Evolution of AI-Powered Visual Inspection – Key Trends Redefining Manufacturing in 2024
By Sekar Udayamurthy, CEO and Co-founder, Jidoka Technologies
Today’s modern manufacturing landscape is dynamic with several new age technologies playing prominent roles. One of the emerging and game-changing technologies is AI-based visual inspection redefining the methods where anomalies and defects are recognised and addressed.
In this article, we explore the trends and impact of AI-based visual inspection across the manufacturing landscape in the coming year, 2024.
Human-AI collaboration
As we stand at the threshold of 2024, the manufacturing industry heralds an important period where collaboration between humans and AI in visual inspection systems is set to redefine the industry’s standards. In today’s age of rapidly evolving technologies, visual inspection in manufacturing is very subjective and heavily relies on people’s experience and expertise for timely and quick decision-making. It is important to note that as AI algorithms get more sophisticated and advanced, the expertise of humans will be highly valued in decision-making and complex inspections. Such collaborative approaches ensure AI to be a trusted partner augmenting human capabilities rather than replacing them. It also plays an important role in addressing complex manufacturing challenges where the AI excels in repetitive tasks and humans contribute to creativity and critical thinking.
Harmonizing AI-based visual inspection with Analytics Platform
In the coming year, the integration of AI-based visual inspection with the Analytics platforms is expected to deliver better operational efficiency, highly valuable insights, and adaptability. Such cases are witnessed where maintenance data on production lines and other connected machinery that generate vast amounts of real-time data are integrated into AI algorithms. With visual Inspection data which is integrated and harmonised, manufacturers can get a holistic view of the entire production process, based on which proactive quality control and informed decision-making are made possible. This integration enables monitoring and analyzing diverse parameters at the same time, such as data on temperature and pressure among other variables in auto manufacturing.
With such capabilities, a Zero-defect outflow to customers can be achieved, providing better customer satisfaction. Furthermore, continuous monitoring of the equipment condition can contribute to predictive maintenance and efficiency of the manufacturing process. Harmonisation thereby enables sustainable manufacturing with a reduction in energy consumption and waste, in addition to improving overall efficiency and even unlocking new possibilities, going forward.
Self-training software to bring a paradigm shift
The introduction of self-training software in the landscape of AI-based visual inspection across the manufacturing sector is expected to be a game changer. This software utilises the real-time data generated during the inspection process to autonomously identify new variations and patterns in product quality. Manufacturers are empowered to review and decide the course of action on the new variations i.e., whether they are acceptable or are they defects that should not reach their customers. Once the data is annotated manufacturers can independently train and deploy AI models on the go.
The teams on the shop floor can now improve quality control processes of the ever-changing production lines quickly and effortlessly. This software leverages the power of advanced AI technologies with continuous learning from different datasets and adopting variations to enhance the accuracy and efficiency of visual inspections to unprecedented levels. Such innovations can take the manufacturing industry to the next level where AI systems can evolve organically ensuring businesses stay ahead of the curve.
As manufacturers embrace the above trends, they can secure a competitive edge by achieving excellence in product quality with AI-based visual inspection in the increasingly complex manufacturing ecosystem.
