Additive manufacturing, commonly known as 3D printing, represents the forefront of modern manufacturing technology. Its growing popularity spans across research and development, material science, design, processes, and everyday applications. This review paper presents a crucial review of nano-level 3D printing, examining it from the perspectives of processes, materials, industrial applications, and future trends. The authors have synthesized the latest insights from a wide range of archival articles and source books, highlighting the key findings. The primary contribution of this study is a condensed review report that consolidates the newest research on nano-level 3D printing, offering a broad overview of this innovative technology for researchers, inventors, educators, and technologists. It is anticipated that this review study will significantly advance research in nanotechnology, additive manufacturing, and related technological fields.

Additive manufacturing (AM), also known as three-dimensional (3D) printing, is the process of building a solid object in a layer-wise manner. Cybersecurity is a prevalent issue that appears more and more frequently as AM becomes popular. This paper focuses on the effect of fan speed on the printing quality and presents a plugin called Fan Speed Attack Detection (FSAD) that predicts and monitors fan speeds throughout the printing process. The goal of the plugin is to prevent cybersecurity attacks, specifically targeting fan speed. Using the proposed FSAD, any fan speed changes during the printing process are evaluated to see whether the printer can sustain the abnormal fan speed change and still maintain good print quality.

Additive Manufacturing (AM), also know as 3D Printing (3DP), is a widely used layer-by-layer manufacturing process, it is evolving rapidly both in research and industry. Among all AM methods, Material Extrusion (ME) is one of the most popular techniques. Based on ME, another new AM method is developed, which is Low-cost Metal Material Extrusion (LCMME). In this newly developed process, pure metallic parts could be fabricated after sintering the metal infused additively manufactured parts. Both AM and sintering process parameters will have influence on the quality of the final pure metallic parts. In this research, several statistics methods were used to analyze the data gotten from the experiment. Then two Machine Learning (ML) algorithms were used to predict the Surface Roughness (SR) of the final specimens. Additionally, the results show that the neural network (NN) is more accurate than the support vector regression (SVR) on prediction.

This study presents a robust approach for identifying irrigation status in wheat fields using specific optical characteristics, validated through multiple deep learning models. Experimental results demonstrate the superior performance of the proposed method in detecting irrigated fields, with all models achieving consistently high accuracy. The optimized lightweight model maintains reliable performance while significantly reducing computational requirements, showing strong potential for practical agricultural monitoring applications. Comprehensive evaluation confirms the method's robustness, with stable detection capability and interpretable error patterns. The findings provide an effective solution for precision agriculture, with future research suggested to explore multi-spectral integration for enhanced performance.