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Performance optimisation of the turning process along with multi-surface heating process

Sathish Kumar D., Thanigaivelan R., Natarajan N.

Materials Science Poland

2022

Vol. 40, No. 4

1--13

Materials that are difficult to cut possess excellent qualities and machinability, though conventional machining techniques require additional energy to circumvent the problems associated with the turning process. In this study, heat-assisted turning of duplex stainless steel (SS) was carried out. Various heating techniques such as infrared (IR)-, ultraviolet (UV)- and hot air(HA)-assisted heating were adopted. The experiment used an L16 orthogonal array with the most significant parameters such as heating method, feed rate in millimetres per revolution (mm/rev), depth of cut (millimetres [mm]) and cutting speed (metres per minute [m/min])on the cutting force and surface roughness. The technique for order performance by similarity to ideal solution (TOPSIS) and grey relational analysis (GRA), were used to optimise the output performance. The results of TOPSIS showed that the 16th experimental combination, i.e., the HA heating method, with feed rate = 0.175 mm/rev, depth of cut = 0.1 mm and cutting speed = 150 m/min, required a smaller cutting force and resulted in lower surface roughness. In case of the GRA method, the best output performance was observed for the 15th experimental combination, that is, the HA heating method, with feed rate = 0.15 mm/rev, depth of cut = 0.2 mm and cutting speed = 200 m/min. Compared to the non-heat-assisted turning process, the HA- and UV-assisted processes required 10.25% and 7.69% lesser cutting force, respectively, and the surface roughness in case of the HA method was 15.13% lesser.

Trend analysis and SARIMA forecasting of mean maximum and mean minimum monthly temperature for the state of Kerala, India

Kabbilawsh P., Sathish Kumar D., Chithra N. R.

Acta Geophysica

2020

Vol. 68, no. 4

1161--1174

The development of temperature forecasting models for the state of Kerala using Seasonal Autoregressive Integrated Moving Average (SARIMA) method is presented in this article. Mean maximum and mean minimum monthly temperature data, for a period of 47 years, from seven stations, are studied and applied to develop the model. It is expected that the time-series datasets of temperature to display seasonality (and hence non-stationary), and a possible trend (due to the fact that the data spans 5 decades). Hence, the key step in the development of the models is the determination of the non-stationarity of the temperature time-series, and the transformation of the non-stationary time-series into a stationary time-series. This is carried out using the Seasonal and Trend decomposition using Loess technique and Kwiatkowski–Phillips–Schmidt–Shin test. Before carrying out this process, several preliminary tests are conducted for (1) fnding and flling the missing values, (2) studying the characteristics of the data, and (3) investigating the presence of the trend and seasonality. The non-stationary temperature time-series are transformed to stationary temperature time-series, by one seasonal diferencing and one frstorder diferencing. This information, along with the original time-series, is further utilized to develop the models using the SARIMA method. The parsimonious and best-ft SARIMA models are developed for each of the fourteen variables. The study revealed that SARIMA(2, 1, 1)(1, 1, 1)12 as the ideal forecasting model for eight out of the fourteen time-series datasets.