The aim of the project is to predict the CNC machining time for steels in Beyçelik-Gestamp Automotive Company. Best regression supervised algorithms such as Neural Network and Gradient Boosting are used to predict the output. The data taken from the company which includes information such as machine type, weight of the steel, amount of chip removed, the number of holes and the number of grooving in the steel. After the data is cleaned, using python, the algorithms are run. One main finding is that Linear Regression is the best algorithm to predict the machine time for steel. As a result, a new data set was compared between the actual hours taken to process the steel and the estimated hours, and the difference was noted to be less than 3%.
The aim of the project is to predict the CNC machining time for steels in Beyçelik-Gestamp Automotive Company. Best regression supervised algorithms such as Neural Network and Gradient Boosting are used to predict the output. The data taken from the company which includes information such as machine type, weight of the steel, amount of chip removed, the number of holes and the number of grooving in the steel. After the data is cleaned, using python, the algorithms are run. One main finding is that Linear Regression is the best algorithm to predict the machine time for steel. As a result, a new data set was compared between the actual hours taken to process the steel and the estimated hours, and the difference was noted to be less than 3%.
The project report is below. CNC MACHINING TIME ESTIMATION.pdf