Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.

Globally, electricity demand rises by 1.8% per year; according to the American Energy Information Administration, global energy demand will increase by 47% over the next 30 years, driven by demographic and economic growth. Global demand for electricity is growing faster than renewable energy sources. Electricity production from renewable sources (i.e., biomass energy, geothermal energy, hydro energy, solar energy, tidal energy, wind energy) is on its way to strong growth around the world over the next dozen years. With the increasing demand for energy, new technologies and materials are being developed to replace exhaustible traditional construction materials. This article aims to provide a comprehensive overview of the research into the application of composite materials in mainstream power generation. The main energy generation technologies, i.e., photovoltaic panels, wind turbines, fuel cells, and biogas generators, were analysed and discussed. The review presented in this article also covers the latest achievements and prospects for the use of composite materials in energy generation devices.

The increased demand for wear-resistant and low-weight components in the automobile industry has led to the utilization of aluminum metal matrix composite parts due to their improved performance. The current work focused on optimizing the tribological factors such as applied load, rotational speed, and percentage weight fraction of the reinforcing particles for minimum dry sliding wear rate of Al6061/nano-Al₂O₃/quartz hybrid composite fabricated by stir casting. The optimization is performed by using the Taguchi L₂₇ orthogonal array experimental plan, and the result is analyzed by the help of analysis of variance (ANOVA). The result of the optimization reveals that the optimum levels of factors for the minimum wear rate are 10N, 200rpm, and 12.5. Furthermore, the ANOVA result depicts that the applied load has the highest impact (87.83) on the wear rate, followed by rotational speed (10.06) and percentage weight fraction of the reinforcement (1.60). The developed linear regression model reveals that the applied load and the rotational speed have a positive relation with the sliding wear rate. However, the percentage weight fraction of the reinforcement has a negative relation. The confirmation test proves that the predicted value of wear rate using the regression equation at optimum levels has a closer agreement with the experimental result having a 6.934 error. Furthermore, the physical property test reveals that the rise in the weight percentage of quartz particles results in a corresponding increase in the percentage porosity of the hybrid composite.

Manufacturing is the foundation of any industrialized country and involves making products from raw materials using various processes. Additive manufacturing (AM) was originally created as a method for swift prototyping, allowing the visualization, testing, and validation of a design prior to final production for end-users. FDM is the most commonly used additive manufacturing process for constructing products and prototypes. It encompasses numerous process parameters that impact the quality of manufactured products. Properly selecting these process parameters is crucial for producing products at a lower cost while enhancing mechanical properties, build time, and part quality, among other factors. Therefore, in the past, researchers have optimized the process parameters to achieve the desired product outcomes. In the present study, we provide an overview of FDM process parameters and review various design optimization methods. We present several experimental designs, such as the Taguchi method, response surface methodology, and design of experiments, as well as computational approaches like artificial intelligence, and machine learning.

Over the last century, the growing demand for clean energy has emphasized wind energy as a promising solu-tion to address contemporary energy challenges. Within the realm of wind energy, the wind turbine plays a pivotal role in harnessing the kinetic energy of the wind and converting it into electrical power. Among the various components of the wind turbine system, turbine blades assume a critical role in capturing the wind's kinetic energy and converting it into rotational motion. Consequently, the design of wind turbine blades holds the utmost importance in determining the overall performance and efficiency of the entire wind turbine system. One essential aspect of blade design involves selecting an appropriate airfoil. Throughout history, numerous airfoil profiles have been developed for various applications. Notably, National Advisory Committee for Aeronautics (NACA) and National Renewable Energy Laboratory (NREL) airfoils have been tailored for aircraft and large-scale wind turbine blades, respectively. However, the quest for suitable airfoil types for small-scale wind turbine blades has been ongoing. This study delves into an examination of over 62 distinct NACA and NREL aerofoil types tailored for small horizontal-axis wind turbine blades. Employing specialized software, namely QBlade, specifically designed for modeling and simulating wind turbine blades, the study calculates key parameters such as power output, stress, deformation, and weight for each airfoil. Subsequently, based on the simulated data, the optimal airfoil is identified using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria selection approach. This selection process takes into account simulation results pertaining to power output, stress, deformation, and weight. The decision-making process involving multiple criteria is facilitated using Excel and Python. The findings of this study reveal that among the 62 airfoil types under consideration, the NACA 0024, NACA 2424, and NACA 4424 airfoils emerge as the most suitable choices for small horizontal-axis wind turbine blades.