Marriage between hardware and software drives success in today’s automated visual inspection

The goal of a visual inspection is to find anything that might be wrong with the asset which could require maintenance.

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Industry 4.0 visual inspection systems depend on advanced technologies and are designed to adapt to the fast-evolving production requirements where the quest for excellence is a non-negotiable imperative. Being not just a quality control measure, visual inspection has a more sophisticated role to play in establishing a competitive manufacturing process. Furthermore, manufacturing customers are increasingly demanding solutions where decisions lead to swift and accurate actions in achieving quick success in the inspection process.  Multiple production lines that operate at record speed and evolving quality standards require a more sophisticated approach in the process.  This is made possible only with holistic visual inspection solutions that seamlessly integrate both intelligent software and advanced hardware. The automated visual inspection that marries both the hardware and software that is gaining traction today is a new-age superior alternative to the manual quality control process.  


The aim of such systems is to collaborate with humans and not substitute them in a way that has not been done before thereby giving a quantum leap to quality while retaining knowledge and improving efficiency.

Hardware versus Software

AI-driven visual inspection has revolutionized manufacturing by seamlessly integrating advanced hardware components and intelligent software solutions.  High-resolution industrial cameras are indispensable.  The other hardware components include a PLC, electrical components, and mechanical systems such as conveyors or cobots/robots.  Cameras, lighting systems, and sensors each play a key role in ingesting the data into the AI platform. They capture high-resolution images with the right amount of light exposure to expose the smallest defects in record time.  The software leverages AI, Machine Learning technologies, and sophisticated algorithms to process the raw ingested data captured by the hardware and make decisions.  The result at high speed e.g., 10,000 items/minute in the case of a CPG company or 125 parts/minute in the case of an auto is recorded with > 99% accuracy. The decision is provided to the action layer. The action of sorting OK and Not OK products is performed by the ejection systems based on the established business rules. Due to the complexity involved with products that are to be inspected which are moving at high speed, the decision to action is as complex as the action itself.  Many a time the products in the OK and the Not OK bins get mixed up leading to bad customer experience although the accuracy of the decision is still at 99%.


Decision versus Action

Industrial cameras and advanced sensors generate vast amounts of data in real-time with the capturing of images where every nuanced aspect is recorded.  Then the decision-making AI algorithms based on knowledge acquired to make decisions on the product’s quality prompt the hardware to act by sorting the OK and the Not OK ones.  The whole process is across three layers that include data ingestion, decision, and action.

Let us consider a scenario where a decision has to be made on an assembly line that rolls out 10,000 biscuits per minute.  Let us assume 2% of them have defects which amounts to 200 biscuits per minute that are classified as Not OK.  The next step of translating these decisions into actions is equally critical. The action should indicate if the biscuit requires to be disposed of or sent for further inspection and more importantly, this has to be quick.  Failure to act in time leads to good products being ejected and bad products being sent to ok besides leaving the end customer dissatisfied.  Furthermore, the sorting process has to be precise as well, based on the inspection criteria, and should be executed quickly on high-speed lines.


Convergence of Decision to Action

Precision in the marriage of decision-making to action is only achievable through the implementation of an advanced algorithm capable of assuring effective state management throughout a product’s lifecycle on the platform.

This will ensure the inspected products are then directed to the OK, Not OK, or further inspection containers.  With the help of self-learning, and adaptive AI algorithms, the entire system can improve its accuracy and adapt to variations in the product.  Further innovation can help in discovering new ways to integrate decision-making with quick precision actions during the visual inspection process.


Real-time processing is critical to facilitate rapid decision-making

Visual data is captured with high-resolution cameras and sensors where detailed information about every product is recorded and transmitted in real-time and is necessary on shop floors where the production life cycle is measured in milliseconds. Post this, software algorithms powered by AI and machine learning rapidly analyze the captured data, where every product is thoroughly assessed at high speed, which is a critical factor.  Holistic visual inspection solutions where software is integrated with the hardware also eliminate bottlenecks and play a key role in industries where massive volumes of products are manufactured within very tight timeframes.

Immediate Action Execution is driven by Real-time decision making


As the decision-making is done rapidly in real-time, it drives the speed and efficiency of manufacturing operations, enhances throughput, and minimizes the moving of defective products to the next stage.  Decision-making also builds the foundation for immediate action execution.  With quick decisions, subsequent actions of sorting, and further investigation are also done with great precision and speed.

Adapting to change and self-training

What is ok today is not ok tomorrow as customer expectations are constantly evolving!  Products are continuously evolving and quality standards are improving further in today’s manufacturing environments, compelling the use of holistic visual inspection solutions.  Additionally, with the software algorithms constantly learning from new patterns and defects and adapting to the changes eliminates re-programming often. This ensures visual inspection not only is effective but also applicable in the modern manufacturing setup.

It is now evident how the synergy between the software and the hardware in visual inspection can bring about extremely high levels of efficiency across the manufacturing sector.  Advanced hardware focuses on capturing the data more precisely and the intelligent software enhances decision-making as well as the next course of action to newer levels.  This comprehensive approach, in addition to fulfilling the demands of modern manufacturing, also creates a new benchmark for quality control in today’s digital age.

By Sekar Udayamurthy, CEO and Co-Founder, Jidoka Technologies

DQ Online