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Manufacturing of aerospace components consists of combination of different types of machining, finishing, and measuring processes. One of the finishing processes is deburring, i.e. a finishing process to remove burrs from work coupons after a boring hole process. Deburring is conducted to achieve required surface finish quality prior to further processes in assembly line. This paper introduces sensor data analysis as a tool to quantify and correlate the deburring stage with the features extracted from sensors data. This study covers signal processing, feature extraction and analytical method to determine its relevancy to the surface finish quality from deburring process. Wavelet decomposition and Welch’s spectrum estimate is used as a signal processing and feature extraction method. Consequently, the features are used as the basis for analysis by adaptive neuro-fuzzy inference system (ANFIS). The ANFIS yields the output corresponding to the predicted surface finish quality in terms of boss hole chamfer length and the stage classification of deburring process. The results show a decreasing trend in measured vibration signal, which is qualitatively well correlated to the deburring stage and the development of chamfer length during deburring process.

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.

The industrial revolution has reached a new development cycle which uses the advantages of cyber world into the physical world of manufacturing, and called Industry 4.0. Cyber world offers Internet of Things which allows transmission and reception of data from and to anywhere on earth through Internet. In the current aerospace industry, the production of components has significant manual work involved. Meanwhile, the demand in quantity and quality of these components are increasing. In order to enhance manufacturing capability and solve this, this paper introduced a universal platform for real time sensors monitoring and analysis for Industry 4.0 implementation in aerospace industry. Our objective was to develop a universal platform that is reliable to be used in various analytical applications and investigate the supporting factors that has to be considered in applying such platform. To achieve this objective, the paper used easily available hardware which consists of ABB Robot, Wi-Fi Data Acquisition Systems (Wi-Fi DAQs), FPGA equipped CompactRIO, Wireless Access Point (WAP), sensors, and monitor. The system mentioned is made to trial for aerospace component manufacturing processes. The data acquired from the sensors is collected and transmitted by the Wi-Fi DAQs to the WAP. Consequently, the CompactRIO will collect the sensors data from the WAP and analyze them. The result will then be sent to the internet and also used to trigger necessary action for the ABB Robot based on predetermined decision making tables or resources. Ultimately, this application enables aerospace component manufacturers to enhance their capability to increase the product quality and manufacturing process safety while also reduce the manufacturing time required.

The number of studies on the Internet of Things (IoT) has grown significantly in the past decade and has been applied in various fields. The IoT term sounds like it is specifically for computer science but it has actually been widely applied in the engineering field, especially in industrial applications, e.g., manufacturing processes. The number of published papers in the IoT has also increased significantly, addressing various applications. A particular application of the IoT in these industries has brought in a new term, the so-called Industrial IoT (IIoT). This paper concisely reviews the IoT from the perspective of industrial applications, in particular, the major pillars in order to build an IoT application, i.e., architectural and cloud computing. This enabled readers to understand the concept of the IIoT and to identify the starting point. A case study of the Amazon Web Services Machine Learning (AML) platform for the chamfer length prediction of deburring processes is presented. An experimental setup of the deburring process and steps that must be taken to apply AML practically are also presented. NRF (Natl Research Foundation, S’pore) Published version

Measurement of surface roughness of any machining process is crucial for obtaining a component or part of the correct size and surface finish in the first instance, in order to minimize the manufacturing cost. In-process monitoring of machining processes based on an estimation of the surface roughness using the cutting parameters is inaccurate. In this investigation, a fuzzy inference system based on an intelligent sensor fusion model has been developed for the purpose of in-process indirect measurement of surface roughness for a machining process. In the proposed technique, measurement of the Speed Force component, Radial Force component, Feed Force component, Vibration, and Acoustic Emission sensor inputs from a turning process have been considered as the inputs. The results have been compared with the surface roughness estimated with a second order regression model using cutting parameters as inputs. The proposed method has shown considerable improvement in the surface roughness estimation in a simulation environment.