From "Manual" to "Intelligent": The Generational Transformation of Machine Tool Operation and How Frontline Workers Adapt
In the workshop of a machinery factory in the Yangtze River Delta, 55-year-old Lao Zhou frowns at the operation panel of a CNC lathe. The program codes and parameter compensation tables on the screen feel foreign to him—he’s used to manual handwheels. Right next to him, 25-year-old Xiao Lin taps the touchscreen to set the part processing parameters, and in half an hour, he produces three times more qualified parts than Lao Zhou did with manual operation. This scene is a microcosm of the generational transformation of machine tool operation.
Over the past few decades, machine tool operation has evolved from "fully manual" to "semi-automatic" and now to "intelligent." Back in the 1980s, ordinary lathes and milling machines in workshops relied entirely on manual control. To process a shaft part, workers had to stare at the dial while manually adjusting the tool feed rate. After a full day, their arms ached, and precision depended entirely on experience. Lao Zhou recalls that back then, he spent three years at the lathe to develop a "sense of touch" that allowed him to control dimensional deviations within 0.1 mm. Machine tool operation at that time was a competition of "physical strength + experience," and workers who could skillfully operate multiple machines were the "most sought-after talents" in the workshop.
After 2000, CNC lathes gradually became popular, and operation shifted toward "semi-automatic." Machines were equipped with display screens and operation keyboards; workers no longer needed to turn handwheels manually but instead controlled the processing process by inputting simple commands. However, this transformation was challenging for many senior workers. When Lao Zhou first used a CNC lathe, he read the manual on "G-codes" and "M-codes" repeatedly but still couldn’t tell the difference between "G01" and "G00." In the end, he had to ask young technicians to teach him hands-on. Once he mastered the basic operations, the efficiency improvement was immediate: processing 100 parts used to take 8 hours manually, but the CNC lathe could finish it in 4 hours, with precision stabilized within 0.02 mm.
Today, the emergence of intelligent machine tools has brought another major change to operation methods. Modern intelligent machine tools can not only automatically identify part materials and adjust cutting parameters but also monitor the processing status in real time via sensors. If a tool wears out, the machine will automatically alarm and switch to a spare tool; if there’s a dimensional deviation in processing, the system will independently compensate for it. More advanced machines can even connect to the factory’s MES (Manufacturing Execution System), allowing workers to check production progress and receive fault alerts on their mobile phones. This "low-intervention, high-intelligence" model has shifted the demand for operators from "skilled workers" to "technical technicians." There’s no longer a need to rely on a "sense of touch" to control precision, but workers must be able to read data reports, troubleshoot simple program errors, and maintain intelligent equipment.
Facing such changes, how should frontline workers adapt? For senior workers, there’s no need to fear the "technical threshold." Many factories offer targeted training, simplifying complex code operations into "one-click start" templates. Now, Lao Zhou only needs to select the corresponding part model on the screen, and the machine runs automatically. His main tasks are to regularly check part quality and clean chips. For young workers, it’s important to learn the "underlying logic." They need to not only master operation but also understand the principles behind parameters—for example, why feed rate should be reduced when processing stainless steel. This way, they can quickly locate problems when faults occur.
In fact, the generational transformation of machine tool operation is not about "replacing people" but "liberating people." In the past, workers had to work around the machine; now, the machine helps workers get the job done, leaving people with more time to improve their skills and optimize processes. Lao Zhou and Xiao Lin often work together now: Lao Zhou, with his years of experience, can quickly identify the difficulties in part processing; Xiao Lin is good at using the intelligent system to set better processing parameters. Working together, their production efficiency is 20% higher than when they operate alone.
In the future, with the integration of AI and big data technology, machine tool operation will become even more intelligent. But no matter how technology changes, "people" will always be the core. Machines can handle repetitive work, but they can never replace workers’ experiential judgment and problem-solving abilities. For frontline workers, embracing change proactively and learning skills alongside technological advancements will help them find their place in this transformation—turning machine tools into "assistants" for improving efficiency, rather than an insurmountable "barrier."