Adaptation to Industry 4.0 Using Machine Learning and Cloud Computing to Improve the Conventional Method of Deburring in Aerospace Manufacturing Industry

The deburring process in aerospace industry involves significant amount of manual work such as surface roughness measurement and quality verification. This manual works hinder the implementation of industry 4.0. This paper discusses on the implementation of machine learning and cloud computing to improve the conventional deburring process in aerospace manufacturing industry to be ready for industry 4.0 upgrade. The paper starts with the introduction of deburring, machine learning, and cloud computing in relevant to aerospace industry. The paper then discusses on the analytical approach of determining chamfer length of deburring through machine learning analysis from sensors data collected in the deburring process. Machine learning is one example of analysis tools to replace the manual work which often involves subjective judgement, with data-based judgement. The paper also shows the offline machine learning result and the advantages that can be brought by online machine learning implementation. After all this, the paper details on the advantages of implementing machine learning into the deburring process for aerospace industry. Moreover, the effort to scale up the deburring process using cloud services for long sustainability will be discussed. At the end of the paper, the readers can understand the implementation of machine learning and cloud computing on deburring process in aerospace industry.