研究目的
To optimize kerf width and kerf deviation simultaneously during the laser cutting of Titanium alloy sheet (Grade 5) using a hybrid approach of Genetic Algorithm and Multiple Regression Analysis.
研究成果
The study successfully developed second-order regression models for kerf width and kerf deviation, which were statistically validated. The hybrid optimization approach using genetic algorithm and multiple regression analysis resulted in significant improvements in kerf width (29.78%) and kerf deviation (95%), with an overall improvement of 27.39%. The findings demonstrate the effectiveness of the proposed methodology in optimizing laser cutting parameters for titanium alloy sheets.
研究不足
The study focuses on the optimization of kerf width and kerf deviation during laser cutting of Titanium alloy sheet (Grade 5) using specific parameters. The applicability of the findings to other materials or different cutting conditions may require further investigation.
1：Experimental Design and Method Selection:
The study used a 300 W Nd:YAG Laser cutting system for cutting titanium alloy sheets. Multiple regression analysis was applied to develop second-order regression models for kerf width and kerf deviation. Genetic algorithm was used for multi-objective optimization of these models.
2：Sample Selection and Data Sources:
Titanium (grade 5) workpiece with dimensions of 140 x 140 x 1.6 mm was used. The range of process control factors was selected based on pilot experiments.
3：6 mm was used. The range of process control factors was selected based on pilot experiments.
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: Advanced 300 W Nd:YAG Laser cutting system, compressed air as an assist gas, stereo optical microscope for measurements.
4：Experimental Procedures and Operational Workflow:
The cut surface was measured at four different points along a 20 mm straight cut on the titanium sheet. Kerf width and kerf deviation were measured using a stereo optical microscope.
5：Data Analysis Methods:
Second-order regression models were developed for kerf width and kerf deviation. The adequacy of the models was checked using ANOVA. Genetic algorithm was applied for optimization.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
