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The major concern technologies during the processing through three-dimensional printing (3DP) are the mechanical and boundary properties of sand models. The parameters such as activator content, resolution X, layer thickness and re-coater speed play a vital role in 3DP sand components. The purpose of this paper is to recommend the optimal process parameters for the best sand mold properties.

In this paper, taking the parameters of the activator content, resolution X, layer thickness and re-coater speed as the influence factors, an orthogonal test of L16(44) was designed to discuss the influences of those parameters on the mechanical and boundary properties. Three-point bending (3PB) test was used to characterize the actual bending strength, and the boundary accuracy was assessed by the deviation of the three-point bending samples compared with its design scale.

The experimental results showed that the resolution X and layer thickness are the main parameters affecting sand mold properties. The strength will attain its maximum when the resolution X and layer thickness are the minimum. The optimal parameters were screened and verified by the confirmation test. The optimal process parameters for best strength and less gas evolution are the activator of 0.19%, resolution X of 0.1 mm, layer thickness of 0.28 mm and re-coater speed of 210 mm/s.

The novelty of this paper is the select of significant parameters on 3D-printed sand model properties. A mathematical model was built to analyze the effect of these parameters. The optimal process parameters for the best properties were got.

The purpose of this paper is to demonstrate by experiments that non‐linear Lamb wave modulation spectrum (NLWMS) will be a good indicator of fatigue cracks in a metallic plate.

A system composed of piezoelectric transducers, arbitrary waveform generator, power amplifier, laser vibrometer, digital oscilloscope and computer has been constructed, and using this system, three samples made of 7075‐T6 aluminium alloy plates are studied, respectively. One is with a fatigue crack, one is with a through hole and the other is intact.

The experimental results show that there are no significant harmonics and sidebands in the intact sample, and there are no modulation frequencies in the drilled‐hole sample. On the contrary, in the cracked sample, there is an abundance of the harmonics and sidebands. Therefore, these new modulation frequency components can be used to indicate the presence of the fatigue crack.

This paper will provide a method for detecting fatigue crack in metallic plates, especially aluminium alloy plates for aerospace applications.

Under the excitation of two different frequency Lamb waves, experiments on 7075‐T6 alloy plates show that new rising modulation frequency components can be used to indicate the presence of the crack. So, the fatigue crack in metallic plates can be detected by NLWMS.

This study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the automation problem that exists in traditional campus networks and how SDN and DL can provide mitigating solutions. It further highlights some challenges which need to be addressed in order to successfully implement SDN and DL in campus networks to make them better than traditional networks.

The study uses a systematic literature review. Studies on DL relevant to campus networks have been presented for different use cases. Their limitations are given out for further research.

Following the analysis of the selected studies, it showed that the availability of specific training datasets for campus networks, SDN and DL interfacing and integration in production networks are key issues that must be addressed to successfully deploy DL in SDN-enabled campus networks.

This study reports on challenges associated with implementation of SDN and DL models in campus networks. It contributes towards further thinking and architecting of proposed SDN-based DL solutions for campus networks. It highlights that single problem-based solutions are harder to implement and unlikely to be adopted in production networks.