3D printing (additive manufacturing) is one of the revolutionary technologies that are transforming manufacturing and industrial processes. Additive manufacturing (AM) technology is being used for extensive customization and production of all types of open-source designs in farming, medical, automotive, locomotive, aerospace, and construction industries. The advantages of 3D printing for industrial applications include little material waste, simple production, minimal human participation, minimal postprocessing, and energy efficiency. There are limited articles on the scope and future possibilities of AM technologies. This article explains numerous AM techniques, uses for technology, and substances used in the manufacturing firm. The numerous materials that may be used with every type of 3D printing process are explained in depth. The numerous settings whereby every processor kind is used are also listed in the study. The original study findings indicate that, while 3D printing technology has made great advances, there are still challenges that need to be addressed; however, the obstacles appear to vary: the cost of preprocessing and postprocessing, a restricted selection of materials, and technological constraints are the most significant 3D printing challenges. Readers will be benefitted from the new dimension added by explanation of the many aspects of additive manufacturing and the identification of potential new research fields in this review. The processes may be enhanced and modified to operate with a range of materials through further research, whereas if the range of applications for 3D printing technology components is to be increased, more effort should be made into developing economical printing procedures and supplies that function with these printers.

Biodiesel was made in this experiment by transesterifying corn oil. The engine's performance and emissions from fossil fuels were evaluated by adjusting the fuel mixture proportions. This investigation examined various parameters, including hydrocarbon emissions, CO, NOx emissions, brake power, mechanical efficiency, brake thermal efficiency, and indicated thermal efficiency. It had a 5.2-kilowatt diesel engine. Making biodiesel is possible by disposing of corn oil. Biodiesel is a product of blending 20% corn oil with diesel. The methyl ester of corn oil satisfied the criteria set by ASTM for fuel. Through the application of different loads to a single-cylinder four-stroke diesel engine, the experimental findings were derived. Although indicated thermal efficiency declined, the B20 blend with 100 ppm titanium oxide nanoparticles led to improvements in brake power, mean effective pressure, mechanical efficiency, and brake thermal efficiency. While the addition of TiO2 nanoparticles to B20 decreased HC and CO emissions, it increased NOx emissions. The BD20+100ppm TiO2 sample provided the most favourable outcomes, all things considered.

Additive Manufacturing (AM) enables unprecedented design flexibility and decentralized production; however, its high energy consumption remains a critical sustainability challenge. While recent digital twin and machine learning (ML)-based studies have focused primarily on quality monitoring, defect detection, or thermal modeling, limited research has developed integrated predictive–optimization pipelines specifically targeting real-time energy management across multiple AM platforms. This study proposes a hybrid machine learning framework that combines predictive modeling, clustering-based data compression, and swarm intelligence optimization to estimate and minimize energy consumption using real-time process parameters. Experimental data were collected from Selective Laser Sintering (SLS) and Fused Deposition Modeling (FDM) systems, incorporating layer-wise geometric descriptors, process variables (laser power, scan speed, build temperature, extrusion rate), and environmental conditions. Multiple supervised learning models—including Support Vector Regression (SVR), XGBoost, Deep Neural Networks (DNN), and a DBSCAN–XGBoost hybrid—were trained and evaluated under a controlled benchmarking protocol. The DNN with feature selection achieved the best predictive performance (RMSE = 0.72 kWh, R² = 0.96), outperforming conventional regression and ensemble models across identical datasets. Statistical comparisons confirmed a consistent improvement in prediction accuracy relative to baseline linear and non-deep learning models. The predicted energy profiles were integrated into a Particle Swarm Optimization (PSO) module to identify energy-efficient process parameter configurations under manufacturability constraints. The optimization stage achieved up to an 18% reduction in energy consumption for both SLS and FDM builds while maintaining tensile strength and dimensional accuracy within acceptable tolerances. By explicitly integrating clustering-based compression, deep learning prediction, and swarm-based optimization into a unified pipeline, this framework extends beyond existing standalone digital twin or energy estimation approaches. The results demonstrate the potential of hybrid ML–optimization architectures for scalable, real-time, energy-aware additive manufacturing and sustainable industrial deployment.