Looking Ahead By Looking Back

Abstract

To understand the evolution of manufacturing and its future requires multidimensional study of different historical milestones, systems developed over a period of time and some concrete analysis amalgamated with experimental results. This thesis is about identifying major milestones in the manufacturing history and then using this information to understand the evolution of innovation process and innovative models. Further, using knowledge obtained from the study of innovation processes to understand the modern trends in manufacturing industry. Experimental analysis is performed using modern machine learning techniques like deep learning to correctly identify human facial expressions. The increasing utility of artificial intelligence was the driving force to exploit modern machine learning techniques that have proven that now decision power can be carefully delegated or shared with these intelligent systems. The Deep Learning based approach using convolutional neural network is tested on human facial expression recognition and accuracy of over 86% is achieved which is higher than other mathematical based machine learning models. These modern machine learning algorithms are also tested on numerical dataset to prove their flexibility and adaptability for different applications which can be faced in any modern day manufacturing industry. The results from this study show that these modern machine learning algorithms have outperformed old decision making methodologies due to their capacity and intelligence in learning different patterns present in the data and correspondingly helping in correct decision making. As a future recommendation, a hybrid system is proposed which is a combination of predictive as well as corrective maintenance. The proposed system is based on deep learning using convolutional neural network to predict end of life of a part.