Particulate contamination on end effectors (EE) in hard disk drive (HDD) precision assembly can trigger frequent cleaning, adding non-value-added downtime and increasing post-maintenance alignment variability, particularly at bottleneck operations. Conventional offline ultrasonic cleaning requires tool stoppage, component removal, transport, drying, reassembly, and process requalification, extending effective downtime. This study proposes and numerically evaluates an in-machine Auto Self-Cleaning (ASC) concept to reduce cleaning-induced downtime while maintaining process stability. Lean-based root-cause analysis was applied to prioritize downtime drivers and translate them into functional design requirements. The proposed concept integrates an air-jet purge strategy within a dedicated cleaning chamber combined with vacuum-assisted extraction. Computational Fluid Dynamics (CFD) was used to iteratively refine chamber geometry and airflow behavior to improve purge coverage and reduce particle re-deposition risk. Simulation results indicated a 98.89% particle-collection ratio under representative boundary conditions. By eliminating major offline cleaning steps, downtime per cleaning event was projected to decrease from 85 minutes to 15 minutes, corresponding to an 82.4% reduction. Beyond mechanical and CFD-based optimization, the ASC architecture provides a scalable foundation for smart-manufacturing integration through IoT-enabled monitoring, predictive maintenance strategies, and data-driven optimization aligned with Lean and TPM principles. The proposed framework enhances equipment availability (OEE), operational robustness, and long-term manufacturing resilience in high-precision HDD assembly environments.

Keywords

Hard Disk Drive, Automation, Auto Self-Cleaning, Lean manufacturing, Downtime