Sensor Design
Assembly Line
🌡️🎛️ Why do we need software-defined factories?
The next logical step is to create a software-configurable universal analog front end (AFE). An example is NXP’s N-AFE. In comparison with other universal AFEs currently available, it also integrates all of the discrete components that go into the system, such as protection and signal conditioning, and a slew of diagnostics features. Because it is software-configurable, all the maintenance engineer has to do is replace the sensor and reconfigure the analog input module remotely via the user interface, eliminating the time required to replace the entire measurement unit.
In a system that integrates universal AFEs, the role of the PLC is expanded to include performing safety checks before, during and after these actions by communicating with internal diagnostics and programming terminals to ensure that everything is running smoothly.
Eliminate Conveyor Jam False Alarms to Boost Factory Automation Productivity
Fast-moving conveyors are widely used in factory automation to accelerate production and enhance efficiency. But occasionally, things can go wrong. A frequent problem is jamming; one item gets stuck, and then others quickly pile up behind. This is not only bad for throughput and damaging to the conveyor system, but it can also be dangerous for nearby workers.
Recent laser sensor product introductions reduce the number of false alarms by leveraging more advanced optical technology and software algorithms. This article briefly describes the two types of light sensors used for jam detection: LED and lasers. It then focuses on the time-of-flight (ToF) laser and considers the key factors that determine how well the sensor performs. The article also introduces a real-world ToF laser sensor from Banner Engineering and illustrates how to set one up for a conveyor jam detection application.
🗜️ How to Monitor Material Levels in Tanks to Improve Supply Chain Management
Sensing and measuring the amount of solid, fluid, or granulated materials stored in tanks has become increasingly important due to supply chain challenges and the need to monitor inventory levels and control manufacturing processes closely. Depending on the application, level sensors can be required to be food-safe, withstand high pressures, temperatures, or vibrations, be used in corrosive environments with high resistance to acids and bases, and have a high degree of electrical and thermal isolation to ensure safe operation.
While it’s possible to design level sensors, it’s a complex task fraught with risk. The process begins with matching the measurement technology, such as capacitive, magnetic, ultrasonic, or optical sensing, with the application. The next step is selecting the housing, components, and other materials to support the operating environment. It’s also often necessary to gain safety and regulatory approvals and ensure that the design achieves the required ingress protection (IP) rating. Instead, designers can turn to pre-engineered level sensing solutions that ensure accurate and reliable measurements and speed time to market.
A High-speed and High-precision Color Sensor for Improving Color Management in the Paper-making Process
Thanks to the expanding retail business, the paperboard market in Asia is growing and thus the demand for paper color control is increasing. To meet this need, online measurement of paper chromaticity in the paper-making process is used to ensure strict quality control of paper color. Yokogawa has enhanced the functions of the LED color sensor for the B/M9000VP paper quality control system. A new high-sensitivity spectroscope enables high-sensitivity and high-speed measurements, and a moisture-proof coating on components has improved moisture resistance. With the enhanced functionality and robustness of the LED color sensor, the B/M9000VP has improved quality control in the paper-making process.
AI in the Process Industry
When applying AI to difficult problems in plants, approaches differ depending on whether AI researchers can access useful information derived from similar problems. This article first discusses how to search and identify useful research and literature. If well established AI research is available, the next step is simply to choose an appropriate AI platform. If not, the most serious bottleneck for the problem-solving task arises: how to integrate plant domain knowledge and AI technology. This article presents a solution to the latter case. This solution enables plant engineers to make full use of AI geared for themselves, not for data scientists. AI-based control, which is one of the promising AI applications for plants and is expected to solve difficult problems in plants, is also discussed.