A Supervised Machine Learning Model for Tool Condition Monitoring in Smart Manufacturing

In the current industry 4.0 scenario, good quality cutting tools result in a good surface finish, minimum vibrations, low power consumption, and reduction of machining time. Monitoring tool wear plays a crucial role in manufacturing quality components. In addition to tool monitoring, wear prediction assists the manufacturing systems in making tool-changing decisions. This paper introduces an industrial use case supervised machine learning model to predict the turning tool wear. Cutting forces, the surface roughness of a specimen, and flank wear of tool insert are measured for corresponding spindle speed, feed rate, and depth of cut. Those turning test datasets are applied in machine learning for tool wear predictions. The test was conducted using SNMG TiN Coated Silicon Carbide tool insert in turning of EN8 steel specimen. The dataset of cutting forces, surface finish, and flank wear is extracted from 200 turning tests with varied spindle speed, feed rate, and depth of cut. Random forest regression, Support vector regression, K Nearest Neighbour regression machine learning algorithms are used to predict the tool wear. R squared, the technique shows the random forest machine learning model predicts the tool wear of 91.82% of accuracy validated with the experimental trials. The experimental results exhibit flank wear is mainly influenced by the feed rate followed by the spindle speed and depth of cut. The reduction of flank wear with a lower feed rate can be achieved with a good surface finish of the workpiece. The proposed model may be helpful in tool wear prediction and making tool-changing decisions, which leads to achieving good quality machined components. Moreover, the machine learning model is adaptable for industry 4.0 and cloud environments for intelligent manufacturing systems

1-(2-Azido­acet­yl)-3-methyl-2,6-diphenyl­piperidin-4-one

In the title compound, C20H20N4O2, the piperidine ring adopts a distorted boat conformation. The two phenyl rings form dihedral angles of 82.87 (1) and 84.40 (1)° with respect to the piperidine ring. The crystal packing is stabilized by inter­molecular C—H⋯O and C—H⋯N inter­actions

(E)-Ethyl 2-cyano-3-(1H-pyrrol-2-yl)acrylate

All the non-H atoms of the title compound, C10H10N2O2, are nearly in the same plane with a maximum deviation of 0.093 (1) Å. In the crystal, adjacent mol­ecules are linked by pairs of inter­molecular N—H⋯O hydrogen bonds, generating inversion dimers with R 2 2(14) ring motifs

Multilayer vectorization to develop a deeper image feature learning model

Computer-Aided Diagnosis (CAD) approaches categorise medical images substantially. Shape, colour, and texture can be problem-specific in medical imagery. Conventional approaches rely largely on them and their relationship, resulting in systems that can\u27t illustrate high-issue domain ideas and have weak prototype generalization. Deep learning techniques deliver an end-to-end model that classifies medical photos thoroughly. Due to the improved medical picture quality and short dataset size, this approach may have high processing costs and model layer restrictions. Multilayer vectorization and the Coding Network-Multilayer Perceptron (CNMP) are merged with deep learning to handle these challenges. This study extracts a high-level characteristic using vectorization, CNN, and conventional characteristics. The model\u27s steps are below. The input picture is vectorized into a few pixels during preprocessing. These pixel images are delivered to a coding network being trained to create high-level classification feature vectors. Medical imaging fundamentals determine picture properties. Finally, neural networks combine the collected features. The recommended technique is tested on ISIC2017 and HIS2828. The model\u27s accuracy is 91% and 92%

(Z)-2-[2-(4-Methyl­benzyl­idene)hydrazin­yl]pyridine

Mol­ecules of the title compound, C13H13N3, are essentially planar (r.m.s. deviation for all non-H atoms = 0.054 Å). The dihedral angle between the two aromatic rings is 6.33 (5)°. In the crystal, pairs of centrosymmetrically related mol­ecules are linked through N—H⋯N hydrogen bonds, forming N—H⋯N dimers with graph-set motif R22(8)

Ethyl 4-(3-bromo­phen­yl)-6-methyl-2-oxo-1,2,3,4-tetra­hydro­pyrimidine-5-carboxyl­ate

In the title compound, C14H15BrN2O3, the dihydro­pyrimidin­one ring adopts a boat conformation. In the crystal, adjacent mol­ecules are linked through N—H⋯O hydrogen bonds forming an R22(8) ring motif and generating a zigzag chain extending in [010]

Versatile poly(diallyl dimethyl ammonium chloride)-layered nanocomposites for removal of cesium in water purification

In this work, we elucidate polymer-layered hollow Prussian blue-coated magnetic nanocomposites as an adsorbent to remove radioactive cesium from environmentally contaminated water. To do this, Fe3O4 nanoparticles prepared using a coprecipitation method were thickly covered with a layer of cationic polymer to attach hollow Prussian blue through a self-assembly process. The as-synthesized adsorbent was confirmed through various analytical techniques. The adsorbent showed a high surface area (166.16 m2/g) with an excellent cesium adsorbent capacity and removal efficiency of 32.8 mg/g and 99.69%, respectively. Moreover, the superparamagnetism allows effective recovery of the adsorbent using an external magnetic field after the adsorption process. Therefore, the magnetic adsorbent with a high adsorption efficiency and convenient recovery is expected to be effectively used for rapid remediation of radioactive contamination

Graphene-Based Nanocomposites for Energy Storage

Since the first report of using micromechanical cleavage method to produce graphene sheets in 2004, graphene/graphene-based nanocomposites have attracted wide attention both for fundamental aspects as well as applications in advanced energy storage and conversion systems. In comparison to other materials, graphene-based nanostructured materials have unique 2D structure, high electronic mobility, exceptional electronic and thermal conductivities, excellent optical transmittance, good mechanical strength, and ultrahigh surface area. Therefore, they are considered as attractive materials for hydrogen (H2) storage and high-performance electrochemical energy storage devices, such as supercapacitors, rechargeable lithium (Li)-ion batteries, Li–sulfur batteries, Li–air batteries, sodium (Na)-ion batteries, Na–air batteries, zinc (Zn)–air batteries, and vanadium redox flow batteries (VRFB), etc., as they can improve the efficiency, capacity, gravimetric energy/power densities, and cycle life of these energy storage devices. In this article, recent progress reported on the synthesis and fabrication of graphene nanocomposite materials for applications in these aforementioned various energy storage systems is reviewed. Importantly, the prospects and future challenges in both scalable manufacturing and more energy storage-related applications are discussed

Facile preparation of a cellulose microfibers–exfoliated graphite composite: a robust sensor for determining dopamine in biological samples

© 2017, Springer Science+Business Media B.V. A simple and robust dopamine (DA) sensor was developed using a cellulose microfibers (CMF)–exfoliated graphite composite-modified screen-printed carbon electrode (SPCE) for the first time. The graphite-CMF composite was prepared by sonication of pristine graphite in CMF solution and was characterized by high-resolution scanning electron microscopy, Fourier transform, infrared, and Raman spectroscopy. The cyclic voltammetry results reveal that the graphite-CMF composite modified SPCE has superior electrocatalytic activity against oxidation of dopamine than SPCE modified with pristine graphite and CMF. The presence of large edge plane defects on exfoliated graphite and abundant oxygen functional groups of CMF enhance electrocatalytic activity and decrease potential to oxidize DA. Differential pulse voltammetry was used to quantify DA using the graphite-CMF composite-modified SPCE and demonstrated a linear response for DA detection in the range of 0.06–134.5 µM. The sensor shows a detection limit at 10 nM with an appropriate sensitivity and displays appropriate recovery of DA in human serum samples with good repeatability. Sensor selectivity is demonstrated in the presence of 50-fold concentrations of potentially active interfering compounds including ascorbic acid, uric acid, and dihydroxybenzene isomers