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Practitioners in the industry are developing predictive methods for assessing key parameters and responses of engineering materials. The aim of this research is to optimize the average surface roughness (R a), flexural strength (FS), tensile strength (TS), print time (T), and print energy consumption (E) of 3D printed Nylon 6 (PA6). Quantitative parameters for infill density (ID), layer thickness (LT), and print speed (PS) were selected. Employing the central component design (CCD)-response surface methodology (RSM) for investigational design, statistical analysis, and multi-objective optimization, a total of 20 samples were produced and analyzed to develop prediction models. The implication of the selected parameters was confirmed through variance analysis (ANOVA), and the models were validated using confirmatory trial tests. It was found that LT was essential in achieving appropriate R a and T values, while ID was a crucial factor in obtaining the necessary mechanical properties. RSM optimization led to an FS of 70.8 MPa, TS of 40.8 MPa, lowest T of 53 min, lowest possible R a of 8.30 µm, and 0.203 kW·h “E” at ID = 84%, LT = 0.21 mm, and PS = 75 mm·s−1. The study also revealed weak bond strength between layers and layers debonding after bending tests, as shown in SEM micrographs. The PA6 material exhibited flexibility during tensile testing, going into plasticity before breaking. The created numerically optimized model is anticipated to benefit manufacturers and practitioners in predicting the required surface quality for various factors before conducting experiments, ultimately improving 3D printing (3DP) processes and outcomes. Despite limitations such as limited parameter selection, small sample size, and material-specific focus, this research presents valuable insights for the 3DP industry.

This research introduces the Iterative Overlap Optimization Placement (IOOP) method, a novel approach designed to enhance the efficiency of irregular pattern packing by dynamically optimizing overlap ratios and pattern placements. Utilizing a modified genetic algorithm, IOOP addresses the complexities of arranging irregular patterns in a given space, focusing on improving spatial and material efficiency. This study demonstrates the method's superiority over the traditional Size-First Non-Iterative Overlap Optimization Placement technique through comparative analysis, highlighting significant improvements in spatial utilization, flexibility, and material conservation. The effectiveness of IOOP is further validated by its robustness in handling diverse pattern groups and its adaptability in adjusting pattern placements iteratively. This research not only showcases the potential of IOOP in manufacturing and design processes requiring precise spatial planning but also opens avenues for its application across various industries, underscoring the need for further exploration into advanced technological integrations for tackling complex spatial optimization challenges. [ABSTRACT FROM AUTHOR]

This research addresses the dearth of real-world data required for effective neural network model building, delving into the crucial field of industrial control and automation system (ICS) cybersecurity. Cyberattacks against ICS are first identified and then generated in an effort to raise awareness of vulnerabilities and improve security. This research aims to fill a need in the existing literature by examining the effectiveness of a novel approach to ICS cybersecurity that draws on data from real industrial settings. Real-world data from a variety of commercial sectors is used in this study to produce a complete dataset. These sectors include power systems, freshwater tanks, and gas pipelines, which together provide a wide range of commercial scenarios where anomaly detection and attack classification approaches are critical. The generated data are shown to considerably improve the models' precision. An amazing 71% accuracy rate is achieved in power system models, and incorporating generated data reliably increases network speed. Using generated data, the machine learning system achieves an impressive 99% accuracy in a number of trials. In addition, the system shows about 90% accuracy in most studies when applied to the setting of gas pipelines. In conclusion, this article stresses the need to improve cybersecurity in vital industrial sectors by addressing the dearth of real-world ICS data. To better understand and defend against cyberattacks on industrial machinery and automation systems, it demonstrates how generative data can improve the precision and dependability of neural network models. [ABSTRACT FROM AUTHOR]

Electrical discharge machining (EDM) has emerged as a pivotal non-conventional production technique due to its unique capability to machine without the cutting tool's physical contact with the workpiece, making it apt for brittle, delicate, and complex materials. This research delved into the influence of operational parameters—pulse duration (Ton), peak current (Ip), duty cycle (T), and gap voltage (Vg)—on machining attributes, namely material removal rate (MRR), electrode wear rate (EWR), and radial overcut (ROC) for AISI D2 steel. Utilizing the Taguchi L9 orthogonal array for experimental design, nine experiments were conducted, followed by signal-to-noise ratio (S/N ratio) computations. Key findings highlighted a 4.02 dB improvement in the S/N ratio for MRR, leading to a 29.13% improvement; a 10.35 dB enhancement in the S/N ratio for EWR, resulting in a 33.33% reduction; and a 2.20 dB increase in the S/N ratio for ROC, leading to a 28.57% increment. ANOVA analyses further underscored the predominant influence of all four parameters. The significance of these findings lies in optimizing the EDM process for increased efficiency, reduced tool wear, and enhanced precision, potentially leading to cost savings and improved production quality in industrial applications. [ABSTRACT FROM AUTHOR]

Introduction and Objective: In recent years, load monitoring and analysis have become increasingly important in athletic training. The aim of this study was to provide a background for businesses and institutes to prepare for the implementation of load training and analysis in sports training, utilizing visual analysis of CiteSpace (CS) software., Material and Methods: A total of 169 original publications were obtained from Web of Science using a comprehensive list for analysis with the CS scientometrics program. The parameters included range (2012-2022), visualization (display of completely integrated networks), precise collection criteria (top 10%), node form (institution, author, area, reference cited; referenced author, key words, and journal), and trimming (pathfinder, slice network)., Results: Visual analysis of load monitoring and analysis for use in athletic training showed that 'questionnaire' was the most popular topic area in 2017 with 51 citations, while 'training programmes' emerged as a new area of study with 8 citations. In 2021 and 2022, the terms 'energy expenditure', 'responses', 'heart rate', and 'validity' gained popularity, increasing from a strength of 1.81 to 1.1. Liverpool John Moores University was the top institution, collaborating with 14 other organizations. The leading authors in this field were Close, Graeme L., and Gastin, Paul B. Most publications were found in the 'SPORTS MED' journal, with authors primarily based in the United Kingdom, the United States, and Australia., Conclusions: The findings of the study highlight the potential frontiers of load training analysis in the research and management of sports, emphasizing the importance of preparing businesses and institutes for the implementation of load training, and analysis in athletic training.

Carbon dioxide (CO 2 ) laser cutting finds one of its most relevant applications in the processing of a wide variety of polymeric materials like thermoplastics and thermosetting plastics. Different types of polymeric materials like polypropylene (PP), polymethyl methacrylate (PMMA), low- and high-density polyethylene (LDPE, HDPE), are processed by laser for different household as well as commercial products in the industry. The reason is their easy availability and economical aspect in the market. The problems associated with laser cutting include heat-affected zone (HAZ) generated on the cut surface, kerf width (KW), surface roughness (SR), dross formation, and striations formation. Furthermore, other related problems include taper cutting for deep parts and high-power consumption. The primary purpose of this work is a comprehensive literature review in CO 2 laser cutting of polymeric materials. The influence of parametric variation on the cut quality is also explained. Cut quality in terms of KW, SR, HAZ, dross formation, and striations formation is analyzed by optimizing cutting variables like laser power (P L ), cutting speed (CS), assist gas pressure (P g ), pulse frequency, nozzle type and its diameter, and stand-off distance (SOD). The effects of the laser cutting on the properties of different thermoplastics/thermosetting materials are also reported. However, this topic requires further studies on exploring the range of polymeric materials, and their optimal parameters selection to improve the cut quality. Therefore, the research gaps and future research directions are also highlighted in the context of CO 2 laser cutting for polymeric materials.