Method for drill-bit arrangement in CNC woodworking drilling machine for mass customization

Abstract

In response to the growing demand for personalized products and dynamic changes in the global market, the consumer goods manufacturing industry, particularly the furniture sector, is increasingly adopting the mass customization (MC) production model. In this context, computer numerical control (CNC) woodworking drilling machines play a critical role in enabling flexible MC furniture production, especially during the drilling phase, which often becomes a bottleneck due to lengthy operation times and significant variability. Traditional methods aimed at speeding up equipment operation can no longer improve drilling efficiency. Therefore, optimizing parameter configurations by focusing on the practical usage of the equipment and implementing reconfigurable manufacturing systems (RMS) is essential. This study addresses the bottleneck by proposing an innovative approach to drill-bit arrangement based on the positional relationship between holes, considering real-world scenarios of multi-machine parallel processing and the challenges of quickly and accurately evaluating results. A universal "grouping-solving-evaluation" method is introduced, incorporating clustering, intelligent optimization, and neural networks within artificial intelligence. This method organizes datasets, solves problems, and evaluates results through a deep understanding of CNC machine operations, extensive analysis of large-scale production data, and the creation of a precise mathematical model. The effectiveness of this approach is validated using data from a production site. Our method showed the potential to reduce drilling times by up to 22.99 %, increase efficiency by as much as 17.80 %, and achieve typical improvements of 19.16 % in time reduction and 14.67 % in efficiency compared to traditional manual configurations. These findings provide valuable insights for advancing MC furniture manufacturing and promoting the intelligent production of customized furniture. By enabling the shift from traditional to more personalized and automated manufacturing processes, this research makes a significant contribution to overcoming current production limitations.