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<p>Metal injection molding (MIM) offers a cost-effective route for fabricating orthodontic brackets, yet quality control is often hindered by defects such as shrinkage, warpage, and incomplete filling arising from complex process–parameter interactions. While previous studies have optimized numerical parameters, the role of categorical factors like injection location remains underexplored. This study integrates Moldflow simulation with Response Surface Methodology (RSM) to optimize critical MIM parameters such as mold temperature, melt temperature, flow rate, and injection location for single orthodontic brackets. A Central Composite Design (CCD) was employed to establish predictive models for three responses: time to ejection temperature, volumetric shrinkage, and cavity fill. The optimized condition of 25&#xa0;°C mold temperature, 137.89&#xa0;°C melt temperature, 127.56&#xa0;cm³/s flow rate, and bottom injection, minimized shrinkage and filling defects. Validation showed prediction–simulation differences below 3%, within acceptable engineering tolerance. This work bridges a critical gap by demonstrating how injection location governs rheological behavior, cooling efficiency, and shrinkage distribution factors often overlooked in conventional MIM optimization. Findings provide a robust computational framework for defect minimization and dimensional control in orthodontic MIM components, offering manufacturers a practical tool to reduce reliance on costly trial-and-error and support more reliable, efficient bracket production.</p>

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Multi-factor optimization on metal injection molding in orthodontic single brackets manufacturing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月25日

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  • Mohd Uzair Rosli,
  • Chu Yee Khor

摘要

Metal injection molding (MIM) offers a cost-effective route for fabricating orthodontic brackets, yet quality control is often hindered by defects such as shrinkage, warpage, and incomplete filling arising from complex process–parameter interactions. While previous studies have optimized numerical parameters, the role of categorical factors like injection location remains underexplored. This study integrates Moldflow simulation with Response Surface Methodology (RSM) to optimize critical MIM parameters such as mold temperature, melt temperature, flow rate, and injection location for single orthodontic brackets. A Central Composite Design (CCD) was employed to establish predictive models for three responses: time to ejection temperature, volumetric shrinkage, and cavity fill. The optimized condition of 25 °C mold temperature, 137.89 °C melt temperature, 127.56 cm³/s flow rate, and bottom injection, minimized shrinkage and filling defects. Validation showed prediction–simulation differences below 3%, within acceptable engineering tolerance. This work bridges a critical gap by demonstrating how injection location governs rheological behavior, cooling efficiency, and shrinkage distribution factors often overlooked in conventional MIM optimization. Findings provide a robust computational framework for defect minimization and dimensional control in orthodontic MIM components, offering manufacturers a practical tool to reduce reliance on costly trial-and-error and support more reliable, efficient bracket production.