Laser cladding of powder mixture of TiN and SS304 is carried out on an SS304 substrate with the help of fibre laser.The experiments are performed on SS304,as per the Taguchi orthogonal array(L^(16))by different combin...Laser cladding of powder mixture of TiN and SS304 is carried out on an SS304 substrate with the help of fibre laser.The experiments are performed on SS304,as per the Taguchi orthogonal array(L^(16))by different combinations of controllable parameters(microhardness and clad thickness).The microhardness and clad thickness are recorded at all the experimental runs and studied using Taguchi S/N ratio and the optimum controllable parametric combination is obtained.However,an artificial neural network(ANN)identifies different sets of optimal combinations from Taguchi method but they both got almost the same clad thickness and hardness values.The micro-hardness of cladded layer is found to be6.22 times(HV_(0.5)752)the SS304 hardness(HV_(0.5)121).The presence of nitride ceramics results in a higher micro hardness.The cladded surface is free from cracks and pores.The average clad thickness is found to be around 0.6 mm.展开更多
Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification...Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification of orthogonal array based model prediction. It shows improvement in modelling of edge quality and kerf width by applying semi-supervised learning algorithm, based on novel error assessment on simulations. The results are expected to depict better prediction on average by utilizing the systematic randomized techniques to initialize the neural network weights and increase the number of initialization. Missing values handling is difficult with statistical tools and supervised learning techniques; on the other hand, semi-supervised learning generates better results with the smallest datasets even with missing values.展开更多
文摘Laser cladding of powder mixture of TiN and SS304 is carried out on an SS304 substrate with the help of fibre laser.The experiments are performed on SS304,as per the Taguchi orthogonal array(L^(16))by different combinations of controllable parameters(microhardness and clad thickness).The microhardness and clad thickness are recorded at all the experimental runs and studied using Taguchi S/N ratio and the optimum controllable parametric combination is obtained.However,an artificial neural network(ANN)identifies different sets of optimal combinations from Taguchi method but they both got almost the same clad thickness and hardness values.The micro-hardness of cladded layer is found to be6.22 times(HV_(0.5)752)the SS304 hardness(HV_(0.5)121).The presence of nitride ceramics results in a higher micro hardness.The cladded surface is free from cracks and pores.The average clad thickness is found to be around 0.6 mm.
文摘Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification of orthogonal array based model prediction. It shows improvement in modelling of edge quality and kerf width by applying semi-supervised learning algorithm, based on novel error assessment on simulations. The results are expected to depict better prediction on average by utilizing the systematic randomized techniques to initialize the neural network weights and increase the number of initialization. Missing values handling is difficult with statistical tools and supervised learning techniques; on the other hand, semi-supervised learning generates better results with the smallest datasets even with missing values.
