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首页> 外文期刊>International Journal of Industrial Engineering Computations >Multi-regression prediction model for surface roughness and tool wear in turning novel aluminum alloy (LM6)/fly ash composite using response surface and central composite design methodology
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Multi-regression prediction model for surface roughness and tool wear in turning novel aluminum alloy (LM6)/fly ash composite using response surface and central composite design methodology

机译：基于响应面和中心复合设计方法的新型铝合金（LM6）/粉煤灰复合材料车削表面粗糙度和刀具磨损的多元回归预测模型

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Turning experiments were conducted on a novel aluminum alloy (LM6)/fly ash composite based on the response surface and face centered central composite design methodology. The effects of cutting parameters on surface roughness and tool wear were investigated. Multiple regression models were developed for the responses and the adequacies of the developed models were tested at 95% confidence interval using the analysis of variance (ANOVA) technique. Carbide inserts (Model: CNMG 120408-M5) were used for turning the specimens in a CNC turning machine (model: LT-16). The test for significance of the regression models, the test for significance on individual model coefficients and the lack-of-fit tests were performed using the statistical Design-Expert7.0v software environments. R2 indicated the model significance and the value was more than 97%, revealed that the relation between cutting responses and input parameters held good for more than 97% and the model was adequate.
机译：基于响应表面和面心中心复合材料设计方法，对新型铝合金（LM6）/粉煤灰复合材料进行了车削实验。研究了切削参数对表面粗糙度和刀具磨损的影响。针对响应建立了多个回归模型，并使用方差分析（ANOVA）技术在95％置信区间测试了开发模型的充分性。硬质合金刀片（型号：CNMG 120408-​​M5）用于在CNC车床（型号：LT-16）中车削样品。回归模型的显着性检验，各个模型系数的显着性检验和不适合检验均使用统计Design-Expert7.0v软件环境进行。 R2表示模型的意义，该值大于97％，表明切削响应与输入参数之间的关系保持97％以上的良好关系，并且模型足够。

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