The tribological properties of zinc borate ultrafine powder as a lubricant additive in sunflower oil

This paper presents an investigation on the tribological properties of zinc borate ultrafine powder employed as a lubricant additive in sunflower oil. The stable dispersions of 0.5 wt%, 1 wt% and 2 wt% zinc borate ultrafine powder in sunflower oil were achieved by using an ultrasonic homogeniser. Both a 4-ball tester and a pin-on-disc tester were employed to evaluate the anti-wear and friction reduction capabilities of zinc borate ultrafine powder. Tribo-films with dark colour were generated on the worn surfaces and showed a good contrast with the substrate. The worn surface with different morphologies reflected as the colour alterations on the worn surface were observed when different lubricants were applied. The morphology and elemental analysis of the worn surfaces were studied using atomic force microscopy (AFM) and scanning electronic microscopy (SEM). Mechanical properties of the tribo-films and substrates were studied with a nano-indentation tester. Test results suggest that tribo-films generated on the worn surface have a relatively low hardness compared with the steel substrate. The substrates on the worn surfaces lubricated in sunflower oil with the powder demonstrated higher hardness than that of the substrate lubricated with pure sunflower oil due to the possible tribo-chemical reaction between the zinc borate additive and substrate. The combination of sunflower oil with 0.5% zinc borate ultrafine powder has delivered the most balanced performance in friction and wear reduction. This study has demonstrated the possibility of application of this industrially applicable solid lubricant additive (zinc borate) with a decomposable vegetable based lubricant oil.Peer reviewedFinal Accepted Versio

WRGAN : Improvement of RelGAN with Wasserstein Loss for Text Generation

Generative adversarial networks (GANs) were first proposed in 2014, and have been widely used in computer vision, such as for image generation and other tasks. However, the GANs used for text generation have made slow progress. One of the reasons is that the discriminator’s guidance for the generator is too weak, which means that the generator can only get a “true or false” probability in return. Compared with the current loss function, the Wasserstein distance can provide more information to the generator, but RelGAN does not work well with Wasserstein distance in experiments. In this paper, we propose an improved neural network based on RelGAN and Wasserstein loss named WRGAN. Differently from RelGAN, we modified the discriminator network structure with 1D convolution of multiple different kernel sizes. Correspondingly, we also changed the loss function of the network with a gradient penalty Wasserstein loss. Our experiments on multiple public datasets show that WRGAN outperforms most of the existing state-of-the-art methods, and the Bilingual Evaluation Understudy(BLEU) scores are improved with our novel method