OLPT CONDUCTIVITY IN WOLLASTONITE INLAID NR/SBR TYPE ELASTOMER BASED MATERIAL

The electrical properties of wollastonite inlaid NR/SBR type elastomer based material have been evaluated. Electrical properties of the samples were measured in the temperature range of 303 to 453 K and the frequency range of 100 Hz – 40 MHz. All electrically measured parameters were given anomalies at 385 K. Only one type of dielectric relaxation process have been observed for all measurements. Physical parameters characterizing the dielectric behavior have been obtained by fitting the experimental results in the modified Debye equation. The activation energy which is thermally activated by dielectric relaxation process have been calculated to be 0.58 eV. DC conductivity increasing by temperature has been explained with the help of VFT model whereas the AC one has been clarified by the OLPT model

Optimization of 4D/3D printing via machine learning: a systematic review

This systematic review explores the integration of 4D/3D printing technologies with machine learning, shaping a new era of manufacturing innovation. The analysis covers a wide range of research papers, articles, and patents, presenting a multidimensional perspective on the advancements in additive manufacturing. The review underscores machine learning's pivotal role in optimizing 4D/3D printing, addressing aspects like design customization, material selection, process control, and quality assurance. The examination reveals novel techniques enabling the fabrication of intelligent, self-adaptive structures capable of transformation over time. Additionally, the review investigates the use of predictive algorithms to enhance efficiency, reliability, and sustainability in 4D/3D printing processes. Applications span aerospace, healthcare, architecture, and consumer goods, showcasing the potential to create intricate, personalized, and once-unattainable functional products. The synergy between machine learning and 4D/3D printing is poised to unlock new manufacturing horizons, enabling rapid responses to market demands and sustainability challenges. In summary, this review provides a comprehensive overview of the current state of 4D/3D printing optimization through machine learning, highlighting the transformative potential of this interdisciplinary fusion and offering a roadmap for future research and development. It aims to inspire innovators, researchers, and industries to harness this powerful combination for accelerated evolution in manufacturing processes into the 21st century and beyond