Estimation of design effort of jigs and fixtures used in aviation industry by machine learning

Especially in large-scale design projects, the correct estimation of the design effort (time) is an important factor in creating a project plan. These estimates, based only on expert opinion, can cause great damage to the project plan when made incorrectly or insufficiently. Based on the specified problem, it is aimed to develop a machine learning model that can predict the tool design effort in the aviation industry. The design requirement input of each tool that does not contain numerical values was obtained from the cooperate database with suitable data queries. In order to deflate the relevant data into numerical, the most efficient encoding method was selected by performing various experiments on the input data. Experiments have been carried out on commonly used decision tree, Support Vector Machine, Linear Regression and Artificial Neural Network machine learning methods to predict tool design effort from encoded input data. In this study; it is aimed to determine the ideal machine learning model for the best estimation of tool design effort and to create the most appropriate input and parameter set. KEYWORDS: tool design, machine learning, design effort estimation, aviation and defense