Application of Time Series for Casting Using Machine Learn Method

Abstract

Time series forecasting plays a vital role in optimizing processes through many industries, and the casting for the domain is no exception. Casting processes suddenly require exact predictions to shine product quality, low waste, and optimize resource allocations. This paper discovers the application of machine learning methods for time series forecasting. It starts with an exact review of existing research, enjoying the critical role of correct forecasting in this field. The research leverages device learning techniques, adding autoregressive integrated walking average (ARIMA), Long Short-Term Memory (LSTM) networks and XGBoost, to model & guess time series data related to casting processes. A different and unique dataset is collected, again processed and used for experimentation. The results shows the  ability and  control of these machine learning methods in forecasting many possibilities of casting processes. Through a serious talk of the results and their real implications, these paper implies to the increasing body of memory in the space of time series forecasting for casting. The discovery offer insights into a feasibility of using machine learning to shows casting operations, improve product quality and optimize resource use. This research no only shows the potential of machine learning in the casting field but also shows as a foundation for further Discovery and application of advanced forecasting techniques in this domain.