Additive manufacturing (AM) or 3D printing is a digital manufacturing process and offers virtually limitless opportunities to develop structures/objects by tailoring material composition, processing conditions, and geometry technically at every point in an object. In this review, we present three different early adopted, however, widely used, polymer-based 3D printing processes; fused deposition modelling (FDM), selective laser sintering (SLS), and stereolithography (SLA) to create polymeric parts. The main aim of this review is to offer a comparative overview by correlating polymer material-process-properties for three different 3D printing techniques. Moreover, the advanced material-process requirements towards 4D printing via these print methods taking an example of magneto-active polymers is covered. Overall, this review highlights different aspects of these printing methods and serves as a guide to select a suitable print material and 3D print technique for the targeted polymeric material-based applications and also discusses the implementation practices towards 4D printing of polymer-based systems with a current state-of-the-art approach.

Casting is one of the oldest manufacturing processes that has been in use since 3400 BC. Over the years, casting technology has evolved tremendously and is one of the most integral parts of ancient history as well as a modern society. The world produced a total of 109.8 million tons of casting in the year 2017, which is a clear indicator of the massive capacity of this industry. Most of it in the current scenario is being used by the automotive industries. Despite being the biggest and the richest industry in the field of energy, hydropower has never been a major market for the casting industry. This might be because the hydropower components are mostly manufactured using other techniques such as machining and rolling. Nevertheless, studies have proven that casting can be used for the manufacturing of several components of the hydropower, especially hydro turbines. Casting technology comes with its own sets of advantages and limitations. This study presents an overview of the status of the casting technology and challenges, the proper optimization in casting that needs to be considered, and the latest technological advancements in this area. This paper aims to develop a theoretical foundation for show-casing the beneficiary, challenges, and possibility of manufacturing hydro turbines through casting technology.

Entangled by a web-like pattern of rivers and rivulets, Nepal’s energy sector currently is dominated by hydroelectricity which sustains a major portion of the country’s economy. But still, after more than a hundred years of its operation, the sector’s dependence on imports is uncanny. This not only upsizes the project cost but also the time and human resources. The research, therefore, aims to aid the cause by examining the prospect of manufacturing runners using local industrial expertise in synchronization with available data and research. Here, the paper focuses on the sand casting of a 750KW Francis runner and the design of its parameters namely gating and feeding system to ensure directional solidification. Apart from the conventional procedure, the casting of the runner was proceeded as three separate parts: hub, shroud, and blade which were later welded together. The gate and riser placement for each of the parts was designed independently and analysed for any shrinkage porosities and possible voids using computer simulation with several iterations each. Specifically, temperature variations during three major stages of solidification assigned as 0, 50, and 100 were tested for a total of 195 nodes. The design was then transferred into the mould and casting was performed. The procedure resulted in a cast with a clean surface finish and minimal visible defects. The study, thus, suggests the possibility of manufacturing runners through metal casting and its substantial implications in the improvement of the hydropower industry in Nepal.

Convolutional Neural Networks are deep learning algorithms that are typically used for the classification of images. A black and white image can be represented as the number of rows and columns along with values of intensity for each of components of these rows and columns. Vibration measured with the help of accelerometer can be used to create an image like data. These data can be used for machine learning applications with the help of deep learning. In deep learning techniques, features do not have to be extracted from the data set. The CNN network can generate a feature-like set which can be further classified with a fully connected neural network to give out the probability over the predefined class. The neural networks model is trained with a data set created in laboratory conditions to monitor the unfractured and single blade fracture condition operation state. The paper deals with showing the proof of concept that CNN algorithms can be applied for conditional monitoring of runner blades. The paper mainly focuses on demonstrating that deep learning algorithms can be used as analysis tools for hydropower runners by applying these techniques in scaled versions of 3D printed runners.

Most of the hydro turbines in Nepalese power plants are imported from foreign industries. Findings from the studies have shown that up to 60% of the 13,000 MW capacity hydropower projects under the survey stage in Nepal would need Francis type of turbine with unit size below 5 MW. To meet the demand for turbines, Nepal has imported turbines worth US$ 5,616,072. The imported turbines from foreign industries could not address local problems of Nepalese hydropower. In Nepal, due to the sediment-laden condition of rivers alternative design and manufacturing techniques of turbines are necessary. To provide the new manufacturing techniques of Francis turbine in Nepal local casting process can be a solution because metal casting is a hereditary profession in Nepal since ancient periods till now. Large-sized bronze cast bells that are placed in Nepalese temples were manufactured in ancient times without a proper theoretical study on them. This paper discusses the materials testing of investment cast and sand cast brass materials of 14 kW Francis runner of Turbine Testing Lab (TTL) for exploring the possibilities of manufacturing Francis turbine. Tensile, Compressive and Charpy impact tests were performed base on ASTM standards and also the microscopic study was conducted at Kathmandu university laboratory. These testing results can be helpful for further studies on alternate turbine manufacturing processes as well.