Heavy machinery operators can easily burn out. Finnish University network is creating simulation-based technologies that turn operators into remote workers, increasing efficiency and extending careers.
Heavy machinery operators face sensory overload akin to that of fighter pilots. Decisions must be made quickly, the machine roars and vibrates, visibility is poor, and mistakes can lead to disasters. Consequently, careers for forest harvester operators often last less than a decade, says Heikki Handroos, a professor of mechanical engineering at LUT University.
Handroos predicts that the smell of diesel will give way to the hum of fans and coffee aroma as mobile machinery is being developed to be remotely operated and partially automated. He explains that the best expertise in the field of simulation in Europe is found in Finland and Sweden, particularly at Lappeenranta University of Technology.
Handroos states that remote operation of machinery significantly reduces the physical and mental strain of work. Recent studies also indicate that automating machinery functions lowers operators' heart rates and stress levels.
Remotely operated devices also benefit productivity, primarily because operators can control multiple machines at the same job site. This prevents time being wasted by each machine's driver waiting in the cab for their turn.
Simulated Seat Feel
Remotely operated machinery is already available on the market, primarily functioning in environments where they can navigate easily, such as docks. However, operating these machines in rocky or forested areas remains challenging.
Handroos's doctoral candidate, Victor Zhidchenko, shares a video via Teams showcasing a construction machine simulator developed at LUT. The simulator is operated by a university instructor. As the landscape changes behind the machine's so-called windshield, the seat begins to shake and vibrate.
Handroos explains that a heavy machinery operator cannot rely solely on sight. An operator of an earth-moving machine must feel with their seat what type of surface is beneath the wheels. For a crane operator, it's crucial to sense the swaying motion caused by a heavy load on the machine.
Along with the tactile seat, automation aids the remote operator. It works on a principle similar to that of an anti-lock braking system (ABS) in a car. If the operator is about to make a mistake or lose control of the machine, the equipment intervenes automatically.
Tech Passes the Torch
As machinery becomes increasingly complex, operators need a broader understanding of how they function. This shift indicates that the socioeconomic background of the profession will change, at least to some extent.
This recently happened at an Indonesian container terminal, where a doctoral student of Handroos was present. Remote-controlled cranes were sold from Finland to the terminal. Instead of hiring seasoned heavy machinery operators, they employed young, highly educated women to operate them.
The phenomenon is due to differences in the operability of the equipment, Handroos explains. It may be more practical to train remote operators from scratch since they don't have ingrained muscle memory of incorrect movements, especially if they already have a background in technology.
The growing complexity of technology also increases the need for educators. This is supported by the Academic Fellows initiative arising from the PoE network, which has significantly multiplied the number of doctoral training positions advancing the field of mobile machinery.
This text is part of a series on the Mobile Machines Platform of Excellence (PoE) network, exploring six themes. The other five themes are autonomy and robotics, the new value of data, machine collaboration, intelligent control systems and sustainable energy solutions.
The themes are based on a roadmap developed by the SIX Mobile Work Machines cluster. The cluster is coordinated by Tamlink and includes Ponsse, Epec, Sandvik, Valmet Automotive, Valtra, Kalmar, Normet, Tana, Nokia, Danfoss, Junttan, Hevtec, Cargotec, VTT, and Tampere University.
The texts are part of an EAKR-funded project called the Twin transition of mobile work machines (SIX-PoE).
Heikki Handroos has served as a professor and head of the Intelligent Machines Laboratory at Lappeenranta University since the 1990s. Before that, he worked as a research and teaching assistant at the Academy of Finland.
Comments