Impact of Bonding Temperature on Microstructure, Mechanical, and Fracture Behaviors of TLP Bonded Joints of Al2219 with a Cu Interlayer.

The present study aims at producing transient liquid phase (TLP) bonded Al2219 joints with pure Cu (copper) as an interlayer. The TLP bonding is carried out at the bonding temperatures in the range of 480 to 520 °C while keeping the bonding pressure (2 MPa) and time (30 min.) constant. Reaction layers are formed at the Al-Cu interface with a significant increase in diffusion depth with the increase in the bonding temperature. The microstructural investigations are carried out using scanning electron microscopy and energy-dispersive spectroscopy. X-ray diffraction study confirms the formation of CuAl2, CuAl, and Cu9Al4 intermetallic compounds across the interface of the bonded specimens. An increase in microhardness is observed across the bonding zone with the increase in the bonding temperature, and a maximum hardness value of 723 Hv is obtained on the diffusion zone of the specimen bonded at 520 °C. Furthermore, the fractography study of the bonded specimens is carried out, and a maximum shear strength of 18.75 MPa is observed on the joints produced at 520 °C.

Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images.

Rice (Oryza sativa) is India's major crop. India has the most land dedicated to rice agriculture, which includes both brown and white rice. Rice cultivation creates jobs and contributes significantly to the stability of the gross domestic product (GDP). Recognizing infection or disease using plant images is a hot study topic in agriculture and the modern computer era. This study paper provides an overview of numerous methodologies and analyses key characteristics of various classifiers and strategies used to detect rice illnesses. Papers from the last decade are thoroughly examined, covering studies on several rice plant diseases, and a survey based on essential aspects is presented. The survey aims to differentiate between approaches based on the classifier utilized. The survey provides information on the many strategies used to identify rice plant disease. Furthermore, model for detecting rice disease using enhanced convolutional neural network (CNN) is proposed. Deep neural networks have had a lot of success with picture categorization challenges. We show how deep neural networks may be utilized for plant disease recognition in the context of image classification in this research. Finally, this paper compares the existing approaches based on their accuracy.