How Electron Diffraction is Expanding the Industrial Automation Capabilities?
Published by : Industrial Automation
The industrial automation sector is growing rapidly, and much of this growth is attributed to the increasing demand for industrial robots with AI capabilities to do jobs such as pick and place, part transfer, packing, and semiconductor manufacturing.
In 2019, the market was valued at $160 billion and was predicted to grow rapidly at a CAGR of ~9% until 2026, bringing it to a value of $300 billion. To meet the requirements of an expanding customer base, the industry will be focusing on developing its capabilities to extend offerings and benefits to customers.
Material Analysis Will Be Developed for Future Industrial Automation Systems
Automation will develop in three levels. The first will be at the hardware and software level where capabilities such as natural language conversations, self-programmable PLC, autonomous flexibility, and predictive maintenance will be the focus of imminent developments.
The second level will be developing attributes such as real-time processing and expandable storage in the CPU/memory of the computers managing the automation systems.
The third level will be improving the output and visualization capabilities of these systems, such as simulative and immersive technologies, real-time work instructions, improved co-ordination, high-resolution machine vision, and material analysis. This article will focus on the development of one of these capabilities, in particular, that of material analysis.
Future Advancements in Industrial Automation
Adding this capability to automation processes would enhance them by giving them the means to quality test products without the need for human workers to remove samples from the production line and engage in lengthy testing processes.
Electron diffraction can establish the short-range order of amorphous solids, identify imperfections known as vacancies, and study the geometry of gaseous molecules. It can also help identify stress and internal fractures that may compromise the function of the quality of the product. Automatically spotting these features can enable them to be automatically removed from the production line. As discussed, electron diffraction is an essential tool in material identification, which may provide useful in combination with artificial intelligence to guide robots in more complex automation processes.
The challenge moving forward is to establish reliable ways to incorporate electron diffraction methods into industrial automation robotics. The future of industrial automation will likely see these advancements being made to enhance the output and visualization capabilities in the field. These will not be the only advancements in the field. We can also expect major advancements in hardware and software as well as computer CPU/memory to emerge in the coming years.