Together, 316L steel, magnesium-alloy, Ni-Ti, titanium-alloy, and cobalt-alloy are commonly employed biomaterials for biomedical applications due to their excellent mechanical characteristics and resistance to corrosion, even though at times they can be incompatible with the body. This is attributed to their poor biofunction, whereby they tend to release contaminants from their attenuated surfaces. Coating of the surface is therefore required to mitigate the release of contaminants. The coating of biomaterials can be achieved through either physical or chemical deposition techniques. However, a newly developed manufacturing process, known as powder mixed-electro discharge machining (PM-EDM), is enabling these biomaterials to be concurrently machined and coated. Thermoelectrical processes allow the migration and removal of the materials from the machined surface caused by melting and chemical reactions during the machining. Hydroxyapatite powder (HAp), yielding Ca, P, and O, is widely used to form biocompatible coatings. The HAp added-EDM process has been reported to significantly improve the coating properties, corrosion, and wear resistance, and biofunctions of biomaterials. This article extensively explores the current development of bio-coatings and the wear and corrosion characteristics of biomaterials through the HAp mixed-EDM process, including the importance of these for biomaterial performance. This review presents a comparative analysis of machined surface properties using the existing deposition methods and the EDM technique employing HAp. The dominance of the process factors over the performance is discussed thoroughly. This study also discusses challenges and areas for future research.
Wind speed interval prediction is gaining importance in optimal planning and operation of power systems. However, the unpredictable characteristics of wind energy makes quality forecasting an arduous task. In this paper, we propose a novel hybrid model for wind speed interval prediction using an autoencoder and a bidirectional long short term memory neural network. The autoencoder initially extracts important unseen features from the wind speed data. The artificially generated features are utilized as input to the bidirectional long short term memory neural network to generate the prediction intervals. We also demonstrate that for time series prediction tasks, feature extraction through autoencoder is more effective than making deep residual networks. In our experiments which involve eight cases distributed among two wind fields, the proposed method is able to generate narrow prediction intervals with high prediction interval coverage and achieve an improvement of 39% in coverage width criterion over the traditional models.
Master mold fabricated using micro milling is an easy way to develop the polydimethylsiloxane (PDMS) based microfluidic device. Achieving high-quality micro-milled surface is important for excellent bonding strength between PDMS and glass slide. The aim of our experiment is to study the optimal cutting parameters for micro milling an aluminum mold insert for the production of a fine resolution microstructure with the minimum surface roughness using conventional computer numerical control (CNC) machine systems; we also aim to measure the bonding strength of PDMS with different surface roughnesses. Response surface methodology was employed to optimize the cutting parameters in order to obtain high surface smoothness. The cutting parameters were demonstrated with the following combinations: 20,000 rpm spindle speed, 50 mm/min feed rate, depth of cut 5 µm with tool size 200 µm or less; this gives a fine resolution microstructure with the minimum surface roughness and strong bonding strength between PDMS–PDMS and PDMS–glass.
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