The manufacturing industry continues to suffer from inefficiency, excessively high prices, and uncertainty over product quality. This statement remains accurate despite the increasing use of automation and the significant influence of Industry 4.0 and AI on industrial operations. This review details an extensive analysis of a substantial body of literature on artificial intelligence (AI) and Industry 4.0 to improve the efficiency of material processing in manufacturing. This document includes a summary of key information (i.e., various input tools, contributions, and application domains) on the current production system, as well as an in-depth study of relevant achievements made thus far. The major areas of attention were adaptive manufacturing, predictive maintenance, AI-driven process optimization, and quality control. This paper summarizes how Industry 4.0 technologies like Cyber-Physical Systems (CPS), the Internet of Things (IoT), and big data analytics have been utilized to enhance, supervise, and monitor industrial activities in real-time. These techniques help to increase the efficiency of material processing in the manufacturing process, based on empirical research conducted across different industrial sectors. The results indicate that Industry 4.0 and AI both significantly help to raise manufacturing sector efficiency and productivity. The fourth industrial revolution was formed by AI, technology, industry, and convergence across different engineering domains. Based on the systematic study, this article critically explores the primary limitations and identifies potential prospects that are promising for greatly expanding the efficiency of smart factories of the future by merging Industry 4.0 and AI technology.

Cooling Rate (CR) definitively influences the microstructure of metallic parts manufactured through various processes. Factors including cooling medium, surface area, thermal conductivity, and temperature control can influence both predicted and unforeseen impacts that then influence the results of mechanical properties. This comprehensive study explores the impact of CRs in diffusion, microstructural development, and the characterization of aluminum alloys and the influence of various manufacturing processes and post-process treatments, and it studies analytical models that can predict their effects. It examines a broad range of CRs encountered in diverse manufacturing methods, such as laser powder bed fusion (LPBF), directed energy deposition (DED), casting, forging, welding, and hot isostatic pressing (HIP). For example, varying CRs might result in different types of solidification and microstructural evolution in aluminum alloys, which thereby influence their mechanical properties during end use. The study further examines the effects of post-process heat treatments, including quenching, annealing, and precipitation hardening, on the microstructure and mechanical properties of aluminum alloys. It discusses numerical and analytical models, which are used to predict and optimize CRs for achieving targeted material characteristics of specific aluminum alloys. Although understanding CR and its effects is crucial, there is a lack of literature on how CR affects alloy properties. This comprehensive review aims to bridge the knowledge gap through a thorough literature review of the impact of CR on microstructure and mechanical properties.

This paper reviews the status of nanoparticle technology as it relates to the additive manufacturing (AM) of aluminum-based alloys. A broad overview of common AM processes is given. Additive manufacturing is a promising field for the advancement of manufacturing due to its ability to yield near-net-shaped components that require minimal post-processing prior to end-use. AM also allows for the fabrication of prototypes as well as economical small batch production. Aluminum alloys processed via AM would be very beneficial to the manufacturing industry due to their high strength to weight ratio; however, many of the conventional alloy compositions have been shown to be incompatible with AM processing methods. As a result, many investigations have looked to methods to improve the processability of these alloys. This paper explores the use of nanostructures to enhance the processability of aluminum alloys. It is concluded that the addition of nanostructures is a promising route for modification of existing alloys and may be beneficial to other powder-based processes.

In additive manufacturing (AM), the surface roughness of the deposited parts remains significantly higher than the admissible range for most applications. Additionally, the surface topography of AM parts exhibits waviness profiles between tracks and layers. Therefore, post-processing is indispensable to improve surface quality. Laser-aided machining and polishing can be effective surface improvement processes that can be used due to their availability as the primary energy sources in many metal AM processes. While the initial roughness and waviness of the surface of most AM parts are very high, to achieve dimensional accuracy and minimize roughness, a high input energy density is required during machining and polishing processes although such high energy density may induce process defects and escalate the phenomenon of wavelength asperities. In this paper, we propose a systematic approach to eliminate waviness and reduce surface roughness with the combination of laser-aided machining, macro-polishing, and micro-polishing processes. While machining reduces the initial waviness, low energy density during polishing can minimize this further. The average roughness ($R_a=1.11\mathsf{\mu }$m) achieved in this study with optimized process parameters for both machining and polishing demonstrates a greater than 97% reduction in roughness when compared to the as-built part.

Scalmalloy^® is an Al-Mg-Sc-Zr-based alloy specifically developed for additive manufacturing (AM). This alloy is designed for use with a direct aging treatment, as recommended by the manufacturer, rather than with a multistep treatment, as often seen in conventional manufacturing. Most work with Scalmalloy^® is conducted using powder bed rather than powder-fed processes. This investigation seeks to fill this knowledge gap and expand beyond single-step aging to promote an overall balanced AM-fabricated component. For this study, directed energy deposition (DED)-fabricated Scalmalloy^® components were subjected to low-temperature treatments to minimize residual stresses inherent in the material due to the layer-by-layer build process. X-ray diffraction (XRD) indicated the possibility of stress minimization while reducing the detriment to mechanical strength through lower temperature treatments. Microstructural analyses consisting of energy dispersion spectroscopy (EDS) and electron backscatter diffraction (EBSD) revealed the presence of grain growth detrimentally affecting the strength and elongation made possible by very small grains inherent to AM and rapid solidification. Tensile testing determined that treatment at 175 °C for 1 h provides the best relief from the existing residual stresses; however, this is accompanied by a diminishment in the yield and tensile strength of 19 and 9.5%, respectively. It is noted that treatment at 175 °C for 2 h did not provide as great of a decrease in residual stresses, theorized to be the result of grain growth and other strengthening mechanisms further stressing the structure; however, the residual stresses are still significantly diminished compared with the as-built condition. Furthermore, a minimal reduction of the tensile strengths indicates the possibility of finding a balance between property diminishment and stress state through the work proposed here.