In the automated handling of automotive parts tires, reducing manual intervention is key to improving production efficiency and ensuring product quality. This is achieved through intelligent equipment upgrades, process optimization, and information system integration. Currently, automated tire handling covers multiple stages of tire production, from raw materials to finished products. The key to reducing manual intervention lies in empowering equipment with autonomous decision-making capabilities and enabling full-process monitoring through digital means.
Raw material handling is the primary step in reducing manual intervention. In traditional tire production, weighing and conveying materials like rubber and carbon black rely on manual labor, which can easily lead to quality fluctuations due to weighing errors or batch mix-ups. Modern automated tire handling systems utilize electronic scales, roller conveyors, and sealing valve control technology to achieve precise raw material proportioning and fully automated handling. For example, automated weighing systems can strictly control the ratios of ingredients like rubber and vulcanizers according to recipe requirements, keeping errors within extremely narrow limits. Automated guided vehicles or stacking systems replace manual handling, and combined with barcode/RFID technology, batch tracking ensures complete traceability of raw materials from warehousing to mixing. This process not only reduces labor intensity but also eliminates quality risks caused by manual operation at the source.
The mixing process is a core step in tire production, and its level of automation directly impacts product quality. Traditional mixing relies on manual monitoring of parameters such as temperature and pressure, which can easily lead to unstable rubber compound properties due to operator errors. Modern mixing equipment utilizes multi-stage temperature control systems, closed-loop pressure-temperature curve control, and offline data acquisition technology to achieve unified management of mixing time, rotation speed, and shear ratio. For example, the system can analyze the impact of raw material fluctuations on the rubber compound in real time and automatically adjust formulation parameters. The centralized monitoring platform can compare mixing efficiency, scrap rate, and other indicators across teams, providing a basis for process optimization. This "equipment autonomous adjustment + data-driven optimization" model significantly reduces the need for manual intervention.
Carcass structure assembly and tread processes are another key area for reducing manual intervention. In traditional production, processes such as auxiliary material placement and belt positioning rely on worker experience, which can easily lead to inaccurate bonding and dimensional deviations. Hyundai's tire automated handling utilizes robots and high-precision vision systems to perform these operations.
Robotic arms identify material edges and angles, ensuring the tightness of each layer structure. Flexible grippers and adjustable positioning fixtures enable rapid switching between tires of different models, reducing line changeover time. In the tread process, an automated glue coating system ensures that the rubber layer thickness remains within tolerance through uniform coating, throttling control, and ambient temperature and humidity monitoring. The calendering equipment utilizes force-controlled rollers, temperature-controlled molds, and an online thickness measurement system to precisely shape and degas the tread sheet. These technologies contribute to a "fewer mold changes, more adjustments" production strategy, reducing the risk of downtime.
Automated upgrades to the tire forming and vulcanization processes further reduce manual operations. During the forming process, the mold system utilizes automatic opening and closing and servo drive technology, combined with closed-loop sensing and torque detection, to verify the force applied at each forming stage, preventing tire asymmetry or internal defects caused by localized pressure variations. The vulcanization process achieves precise control of the vulcanization curve through multi-zone temperature control, pressure gradient control, and closed-loop mold temperature monitoring. The system dynamically sets the vulcanization time and pressure profile based on tire model and structural complexity, and monitors mold status through a sensor network to prevent thermal deformation from affecting finished product quality. This process eliminates the need for manual parameter adjustment, ensuring a stable vulcanization process.
Automation of finished product inspection and post-processing is the final line of defense to reduce manual intervention. Traditional inspection relies on manual visual inspection and simple tools, which can easily miss surface defects or internal cracks. Modern tire automated handling uses technologies such as visual inspection, laser profiling, eddy current non-destructive testing, and X-ray internal defect detection to combine and analyze tire dimensions, tread pattern, hardness, roundness, and other indicators to create a comprehensive quality profile. For example, vision systems can quickly identify surface defects and coating uniformity; laser measurement provides geometric information such as outer diameter and roundness; and eddy current and X-rays detect internal cracks and interlayer delamination. Once inspection results are linked to work orders and batch numbers, the system can automatically trigger repairs, material replacements, or production line suspensions, minimizing the risk of recalls.
Information technology is the foundational guarantee for reducing manual intervention. Modern tire factories, with a manufacturing execution system at their core, connect enterprise resource planning systems, machine-side PLC controls, sensor networks, and quality inspection systems to create a comprehensive data loop. Through digital dashboards, operators receive clear operational instructions and quality indicators, while management can access macro data such as capacity utilization and work-in-process turnover. This collaborative "equipment-system-people" model ensures continuous optimization of tire automated handling.
