Failure detection in production has historically relied on a combination of human inspection, statistical controls, and traditional sensors. However, the increasing complexity of processes, the pressure to reduce scrap, and the need for real-time traceability have highlighted clear limits in these approaches. In this context, a frequently asked question on the shop floor is: How
Spec sheets vs shop-floor reality On paper everything looks precise. In production you face tolerances, scrap risk, manual tweaks, and material variability. The honest question is: “Will the robot actually be more accurate than what we do today?” Accuracy vs repeatability Repeatability The robot’s ability to return to the same point over and over. Typical
This question rarely appears when a robot first arrives on the production floor. It emerges months later. When everything works. When the cell is producing. When nobody questions the arm, the gearbox, or the repeatability anymore. The doubt appears in front of a screen: A pending update. A license about to expire. A file that
In many companies, the decision to automate is not held back by the cost of the robot or by floor space, but by a less visible—yet decisive—concern: technical dependency. The question is not always stated openly, but it quickly emerges in any investment committee: What happens when the supplier leaves? Robotic automation introduces powerful technology,
Palletizing is one of the most critical stages at the end of the production line. Although it is often perceived as a simple process, in practice it involves occupational risks, production bottlenecks, and hidden operational costs. For many years this process has been handled using traditional systems: manual palletizing, semi‑automatic solutions, or low‑flexibility dedicated machines.
There’s an awkward moment in some automation projects when no one really wants to look too closely at the first batches. The parts come out quickly. The robot never stops. Productivity indicators look great. And yet… something feels off. The defect that used to appear sporadically now shows up with impeccable regularity. There’s no debate:
MIG/MAG welding is one of the most widely used manufacturing processes, but when performed manually it often leads to significant variation. Deciding to automate is not just about bringing new technology into the factory – it’s about determining whether your process is technically and operationally ready for a robot to deliver real, tangible improvements. There
In the robotics industry, the Motoman MH24 is a six-axis articulated robot designed for high-speed tasks such as material handling, general operations, and other precision applications. While it is not a welding-specific robot like some models in Yaskawa’s AR series, its combination of speed, rigidity, and path accuracy makes it a viable option for welding
At the heart of many automated factories, a group of robots works tirelessly for hundreds of hours on end. But what happens if one of these machines fails unexpectedly? An unplanned stoppage can cost thousands of euros per hour, result in lost orders and delayed deliveries. This is where predictive maintenance steps in: instead of
Industrial robots, like any machinery, require regular maintenance. But the key question is: do we act before a failure occurs or after it? Predictive maintenance redefines efficiency by anticipating breakdowns and optimising resources. Corrective: The Traditional Model Corrective maintenance takes place after a failure: when a servomotor stops, an axis loses calibration or a controller
