The Role of Electron Diffraction in Industrial Automation Applications
Published by : Industrial Automation
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.
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 electron diffraction market was valued at a huge USD 160 billion and was predicted to grow rapidly at a CAGR of 8.4% until 2026, bringing it to a value of USD 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.
Electron Diffraction is Vital to Developing Material Analysis
Electron diffraction is essential in developing material analysis capabilities in next-generation industrial automation equipment.
The wave nature of electrons is described by electron diffraction. It is a technique that investigates materials and indeed any matter by investigating the interference pattern caused by firing electrons at a sample. Scientists describe this effect as the wave-particle duality, the laws of which state that electrons can be regarded as waves.
Electron diffraction has a number of uses in hard science, mostly in solid-state physics, as well as in chemistry for the purpose of studying a material’s chemical structure. Uncovering the chemical structure of a sample can be useful in identifying it. This can be vital in automation, allowing machines to visually inspect and recognize items without human assistance, as well as playing an essential role in quality control.
The main method of carrying out electron diffraction experiments is with a transmission electron microscope (TEM), although a scanning electron microscope can also be used. The process involved in using either of these instruments involves using an electrostatic potential to accelerate electrons until they gain the desired level of energy. Their wavelength is determined prior to them interacting with the sample under investigation.
The electrons are then passed through the sample, where they are scattered, before moving through an electromagnetic objective lens, which has the effect of focusing the scattered electrons that originate from one particular point and move into a specific point on the image pane.
Electrons scattered in the same direction, converging onto a single point, is the objective lens' back focal plane and where the diffraction pattern is formed.
Material identification by electron diffraction has been around since the 1950s. It can be used to assist in robotic industrial automation by coupling it with artificial intelligence, enabling robots to recognize the items it is handling. This may be crucial to certain roles, such as putting together parts in car assembly applications.
The technique can also be used to obtain data on crystalline structures, stress, contamination, internal fractures, and more, helping robots to automatically spot faults, preventing them from continuing their journey to the consumer.