Study of the Processing of a Recycled WC–Co Powder: Can It Compete with Conventional WC–Co Powders?

Cemented carbide tools suffer from many issues due to the use of tungsten and cobalt as raw materials. Indeed, those are listed by the European Commission as “critical raw materials” since 2011 and by the US Department of Interior as “critical minerals” in 2018. To remain competitive with the conventional high-speed steels, less performant but cheaper, WC–Co tools can be recycled. In the present paper, a WC–7.5Co powder, recycled by the “Coldstream” process, has been sintered with vacuum sintering. As preliminary experiments have shown that the sinterability of the powder is low, the sintering temperature was set at 1500 °C to achieve full density. In parallel, the influence of ball milling conditions (rotation speed and milling medium) on the reactivity of the recycled powder has been studied in terms of grain size distribution, hardness, and fracture toughness. The optimized milling conditions were found to be 6 h wet milling, leading to a hardness of about 1870 HV30 and a toughness of about 10.5 MPa√m after densification. The recycled powder can thus totally compete with conventional powders, opening avenues for the recycling of cemented carbide tools. Graphical Abstract

Effect of Different Rotational Speeds on Graphene-Wrapped SiC Core-Shell Nanoparticles in Wet Milling Medium

The effects of the wet milling rotating speed on the number of graphene layers and graphene quality, and the conversion efficiency of graphite exfoliate to graphene, were investigated by scanning electron microscopy (SEM), high resolution transmission electron microscopy (HRTEM), Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). The results show that the number of few-layer graphene nanometer sheets (GNSs) (≤10 layers) gradually increases with the increase of rotational speed in the range of 160–240 rpm. The proportion of GNSs with 0–10 layers reaches more than 80% as the rotational speed is increased to 280 rpm. GNS defect types in the composite materials are marginal defects with minimal influence and almost no oxidation. In the range of 160–280 rpm, the intensity of graphite peak decreases and the conversion efficiency of graphene increases with the increase of rotational speed. This is the same as the experimental result obtained by HRTEM.

Top-Down Synthesis of a Lamivudine-Zidovudine Nano Co-Crystal

Lamivudine (3TC) and zidovudine (AZT) are antiretroviral agents used to manage HIV/AIDS infection. A wet media milling top-down approach was used to develop and produce nano co-crystals of 3TC and AZT. Micro co-crystals were prepared by solvent evaporation and subsequently milled in the presence of two surfactants, viz., sodium lauryl sulfate (SLS) and α-tocopheryl polyethylene glycol succinate 1000 (TPGS 1000). Optimisation was undertaken using design of experiments (DoE) and response surface methodology (RSM) to establish and identify parameters that may affect the manufacturing of nano co-crystals. The impact of SLS and TPGS 1000 concentration, milling time, and number of units of milling medium on the manufacturing of nano co-crystals, was investigated. The critical quality attributes (CQA) monitored were particle size (PS), Zeta potential (ZP), and polydispersity index (PDI). Powder X-ray diffraction, Fourier transform infrared spectroscopy, differential scanning calorimetry, transmission electron microscopy, energy dispersive X-ray spectroscopy scanning electron microscopy, and cytotoxicity assays were used for additional characterization of the optimised nano co-crystal. The mean PS, PDI, and ZP of the optimised top-down nanocrystal were 271.0 ± 92.0 nm, 0.467 ± 0.073, and −41.9 ± 3.94 mV, respectively. In conclusion, a simple, inexpensive, rapid, and precise method of nano co-crystal manufacturing was developed, validated, and optimised using DoE and RSM, and the final product exhibited the target CQA.

Study on Optimization of Wet Milling Process for the Development of Albendazole Containing Nanosuspension with Improved Dissolution

The main objective of this work was to show the potential of the optimization of top-down wet planetary bead milling process parameters (milling speed, process time and size of the milling medium) by Design Of Experiments (DOE) approach for the development of albendazole (ABZ) containing nanosuspension with improved dissolution. In addition, the influence of process parameters (capacity of milling container, applied volume of milling beads, size of the milling medium, milling speed, milling time) on ABZ polymorphic transition has also been investigated. The optimized, milled formula yielded ~ 145.39 times reduction in mean particle size (182.200 ± 1.3130 nm) compared to unmilled dispersion, which demonstrated 13.50 times gain in mean dissolution rate value compared to the unmilled dispersion in medium at pH = 1.2. No lag time values were observed in the dissolution kinetics of the nanosuspension in comparison with the unmilled samples. Moreover, maximal mean solubility value was also improved by 1.45 times compared to the unmilled suspension, in medium at pH = 6.8, supporting the significance of the Ostwald-Freundlich equation. Diffraction pattern comparisons have indicated a polymorphic transition of albendazole to Form II, which was more pronounced in smaller container at high milling speed values and prolonged operations.

Modeling for prediction of surface roughness in milling medium density fiberboard with a parallel robot

Purpose This paper aims to discuss the utilization of artificial neural networks (ANNs) and multiple regression method for estimating surface roughness in milling medium density fiberboard (MDF) material with a parallel robot. Design/methodology/approach In ANN modeling, performance parameters such as root mean square error, mean error percentage, mean square error and correlation coefficients (R2) for the experimental data were determined based on conjugate gradient back propagation, Levenberg–Marquardt (LM), resilient back propagation, scaled conjugate gradient and quasi-Newton back propagation feed forward back propagation training algorithm with logistic transfer function. Findings In the ANN architecture established for the surface roughness (Ra), three neurons [cutting speed (V), feed rate (f) and depth of cut (a)] were contained in the input layer, five neurons were included in its hidden layer and one neuron was contained in the output layer (3-5-1).Trials showed that LM learning algorithm was the best learning algorithm for the surface roughness. The ANN model obtained with the LM learning algorithm yielded estimation training values R2 (97.5 per cent) and testing values R2 (99 per cent). The R2 for multiple regressions was obtained as 96.1 per cent. Originality/value The result of the surface roughness estimation model showed that the equation obtained from the multiple regressions with quadratic model had an acceptable estimation capacity. The ANN model showed a more dependable estimation when compared with the multiple regression models. Hereby, these models can be used to effectively control the milling process to reach a satisfactory surface quality.

Stabilization of Heavy Metal Containing Smelting Ash through Mechano-Chemical Reaction

The present study described the application of mechano-chemical reaction on stabilization of heavy metal containing smelting ash. Extraction procedure for leaching toxicity test showed that the leaching toxicity of cadmium and lead in smelting ash decreased very significantly after grinded. The optimal condition of the mechano-chemical stabilization process was established as follow. The ball milling medium was stainless steel, the additive was the mixture of elemental sulfur and iron powder, the dosage of additive was n(Cd):n(Fe):n(S)=1:3:3, and ball milling time was 3.5 hour. The results showed that the leaching toxicity of cadmium decreased from 2496 mg/L to 0.43 mg/L, compared to lead decreased from 7.30 mg/L to 0.12mg/L under the optimal condition.