As end-users of lubricants move to Industry 4.0, which links data, connectivity, automation and artificial intelligence (AI), there is a growing use of smart manufacturing to improve productivity and reduce costs. The move to Industry 5.0 is prompting the need for human involvement in facilitating the use of smart machines that can interact with each other and with individual operators. 

In the 2023 STLE Report on Emerging Issues and Trends in Tribology and Lubrication Engineering,¹ work was discussed that is promoting smart manufacturing by the use of a “smart factory” to help users evaluate the interaction of AI, machine learning, big data, cloud, edge applications, robotics and computer vision. The prospect of using a technique known as tribotronics may dramatically improve the application of AI and machine learning to condition monitoring of lubricants. In this approach, a controller can be used to receive information on the condition of a specific tribological system from sensors and then modify the condition of the system, if necessary, by changing such parameters as operating conditions and lubricant feed rate.

But for tribotronics to work effectively, communication among machines and human operators must be efficient, low-cost and, if possible, not use a lot of power. Atsutse Kludze, graduate student in the Department of Electrical and Computer Engineering at Princeton University in Princeton, N.J., says, “A method known as backscattering can enable a device to piggyback its data on the ambient wireless signals instead of generating its own radio frequency (RF) signals. As an example, consider an individual with a wireless device communicating with a cell tower that generates signals at a specific RF. Instead of generating its own RF signal to respond back, the phone device replies back by reflecting the tower’s signal. This process does not involve the need for using power-hungry components such as oscillators, and amplifiers.”

Backscattering appears to be a useful approach for connecting internet of things (IoT) devices because high-cost and power-demanding circuits are not needed, and the technique can be scaled up to meet specific application needs, according to Kludze. Current networks typically operate at frequencies below 6 gigahertz (GHz), but there are inherent limitations. Kludze says, “Backscattering in this frequency range tends to lead to problems if a network has too many devices trying to communicate at the same time. Unfortunately, as more users and devices are introduced, they tend to interfere with each other, and the information gets jumbled up. Another negative factor is that the communication speeds are slow, and the amount of information that each signal can transmit is low.”

Kludze and his colleagues have now determined that moving the frequency range for communication can enhance communications among generators and receivers and reduce interference. Such an approach has now been designed and evaluated.

Sub-terahertz
The researchers found that communications among machines can be enhanced if the signals transmitted among them are carried out at higher frequencies, in the sub-tetrahertz range. Kludze says, “Signals in this frequency range can be directional to a specific target and not spread out over a wide range where interference is a possibility.”

Kludze indicates that sub-tetrahertz range offers a larger capacity of concurrent device image. He draws an analogy to two different bodies of water: “Transmissions operating in the sub-6 gigahertz range are so congested as to be in a bucket of water. In contrast, working in the sub-tetrahertz range offers the opportunity to transmit in an ocean of water where there is a large amount of freedom to direct signals accurately and without interference.”

To prepare a scale-model demonstrating the efficacy of communicating in the sub-tetrahertz range, the researchers worked with leaky-wave devices. The experimental setup is shown in Figure 1.


Figure 1. An experimental setup was used to demonstrate the effectiveness of leaky-wave devices to communicate with each other using backscattering in the sub-tetrahertz range. Figure courtesy of Princeton University.

Kludze says, “Leaky-wave antennas can be envisioned by gaining an understanding of how white light can be transmitted through a prism. As the white light is refracted in the prism, different colors move away from it in different directions. Yellow light may move to the left of the prism while purple light may move to the right. In essence, the light is moving through an aperture (the prism in this case) to ‘leak’ out into free space.”

The design of the scale-model test bed involves measuring the angle-frequency features in a parallel-plate leady wave device with a single rectangular slot in multiple transmit and receive configurations. In one experiment, the researchers broadcasted ultra-wide band pulses in space to three leaky-wave backscatter devices within the expanded cone range of the beams. In response, each of the devices was able to simultaneously send data to a sub-tetrahertz reader at specific frequencies.

From a power standpoint, Kludze indicates that the sub-tetrahertz system minimizes the need for complex and power-consuming array architectures. This development has the potential for improving communications among machines in an industrial setting.

For those of us monitoring lubricants, such a setup could dramatically improve communication among machines leading to quicker resolution of issues before they become a problem that might lead to a shutdown of production in an industrial facility. 

Kludze says, “We are working to make the sub-tetrahertz communication system more amenable for commercialization through scaling it up. This will include improving the quality of the components and enabling small devices to be able to use it.”

Additional information can be found in a recent article² or by contacting Dr. Yasaman Ghasempour, assistant professor of electrical and computer engineering at Princeton University, at ghasempour@princeton.edu. 
REFERENCES
1. Canter, N. (2023), “2023 STLE Report on Emerging Issues and Trends in Tribology and Lubrication Engineering,” pp. 40-42. Available at www.stle.org/files/White_Papers/2023_Emerging_Trends_Report.aspx.
2. Kludze, A., Kono, J., Mittleman, D. and Ghasempour, Y. (2024), “A frequency-agile retrodirective tag for large-scale sub-tetrahertz data backscattering,” Nature Communications, 15, Article Number: 8756.