首页|Recent Findings in Machine Learning Described by a Researcher from Chandigarh Un iversity (Investigation of melt flow index and tensile properties of dual metal reinforced polymer composites for 3D printing using machine learning approach: . ..)
Recent Findings in Machine Learning Described by a Researcher from Chandigarh Un iversity (Investigation of melt flow index and tensile properties of dual metal reinforced polymer composites for 3D printing using machine learning approach: . ..)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
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.”
NETL
NSTL
