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<p>Laser Beam Machining (LBM) is a non-traditional machining process known for its accuracy, efficiency, and its ability to machine complex geometries over wide array of materials. CO₂ laser machining is widely used due to its non-contact nature, high material removal rate, minimal thermal damage, and adaptability to automation. Polymethylmethacrylate (PMMA), a thermoplastic polymer, has strong absorptivity at the CO₂ laser wavelength is broadly used in applications where high-precision machining is the key. Experiments were conducted using a 130&#xa0;W CO₂ laser cutting system to investigate the influence of laser power, scanning speed, and number of passes on two key outcomes: depth of cut and surface roughness. Regression modeling and Response Surface Methodology (RSM) were used to study the effects of individual parameters and their interactions. Additionally, a Desirability function analysis was adopted as a multi-objective optimization technique to identify the optimal set of input parameters that simultaneously maximize the depth of cut and minimize roughness. The developed models exhibited high prediction accuracy with strong correlation coefficients. Results indicate that increased laser power and reduced scanning speed significantly enhance the depth of cut, while excessive energy input can adversely affect surface quality. The study provides a robust framework for optimizing CO₂ laser machining of PMMA, facilitating improved process efficiency and surface integrity in industrial applications.</p>

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Optimization of CO₂ laser machining parameters for PMMA using RSM and desirability analysis

  • 期刊论文

  • 发表日期：

    2025年10月13日

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  • Shivram R. Poojari,
  • Pirsab R. Attar,
  • Arif M. Varsi

摘要

Laser Beam Machining (LBM) is a non-traditional machining process known for its accuracy, efficiency, and its ability to machine complex geometries over wide array of materials. CO₂ laser machining is widely used due to its non-contact nature, high material removal rate, minimal thermal damage, and adaptability to automation. Polymethylmethacrylate (PMMA), a thermoplastic polymer, has strong absorptivity at the CO₂ laser wavelength is broadly used in applications where high-precision machining is the key. Experiments were conducted using a 130 W CO₂ laser cutting system to investigate the influence of laser power, scanning speed, and number of passes on two key outcomes: depth of cut and surface roughness. Regression modeling and Response Surface Methodology (RSM) were used to study the effects of individual parameters and their interactions. Additionally, a Desirability function analysis was adopted as a multi-objective optimization technique to identify the optimal set of input parameters that simultaneously maximize the depth of cut and minimize roughness. The developed models exhibited high prediction accuracy with strong correlation coefficients. Results indicate that increased laser power and reduced scanning speed significantly enhance the depth of cut, while excessive energy input can adversely affect surface quality. The study provides a robust framework for optimizing CO₂ laser machining of PMMA, facilitating improved process efficiency and surface integrity in industrial applications.