automate food and beverage packaging processes stand right at the intersection of science, engineering, and art. Successful applications do not depend exclusively on any of the three, but rather a bit of each.
The global food and beverages market is on track to grow from $5838.8 billion in 2020 to $6196.15 billion in 2021 at a compound annual growth rate (CAGR) of 6.1%. The market is expected to reach $8163.61 billion in 2025 at a CAGR of 7%. In this dynamic industry, the packaging is the very first impression of a product and its quality is as paramount as the product’s itself. Any inconsistencies in the packaging such as a defect or incorrect labelling that goes unnoticed may prove disastrous for the product and the reputation of your organisation. This is where machine vision technology has the potential to play a powerful role in improving food quality inspection and Safe Quality Food (SQF) inspection.
From the earliest days of machine vision, the technology has been used to address applications on packaging lines. Even the most primitive machine vision systems could perform relatively simple tasks such as simple packaging inspection, verifying that the label was on the package, that it was straight and that the cap was on the container.
Machine vision technology facilitates real-time analysis of data extracted from captured images to perform complex food quality inspection tasks and identify packaging and manufacturing defects. However, it is easier said than done. In this blog post, we explore the impact of machine vision technology in food and beverage quality inspection and Safe Quality Food (SQF) inspection and discuss its applications for you to make the best use of the technology.
Challenges faced in the industrial packaging industry
Some of the common raw materials used by food and beverage packaging companies include paper, plastic, resins, films, paperboard, and adhesives. In recent times, the industry has witnessed a steep rise in the prices of these raw materials. In addition, the prices for polypropylene, used in resins, bottles, and packaging films have been on a rise due to reduced supply, especially in the pandemic scenario. This situation has affected the competitiveness of companies in the F&B and the packaging industry. Organisations can simply not afford packaging defects anymore due to risks it presents.
F&B companies not only have the pressure to innovate in their offerings but at the same time must also ensure that their products are of the highest quality and the packaging is designed to protect the packaged contents from the external environment without any major defects. Choosing packaging materials that can survive external factors such as sunlight, UV rays, high and low temperature, humidity, moisture, and rain is a task of great difficulty. So what can help with food quality inspection and SQF inspection? Yes, you guessed right. Machine Vision.
How can machine vision help?
Today one can find machine vision-based systems suitable for addressing virtually any problems in the food quality inspection domain and the subsequent SQF inspection. Grading applications typically entail separation based on photometric properties; such as appearance, colour, and surface. While there are many applications of machine vision in the F&B industry, varying widely depending on the industry and production environment, a few typical applications include:
? Component Presence
Checks the presence/absence of the correct items or right parts of an individual product.
? Dimensional Quality
Checks how accurate the dimensions of a food object are against geometric tolerances.
? Structural Quality
Checks the missing or presence of foreign unwanted components on a product or package.
? Operational Quality
Operation quality relates to the verification of accurate operations of products ensuring smooth and efficient workflow.
? Packaging & Labelling
Controls alignment and placement of labels as well as identifying packaging defects. Additionally, it can help in ensuring accuracy in packaging by identifying incorrect labels as well as maintain the highest quality of food inspection quality control.
Impact of machine vision in the packaging industry
Defects unnoticed by the human eyes can be spotted almost instantaneously with machine vision solutions. This gives a better choice for an inspection process that is fast, detailed, and tirelessly performs 100% online inspections eliminating all human error. This results in improved product quality, higher yields, and lower production costs better resource management, as well as generating automated reports for further analysis and optimisation.
Some of the major areas of impact observed with the use of machine vision are:
? High Accuracy
The higher rate of accuracy that machine vision offers compared with human inspection, results in fewer errors and higher quality control.
? Cost Efficiency
Production costs tend to be lower with fewer reworks, saving costs by reducing waste and manual efforts in turn.
? Stronger Brand Integrity
Keeping flawed products off the hands of your clients and customers, maintaining packaging quality control, high-quality products, and fewer errors in packaging and inspection strengthen brand integrity in the long run.
? Resource Management
Machine vision significantly helps in improving bottom lines by allowing organisations to utilise employees in other areas of the production line without manual inspection teams.
? Quality and Economic Goals
Evolving in the competitive environment of the fast-moving F&B industry, machine vision can help overcome the challenge of ensuring reliable, 100% quality inspection, quality control as well as optimising cost.
? Stronger Customer Relationship
Inspection systems make it possible to demonstrate reliable quality and assurance processes in place to satisfy your clients and further build your relationship.
Conclusion
Manually inspecting tangible defects is not always reliable. It is also labour-intensive. Why not choose a system that has a proven accuracy of more than 98%? How? All of this is possible by delivering a high-performance customised deep learning model for identifying defects in products and packaging in real-time using machine vision.
Raghava Kashyapa , (The author is CEO/founder, Qualitas Technologies Pvt Ltd)
|
|
|
|
|
|
|
|
|
|