Manufacturing process modeling with natural language processing

Natural language processing is a sub-science of artificial intelligence and linguistics and it is defined as the analysis of texts in natural languages, sound waves and their transfer to the computer environment. In natural language processing, the inputs can be text or sound. Text processing, which is an application area of natural language processing applied in many fields, is the process of getting ready for processing for analysis by classifying or clustering the words of the text for the desired purpose. In this study, a program that will enable the modeling of natural language processing and production processes was written using Python 3.7. In the text given as input, the program performs a preprocessing on the text with text processing techniques. It then determines which type of production is cell type, u type, workshop type or mass production type, and accordingly locates existing machines and models the process. Then the modeled process can be saved/printed or the simulation process can be started over the modeled process. In the simulation section, by entering the distribution type and parameter values of the process times, the output amounts of the production and the usage rates of the machines are reached. If the distribution type of the data is unknown, the distribution type of data can be determined using Chi-square and Kolmogorov-Smirnov tests. The study aims to provide an initial basis for a program that will provide all the results with only audio expression without the need for any other program for modeling and simulation of production processes.