... cloud compensation and control data fusion additive manufacturing control and automation inspection and quality control production systems optimization sensing monitoring and diagnostics In modern manufacturing processes, the control and compensation of 3D shape deviations have become...

... through complementary data fusion with process features [ 21 ]. This promising approach addresses the discretization issue of 2D image stacks and holds potential for application in our functional qualification work. Additionally, in the literature, treating 3D point cloud data as a stack of 2D images...

... and porosity in LBAM and provide new possibilities for efficient and accurate porosity prediction. We present a deep learning-based data fusion method to predict porosity in LBAM parts by leveraging the measured melt pool thermal history and two newly created deep learning neural networks. A PyroNet, based...

.... Manuscript received January 15, 2018; final manuscript received September 6, 2018; published online October 10, 2018. Assoc. Editor: Laine Mears. 15 01 2018 06 09 2018 surface variation modeling data fusion nonstationary process transfer learning varying coefficient models adaptive...

... machining processes in an engine plant. multitask learning Gaussian process data fusion surface variation surface modeling The control of surface shape variation on a machined surface is of great importance in automotive powertrain manufacturing because such variation has a significant...