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Chatter is a phenomenon that occurs during machining operations, causing vibrations that can negatively impact the quality of the machined surface. Detecting and avoiding chatter is crucial for efficient machining processes. Various strategies have been developed to address this issue, including offline chatter prediction, online chatter detection and suppression, and the use of artificial intelligence (AI) solutions in line with Industry 4.0 trends. However, the topic of chatter detection is partially discussed as a section in some review publications, and it does not appear as a kernel focus. With the addition of the latest development in chatter detection and suppression, conducting a rigorous review of chatter is critical. This work entails tracing analytical chatter detection techniques (stability lobe diagram, Nyquist plot, finite element analysis), experimental chatter detection techniques by using various data acquisition signals and from time–frequency signal processing methods (fast Fourier transform, discrete wavelet transform, hilbert-huang transform, short-time Fourier transform, etc.), as well as the most recent AI techniques (artificial neural network, support vector machine, hidden markov model, fuzzy logic, k-nearest neighbor, etc.). A thorough investigation was conducted to determine the limitations of these various techniques and to provide potential solutions for detecting chattering in machining processes. Moreover, The approaches for suppressing chatter (active + passive) during the machining process will also be thoroughly reviewed in this article. [ABSTRACT FROM AUTHOR]

The objective of this research is to examine the impact of welding parameters on the surface roughness, hardness, and tensile strength of welds produced by a single-pass TIG welding process in austenitic stainless-steel plates (AISI 316L). Furthermore, the optimal processing variable settings will be identified in order to provide the highest levels of hardness, lowest levels of surface roughness, and highest levels of tensile strength. This study focuses on three parameters: arc current, voltage, and shielding gas flow rate, which were altered across three levels. A large number of tests were carried out on 3-mm thick AISI 316L stainless steel plates in order to determine the efficient and realistic operating limits of TIG welding settings. The welding process parameters’ operational limitations were determined by visual examination and bead shape. The values of current are taken as 100, 125, and 150 (A); voltage as 16, 18, and 20 (V); and gas flow rate as 6, 9, and 12 (L/min). Their impact on tensile strength, microhardness, and surface roughness was inspected. The experiments were conducted using Taguchi L9 orthogonal array (OA). Each input parameter was found to have an impact on the response. The optimized welding parameters for improved mechanical properties and surface roughness were identified. The results indicated an optimum tensile strength, 624.92 MPa, achieved at the parametric combination of 100 A current, voltage of 16 V, and gas flow of rate 6 L/min. The microhardness results indicated an optimum value, of 319.2 HV, achieved at the parametric combination of a current of 125 A, 16 V of voltage, and a gas flow rate of 9 L/min. A minimum surface roughness, 7.33 µm, was attained at 125 A current, voltage of 16 V, and gas flow rate of 9 L/min. These results highlight the significant impact of welding parameters on the mechanical properties and surface roughness of AISI 316L stainless steel welds. [ABSTRACT FROM AUTHOR]

The student's performance prediction is an important research topic because it can help teachers prevent students from dropping out before final exams and identify students that need additional assistance. The objective of this study is to predict the difficulties that students will encounter in a subsequent digital design course session. We analyzed the data logged by a technology-enhanced learning (TEL) system called digital electronics education and design suite (DEEDS) using machine learning algorithms. The machine learning algorithms included an artificial neural networks (ANNs), support vector machines (SVMs), logistic regression, Naïve bayes classifiers and decision trees. The DEEDS system allows students to solve digital design exercises with different levels of difficulty while logging input data. The input variables of the current study were average time, total number of activities, average idle time, average number of keystrokes and total related activity for each exercise during individual sessions in the digital design course; the output variables were the student(s) grades for each session. We then trained machine learning algorithms on the data from the previous session and tested the algorithms on the data from the upcoming session. We performed k-fold cross-validation and computed the receiver operating characteristic and root mean square error metrics to evaluate the models' performances. The results show that ANNs and SVMs achieve higher accuracy than do other algorithms. ANNs and SVMs can easily be integrated into the TEL system; thus, we would expect instructors to report improved student's performance during the subsequent session. [ABSTRACT FROM AUTHOR]

Date palm ash (DPA) and MgAl-layered double hydroxide (LDH) composites were synthesized by the co-precipitation method and characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), X-ray diffraction (XRD), and Brunauer-Emmett-Teller (BET). The DPA-MgAl-LDH (DPA/MgAl) composites were employed for the removal of methyl orange (MO) and eriochrome black-T (EBT) from aqueous phase. Incorporation of 33.33% (w/w) DPA into the layers of MgAl increased the surface area from 44.46 to 140.65 m2/g, which leads to the improved adsorption performance. The maximum adsorption capacity of DPA/MgAl (1:2) at 298 K was 242.98 and 425.16 (mg/g) for MO and EBT, respectively. The adsorption data of dyes were adequately fitted by a pseudo-second-order and Langmuir isotherm model. The composite showed excellent reusability performance up to three cycles. Addition of DPA into MgAl-LDH resulted in an effective low-cost adsorbent for decontamination of dyes from wastewater.ᅟ [ABSTRACT FROM AUTHOR]

The exceptional properties of graphene make it ideal as a reinforcement to enhance the properties of aluminum matrices and this critically depends on uniform dispersion. In this study, the dispersion issue was addressed by sonication and non-covalent surface functionalization of graphite nanoplatelets (GNPs) using two types of surfactant: anionic (sodium dodecyl benzene sulfate (SDBS)) and non-ionic polymeric (ethyl cellulose (EC)). After colloidal mixing with Al powder, consolidation was performed at two sintering temperatures (550 and 620°C). The structure, density, mechanical and wear properties of the nanocomposite samples were investigated and compared with a pure Al and a pure GNPs/Al nanocomposite sample. Noticeably, EC-based 0.5wt% GNPs/Al samples showed the highest increment of 31% increase in hardness with reduced wear rate of 98.25% at 620°C, while a 22% increase in hardness with reduced wear rate of 96.98% at 550°C was observed, as compared to pure Al. Microstructural analysis and the overall results validate the use of EC-based GNPs/Al nanocomposites as they performed better than pure Al and pure GNPs/Al nanocomposite at both sintering temperatures. [ABSTRACT FROM AUTHOR]

Effect of mango kernel fat (MKF) on antioxidant characteristics and lipolysis of Gouda cheese was investigated. Milk fat (3.5%) was partially replaced with MKF i.e. 5, 10, 15 and 20% concentrations (T1, T2, T3 and T4). Cheese prepared from 100% milk fat served as control. Cheese samples were ripened for 90 days at 6 ± 1 °C and analysed at 0, 45 and 90 days of ripening. Total phenolic contents of control, T1, T2, T3 and T4 were 14 ± 0.35, 129 ± 0.75, 188 ± 2.52, 267 ± 10.61 and 391 ± 8.46 mg GAE/g. Total flavonoid content of control, T1, T2, T3 and T4 were 0.22 ± 0.03, 1.47 ± 0.09, 3.62 ± 0.15, 5.88 ± 0.35, 8.29 ± 0.63 mg quercetin equivalent/ml. DPPH free radical scavenging activity of control and experimental samples increased throughout the ripening period. DPPH free radicals scavenging activity of 90 days old control, T1, T2, T3 and T4 were 16.38 ± 0.0.26e, 30.47 ± 0.64d, 68.62 ± 0.91c, 73.29 ± 0.85b, 92.61 ± 1.44a %. HPLC characterization revealed the existence of mangiferin, caffeic acid, catechin, quercetin and chlorogenic acid in MKF fortified Gouda cheese. Fortification of MKF increased the concentration of C18:1, C18:2 and C18:3 in cheese. The concentration of C18:1, C18:2 and C18:3 in control were 24.55 ± 0.95, 1.76 ± 0.09 and 0.31 ± 0.02%. While, the concentration of C18:1, C18:2 and C18:3 in T4 were 30.11 ± 1.34, 2.79 ± 2.79 and 0.92 ± 0.11%. MKF fortified Gouda cheese had better oxidative stability and sensory characteristics. These results evidenced that antioxidant capacity, unsaturated fatty acids and oxidative stability of Gouda cheese can be improved with MKF. [ABSTRACT FROM AUTHOR]