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Automating welding processes with robots enhances productivity and environmental friendliness. However, the quality of robotic welding can deteriorate due to various factory conditions. Continuous monitoring and adjustments are necessary to maintain high standards.

Predictive Maintenance is a sophisticated approach in equipment management that employs machine learning to constantly monitor and evaluate the condition of machinery. This methodology aids manufacturers in predicting potential faults, significantly reducing production costs, optimizing device usage, and enhancing productivity. Key activities in predictive maintenance include continuous monitoring of equipment health, data-driven condition assessment, and using advanced algorithms for predicting potential failures. A digital twin—a digital replica of a physical object, contextualized within its environment—plays a crucial role in this process.A leading spiral welded steel pipe manufacturer in Turkey faced significant production challenges. The factory's production process, reliant on a series of interdependent machines, was highly susceptible to disruptions. Any machine failure would halt the entire production line, leading to unpredictable and prolonged downtimes. Additionally, the lack of sufficient failure data necessitated the generation of synthetic data using high-fidelity hybrid models.

Chatter, or unwanted vibrations during CNC machining, can cause noise, heat, and damage to both the workpiece and cutting tools. It is crucial to detect and mitigate chatter to ensure high-quality production and prolong the lifespan of the equipment.