How digital process monitoring drives sustainability
Predictive tools, standardized data, and self-learning production systems can go a long way in helping manufacturers save energy and minimize their environmental footprints.
As energy becomes more expensive and sustainability more prized, manufacturers are under the gun to reduce energy consumption. Process monitoring, which standardizes and optimizes manufacturing processes— helps to make this possible in the age of Industry 4.0.
What is process monitoring, exactly?
Process monitoring ensures modern manufacturing equipment is flexible, sustainable and easy to operate—all with minimum human interaction. Monitoring is the first step to create an effective control process. Sensors collect accurate and real-time data that can be shared through interconnected machine tools. This all contributes to a complete overview of the factory.
Process monitoring is the management of sensor measurements, such as force, vision, and temperature, which helps organizations determine the state of both a machine and a manufacturing process.
A machine tool operator performs routine monitoring tasks to detect missing and broken tools and monitoring algorithms filter sensor measurements along with operator inputs to determine the process state.
From process monitoring comes process control, where sensor measurements, monitored by signal processing, gauge these complex processes. Process control, in other words, is the management of process variables—such as feed, speed, and depth-of-cut—to regulate processes. Machine tool operators perform both on- and off-line process control by altering feeds and speeds to suppress chatter, or introducing emergency stops when tools break, for example.
How does it help sustainability efforts?
Industry 4.0 has ushered in digitalization to the shop floor. Digitally controlling production processes supports sustainable decision-making through autonomous machine tools, which then reduces energy consumption through large, low-cost wireless sensor networks.
The digitization and complexity of Big Data in today’s manufacturing environments makes the process of monitoring and analyzing data for quality control difficult. Networks can crash, and valuable information can be duplicated or lost. This can lead to errors, lead to lost productivity, squandered energy and unnecessary waste.
But the use of cyber-physical process monitoring systems can predict future energy consumption rates depending on machines tool schedules. Wireless sensor monitoring networks can collect and save large quantities of shop data, monitoring vibrations, currents, voltage and temperature from machine tools to modify their schedules and reduce energy consumption.
Self-learning production systems can also help. They can predict future utilization of machine tools to suggest changes in energy-saving modes. Data management tools also help to standardize data, enabling interoperability and ultimately making data collection more efficient.
As Industry 4.0 opens up a new range of opportunities to advance process monitoring and control systems, it goes a long way toward supporting sustainable decision-making. Autonomous machine tools help manufacturers to implement sustainable programs, or to reduce energy consumption by implementing large, affordable wireless sensor networks.
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