Metadata about: Abstract Manufacturing has evolved to become more automated in pursuit of higher quality, better productivity and lower cost. However, industrial logistics between the end customer and manufacturing supply chain still demands human labor and intelligence. This logistic work requires engineers with experienced knowledge to identify machining processes from the CAD model provided by customers at the beginning, and later sourcing for qualified manufacturing suppliers according to identified manufacturing processes. Developing an efficient automatic Machining Process Identification (MPI) system becomes a pivotal problem for logistic automation. In this paper, a novel MPI system is presented based on 3D Convolutional Neural Networks (CNN) and Transfer learning. The proposed system admits triangularly tessellated surface (STL) models as inputs and outputs machining process labels (e.g. milling, turning etc.) as the results of classification of the neural network. Computer-synthesized workpiece models are utilized in training the neural network. In addition to the MPI system, a portable framework was developed for future applications in related fields. The MPI system shows more than 98% accuracy for both synthesized models and real workpiece models which verifies its robustness and real-time reliability.

About: Abstract Manufacturing has evolved to become more automated in pursuit of higher quality, better productivity and lower cost. However, industrial logistics between the end customer and manufacturing supply chain still demands human labor and intelligence. This logistic work requires engineers with experienced knowledge to identify machining processes from the CAD model provided by customers at the beginning, and later sourcing for qualified manufacturing suppliers according to identified manufacturing processes. Developing an efficient automatic Machining Process Identification (MPI) system becomes a pivotal problem for logistic automation. In this paper, a novel MPI system is presented based on 3D Convolutional Neural Networks (CNN) and Transfer learning. The proposed system admits triangularly tessellated surface (STL) models as inputs and outputs machining process labels (e.g. milling, turning etc.) as the results of classification of the neural network. Computer-synthesized workpiece models are utilized in training the neural network. In addition to the MPI system, a portable framework was developed for future applications in related fields. The MPI system shows more than 98% accuracy for both synthesized models and real workpiece models which verifies its robustness and real-time reliability.

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