摘要
由一名新闻记者兼机器人与机器学习每日新闻的新闻编辑-一项关于人工智能的新研究现在可用。根据来自印度旁遮普邦的新闻报道，By NewsRx通讯员Research称，“本研究调查了通过熔融d位置模型生产的金属/聚合物复合材料力学性能的增强，以及通过使用分类和回归树(CART)的机器学习预测最终拉伸强度(UTS)。”这项研究的资助者包括科学研究院长，Khalid U Niversity国王。新闻记者从昌迪加尔大学的研究中获得了一句话：“由80%的丙烯腈-丁二烯-苯乙烯基体和10%的铝(Al)和铜(Cu)填料组成的复合材料，我们用田口方法对印刷参数进行了全面的探索，包括印刷温度、填充图案和填充密度。该小车展示了一个具有四个终端节点的层次树结构，该模型具有很高的预测能力，训练数据的R-S拟合值为0.9154，测试数据的R-S拟合值为0.8922，证明了其捕捉变异性的有效性。最大化UTS的参数组合为锯齿状填充模式，预测效果最好。打印温度为245°C，填充密度为10%，与680 N的最高UTS相关。通过配对的T-TES T和两个方差的检验和置信区间证实了模型的可靠性，表明观察到的UTS值和预测值之间没有显著差异。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news reporting originating from Punjab, India, b y NewsRx correspondents, research stated, “This study investigates the enhanceme nt of mechanical properties of metal/polymer composites produced through fused d eposition modeling and the prediction of the ultimate tensile strength (UTS) by machine learning using a Classification and Regression Tree (CART).” Funders for this research include Deanship of Scientific Research, King Khalid U niversity. The news journalists obtained a quote from the research from Chandigarh Universi ty: “The composites, comprising 80% acrylonitrile butadiene styren e matrix and 10% each of aluminum (Al) and copper (Cu) fillers, we re subjected to a comprehensive exploration of printing parameters, including pr inting temperature, infill pattern, and infill density using the Taguchi method. The CART unveiled a hierarchical tree structure with four terminal nodes, each representing distinct subgroups of materials characterized by similar UTS proper ties. The predictors’ importance was assessed, highlighting their role in determ ining material strength. The model exhibited a high predictive power with an R-s quared value of 0.9154 on the training data and 0.8922 on the test data, demonst rating its efficacy in capturing variability. The optimal combination of paramet ers for maximizing UTS was a zigzag infill pattern, a printing temperature of 24 5 °C, and an infill density of 10%, which is associated with the hi ghest UTS of 680 N. The model’s reliability was confirmed through a paired t-tes t and test and confidence interval for two variances, revealing no significant d ifference between the observed and predicted UTS values.”
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