In today’s world, artificial intelligence (AI) is transforming several industries, and many of us interact with AI on regular basis in some form or another. From banking and manufacturing to e-commerce and personalized advertisements, AI is becoming imperative in almost every business. Business experts believe that it is an essential tool for data analytics, predictive suggestions, chatbots, and so on.
AI can be defined as any software algorithm which possesses human-like features, such as an ability to learn, plan, and solve problems. These attributes can be groomed, and the system made more intelligent depending upon the type of industry where it is going to be used.
Machine learning (ML) is presently the most common and widely used type of AI in businesses. It is predominantly used to process and analyze large amounts of data swiftly and rapidly. ML algorithms tend to “learn” over time and enhance themselves to produce better outcomes for the tasks that they perform repeatedly. In a typical manufacturing unit where process equipment continuously collects production data via connected devices, it is difficult for humans to process and interpret the massive amounts of data being collected. In such situations, ML-based AI is extremely effective as it can analyze the data by recognizing patterns and abnormalities. For example, if due to some unforeseen activities, the production capacity of a pharmaceutical manufacturing plant is reduced, ML will inform the stakeholders, who can then take appropriate corrective actions.
With the evolution of interconnected artificial neural networks, there has been a rise in the use of deep learning (DL), which is another form of AI. Essentially, DL is a subset of ML and operates with different capabilities. With a DL model, an algorithm can determine on its own whether a prediction is accurate or not, through its neural network. An excellent example of DL are chatbots used on websites, which interact with humans to solve their queries and enrich the customer experience. Another example of DL-based AI are autonomous or semi-autonomous vehicles, which receive information through millions of individual DL models that allow the vehicle to avoid accidents through the use of various safety features.
In this era of smart technologies, there are opportunities for businesses to transcend to new levels. Utilizing the correct technology according to business needs, companies can implement ML- or DL-based AI to develop intelligent infrastructure, potentially revolutionizing the way they compete, grow, and engage with customers.
Harnessing The Power Of AI Technology
AI has created an impact on several industries, including those based on machine vision inspection systems. Being a pioneer in machine vision systems and with a decade of rich experience, ACGI has tapped into some of the essential in-trend business drivers of the market, which require vision systems to be adaptive, selflearning, and able to make decisions. Also, keeping the future roadmap in mind, ACGI is in the process of developing products based on the Internet of Things (IoT) and process analytical technology (PAT), which will be capable of generating huge amounts of data accumulated over long periods, using interconnected equipment. However, to analyze data at such a colossal scale is humanly impossible, and that is where AI can intervene. ACGI has adopted AI technology, integrating it into existing as well as new products. Using AI, our machines can analyze data to foresee the expected load through a discrete type of DL neural network. This data is then turned into insight, the insight into a result, and the result into an action. This approach is known as informative based manufacturing” and is widely discussed on platforms such as Industry 4.0.
Current Challenges In Pharma Vision Inspection
In the pharmaceutical industry, once tablets or capsules are manufactured, they are sent to be packed in a blister using a blister packaging machine. In most cases, the tablets or capsules are manually fed into the hopper of the packaging machine. Hence, there are chances of errors occurring during this process. Below are some of the commonly observed problems that occur during this process:
- Foreign particles
- Crushed/broken product
- Only body/cap in capsule
- Changes in shape, size, and form
- Spots or discolorations
Besides these, there are other challenges that traditional vision systems may not be capable of addressing effectively, such as learning a new model for a new product from an operator or detecting defects in products having similarly colored packaging (e.g., grey tablet in grey foil). The learning time for a new product is almost 15–30 minutes for traditional vision systems, and these systems may sometimes fail to detect the aforementioned colorrelated defects, resulting in high rejection ratios and lowered productivity. To overcome these challenges and to further improve the machine efficiency, ACGI has developed an intelligent camera based inspection system for blister packaging machines, which ensures defectless product packaging, with minimal human intervention and no requirement for rework.
How AI-Based Inspection Systems Can Help
AI-based applications involve opinion-based (a software algorithm that possesses human-like abilities, such as learning, planning, and problem solving) inspection and therefore, are more efficient than manual inspection or traditional machine vision systems. Several manufacturers are now opting for AI to find solutions for their most complex inspection requirements. AI-based image analysis is a combination of learning and experience gained by human visual inspection, having the consistency and speed of a CPU-based system. AI learning models can effectively resolve tedious vision-related tasks that would be nearly impossible to perform using traditional machine vision systems. These models can identify minute defects such as low contrast product-and-foil combination (e.g., white on white or grey on grey). In addition, the learnings of various defects captured by trained model can be transferred to new models so that every new model created will already know how to detect such defects, thus eliminating the need for repeated learning.
An inspection system on a typical blister packaging machine is positioned immediately after where the tablets/capsules are dropped from the hopper and placed into the cavities of the base foil. A reference image of a good product is fed into the system and after that, the camera, along with software-based image algorithms, continuously captures images and compares them with the reference image. With ACGI’s AI-enabled inspection system, the teaching or setup process for a new model is achieved via a single click, which minimizes the product changeover time. If there are any defects or rogue products, the camera provides a rejection signal, and the defective blister gets rejected at the end of the line. All the faulty blisters are automatically collected in a separate rejection bin without any manual intervention.
The above is an extract from a white paper. The full paper can be downloaded here.
For more information: www.acg-world.com