Two important factors affecting the performance of sand mold/core generated by 3D printing(3DP)are strength and dimensional accuracy,which are not only closely related to the reactivity of furan resin and the phase tr...Two important factors affecting the performance of sand mold/core generated by 3D printing(3DP)are strength and dimensional accuracy,which are not only closely related to the reactivity of furan resin and the phase transition of silica sand,but also the curing agent system of furan resin.This paper studies the influence of gel time on the strength and dimensional accuracy of a 3DP sand mold/core,taking the furan resin system as an example and using a sand specimen generated by a 3DP inkjet molding machine.The experiment demonstrates that the gel time of 3 to 6 min for the sand mixture suits 3DP core-making most under the experimental condition.However,it should be noted that under the same resin condition,the strength of a no-bake sand mold/core is higher than that of a 3DP sand mold/core.The dimensional accuracy of the sand mold/core does not change significantly when the gel time is less than 15 min.Improving the activity of binder and developing ultra-strong acid with low corrosion shall be an effective way to improve the quality of the mold/core by 3D printing.展开更多
The 3D sand printing(3DSP),by binder jetting technology for rapid casting,has a pivotal role in promoting the development of the traditional casting industry as a result of producing high-quality and economical sand m...The 3D sand printing(3DSP),by binder jetting technology for rapid casting,has a pivotal role in promoting the development of the traditional casting industry as a result of producing high-quality and economical sand molds.This work presents an approach for monitoring and analyzing powder sand-bed images to serve as a real-time control system in a 3DSP machine.A deep residual network(ResNet)is used to classify the defects occurring during the powder spreading stage of the process.Firstly,a pre-trained network was applied as the initial parameter;then it was fine-tuned on the labelled defective sample dataset to accomplish the task,which defines the sand-bed defects induced in the 3DSP processing.Furthermore,the recognition and positioning of sand-bed defects were readily achieved by dividing the sand-bed images into blocks.Experiments show that the fine-tuned network has a 98.7%classification accuracy on the validation dataset of sand-bed defects and 95.4%recognition accuracy for the sand-bed images.展开更多
基金financially supported by the Liaoning Science and Technology Plan Program(2019-ZD-0998)the National Natural Science Foundation of China(Grant No.U1808216)。
文摘Two important factors affecting the performance of sand mold/core generated by 3D printing(3DP)are strength and dimensional accuracy,which are not only closely related to the reactivity of furan resin and the phase transition of silica sand,but also the curing agent system of furan resin.This paper studies the influence of gel time on the strength and dimensional accuracy of a 3DP sand mold/core,taking the furan resin system as an example and using a sand specimen generated by a 3DP inkjet molding machine.The experiment demonstrates that the gel time of 3 to 6 min for the sand mixture suits 3DP core-making most under the experimental condition.However,it should be noted that under the same resin condition,the strength of a no-bake sand mold/core is higher than that of a 3DP sand mold/core.The dimensional accuracy of the sand mold/core does not change significantly when the gel time is less than 15 min.Improving the activity of binder and developing ultra-strong acid with low corrosion shall be an effective way to improve the quality of the mold/core by 3D printing.
文摘The 3D sand printing(3DSP),by binder jetting technology for rapid casting,has a pivotal role in promoting the development of the traditional casting industry as a result of producing high-quality and economical sand molds.This work presents an approach for monitoring and analyzing powder sand-bed images to serve as a real-time control system in a 3DSP machine.A deep residual network(ResNet)is used to classify the defects occurring during the powder spreading stage of the process.Firstly,a pre-trained network was applied as the initial parameter;then it was fine-tuned on the labelled defective sample dataset to accomplish the task,which defines the sand-bed defects induced in the 3DSP processing.Furthermore,the recognition and positioning of sand-bed defects were readily achieved by dividing the sand-bed images into blocks.Experiments show that the fine-tuned network has a 98.7%classification accuracy on the validation dataset of sand-bed defects and 95.4%recognition accuracy for the sand-bed images.
