Distributed Analytics Framework for Integrating Brownfield Systems to Establish Intelligent Manufacturing Architecture

Intelligent manufacturing otherwise called as smart manufacturing concentrates upon optimising production and processes by making full use of data available. It is regarded as a new manufacturing model where the entire product life cycle can be simplified using various smart sensors, data-driven decision-making models, visualisation, intelligent devices, and data analytics. In the Industry 4.0 era, Industrial Internet of Things (IIoT) architecture platform is required to streamline and secure data transfer between machines, factories, etc. When certain manufacturing industry is equipped with this platform, an intelligent manufacturing model can be achieved. In today’s factories, most machines are brownfield systems and are not connected to any IoT platforms. Thus they cannot provide data or visibility into their performance, health, and optimal maintenance schedules, which would have improved their operational value. This paper attempts to bridge this gap by demonstrating how brownfield equipment can be IIoT enabled and how data analytics can be performed at the edge as well as cloud using two simple use cases involving industrial robot on the abrasive finishing process. The focus of this paper is on how a scalable data analytics architecture can be built for brownfield machines at the edge as well as the cloud

Experimental investigation of high frequency media finishing process

Media finishing processes comprise a group of secondary manufacturing operations. Most media finishing processes involve bulk processing of metallic parts in a recirculating flow of loose, bonded media to modify the surface properties of a work piece through abrasive contact. The amount of material removed during finishing is generally small and typically micrograms per part in the case of polishing and grams for deburring and edge finishing. Industrial interest in optimizing media finishing processes has greatly augmented in recent years. An experimental investigation into the vibratory media finishing process was accomplished by increasing the input frequency imparted on vibratory bowl and decreasing surface roughness (Ra) of an aluminium AA6061-T6 work piece as the dependent variable. Bowl performance can best be portrayed in terms of surface roughness (Ra) with respect to lead time. A video system was used to trace the motion of the finishing media in different frequency and amplitude combinations. Results from videotaped observation revealed that increasing the frequency in the range of 50 Hz to 100 Hz increased the velocity of the media particles. The effect of frequency on the surface evolution and process time was studied with scanning electron microscopic images gathered at different time intervals at different frequencies. Surface measurements were made using Taly-scan profilometer to determine the level of surface roughness (Ra). On the basis of these experimental results increasing the frequency offers the potential for shorter processing time to reach surface roughness (Ra) of 0.4 µm.Master of Science (Power Engineering

Modelling and in-process monitoring of abrasive belt grinding process

Automation and self-monitoring implementation of manufacturing processes will support the development of interoperable ecosystem relevant to the Industry 4.0 concept. Among many industrial cases, abrasive belt grinding is a tertiary machining process used to achieve desired surface quality and to machine off features such as burrs and weld seams. Manufacturers are in the need of an ability to closely monitor and optimise the performance of abrasive belt grinding processes to meet tight tolerances. The abrasive belt grinding process is highly nonlinear due to the complexity of the underlying physical mechanisms, some of which remain unknown. Existing research in the literature on in-situ tool wear prediction were primarily focused on hard tools, but limited effort can be found on that of compliant belt tools. Although many advanced machining cells are equipped with belt grinder and robotic manipulators for surface finishing, industries still rely on skilled operators to manually remove weld seams using belt sanders. Self monitoring of such a dynamic process in industrial robot cell environment is essential in having a fully automated system. This research aims to model the robotic abrasive belt grinding process in dry conditions appropriate for monitoring purposes. The first part of this thesis discusses the influence of the process parameters on material removal and surface quality in abrasive belt grinding process. Interpretation of Taguchi's Design of Experiments (DoE) experimental results revealed that abrasive grain distribution on backing material has significant influence on material removal and surface quality. Subsequently, a systematic approach to mathematically model the belt grinding process using regression techniques based on soft computing is presented. The second part of the thesis deals with real-time monitoring of the belt grinding tool life. Predicting belt tool life helps to determine whether it is under-utilised, overused or it is due for replacement. Unlike other rigid abrasive machining tools, in abrasive belts the grains are not regenerated. The influence of grain wear on material removal mechanisms namely cutting, ploughing and rubbing were investigated with single grit scratch tests and Acoustic Emission (AE) sensor reading analysis. Having understood the effect of abrasive grain wear on belt grinding performance, a methodology to virtually monitor the coated abrasive belt tool life in real time with the help of physical sensors and machine learning classifiers is developed. In the last part of this thesis, an automated weld seam removal method is proposed. The method offers a real time endpoint verification system for weld seam removal using accelerometer, force and vision-based sensors along with machine learning and deep learning algorithms. Expectedly, this will reduce unnecessary costs and also increase the safety level of operators. In general, the proposed modelling and virtual metrology techniques will add values to the entire manufacturing process, in particular to those involving abrasive belt grinding, and will comply to Industry 4.0 objectives.Doctor of Philosoph

Use of Acoustic Emissions to detect change in contact mechanisms caused by tool wear in abrasive belt grinding process

Abrasive belt tools are widely used for finishing processes, where the abrasive grains on the belt tool serve as the cutting edge to remove materials. The interaction between abrasive grain and the material surface might result in three contact mechanisms, i.e. rubbing, ploughing and cutting, where their nature are not fully understood. On the other hand, the performance of a coated abrasive belt tool is highly affected by the grain wear. A single grain scratch test with different abrasive grain wear conditions is conducted to explore the three contact mechanisms. Through scratch experiments of prismatic Aluminium Oxide (A12O3) grain on Aluminium 6061 workpiece, Acoustic Emission (AE) frequency signatures that correspond to the three mechanisms are examined. Dominant frequencies and energy signatures occupied by the three contact mechanisms are analysed using Short-Time Fourier Transform (STFT). The energy content of the dominant frequency signatures revealed that the cutting mechanism is more predominant on belt tool with new grains, which gradually becomes less significant as the grain wears. A similar trend is also observed in ploughing and rubbing modes with respect to the wear flat level of the belt tool. The general conclusion suggests that the intensity of contact mechanisms changes according to the condition of the abrasive grain, i.e. tool wear, and can be correlated with AE sensor data.NRF (Natl Research Foundation, S’pore)Accepted versio

In-process endpoint detection of weld seam removal in robotic abrasive belt grinding process

This paper proposes a novel approach for inprocess endpoint detection of weld seam removal during robotic abrasive belt grinding process using discrete wavelet transform (DWT) and support vector machine (SVM). A virtual sensing system is developed consisting of a force sensor, accelerometer sensor and machine learning algorithm. This work also presents the trend of the sensor signature at each stage of weld seam evolution during its removal process. The wavelet decomposition coefficient is used to represent all possible types of transients in vibration and force signals generated during grinding over weld seam. “Daubechies-4” wavelet function was used to extract features from the sensors. An experimental investigation using three different weld profile conditions resulting from the weld seam removal process using abrasive belt grinding was identified. The SVM-based classifier was employed to predict the weld state. The results demonstrate that the developed diagnostic methodology can reliably predict endpoint at which weld seam is removed in real time during compliant abrasive belt grinding.NRF (Natl Research Foundation, S’pore)Accepted versio

High frequency and amplitude effects in vibratory media finishing

The vibratory media finishing process is known for its long process time and there is an industrial need to speed up this process. Increasing frequency and amplitude of vibration, beyond the current process window commonly used, is an option to reduce process time. Using a laboratory scale electro-magnetic shaker setup, the effects of increasing frequency and amplitude of vibration is investigated. By monitoring the surface roughness with processing time it is shown that, while for a given amplitude frequency has a strong effect, amplitude in general has a stronger effect in quickening the time to saturation. Using high-speed camera measurements in a transparent bowl it is also shown that the average media speed increases with increase in frequency and this can partially explain the resulting shorter process time.NRF (Natl Research Foundation, S’pore)Published versio

Modelling of Material Removal in Abrasive Belt Grinding Process: A Regression Approach

status: publishe

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning

Metal-based Laser Powder Bed Fusion (LPBF) has made fabricating intricate components easier. Yet, assessing part quality is inefficient, relying on costly Computed Tomography (CT) scans or time-consuming destructive tests. Also, intermittent inspection of layers also hampers machine productivity. The Additive Manufacturing (AM) field explores real-time quality monitoring using sensor signatures and Machine Learning (ML) to tackle this. One such approach is sensing airborne Acoustic Emissions (AE) from process zone perturbations and comprehending flaw formation for monitoring the LPBF process. This study emphasizes the importance of selecting airborne AE sensors for accurately classifying LPBF dynamics in 316 L, utilizing a flat response sensor to capture AE's during three regimes: Lack of Fusion, conduction mode, and keyhole. To comprehensively understand AE from a broad process space, the data was collected for two different 316 L stainless steel powder distributions (> 45 µm and < 45 µm) using two different parameter sets. Frequency analysis unveiled distinct LPBF dynamics as dominant and correlated in specific frequency ranges. Empirical Mode Decomposition was used to examine the periodicity of AE signals by separating them into constituent signals for comparison. Transformed AE signals were trained to distinguish regimes using ML classifiers (Convolutional Neural Networks, eXtreme Gradient Boosting, and Support Vector Machines). Sensitivity analysis using saliency maps and feature importance scores identified frequency information below 40 kHz relevant for decision-making. This study highlights interpretable machine learning's potential to identify critical frequency ranges for distinguishing LPBF regimes and underscores the importance of sensor selection for enhanced process monitoring.ISSN:0924-013

In-process surface roughness estimation model for compliant abrasive belt machining process

Surface roughness inspection in robotic abrasive belt machining process is an off-line operation which is time-consuming. An in-process multi-sensor integration technique comprising of force, accelerometer and acoustic emission sensor was developed to predict state of the surface roughness during machining. Time and frequency-domain features extracted from sensor signals were correlated with the corresponding surface roughness to train the Support vector machines (SVM's) in Matlab toolbox and a classification model was developed. Prediction accuracy of the classification model shows proposed in-process surface roughness recognition system can be integrated with abrasive belt machining process for capping lead-time and is reliable.Published versio

Modelling of material removal in abrasive belt grinding process : a regression approach

This article explores the effects of parameters such as cutting speed, force, polymer wheel hardness, feed, and grit size in the abrasive belt grinding process to model material removal. The process has high uncertainty during the interaction between the abrasives and the underneath surface, therefore the theoretical material removal models developed in belt grinding involve assumptions. A conclusive material removal model can be developed in such a dynamic process involving multiple parameters using statistical regression techniques. Six different regression modelling methodologies, namely multiple linear regression, stepwise regression, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and random forests (RF) have been applied to the experimental data determined using the Taguchi design of experiments (DoE). The results obtained by the six models have been assessed and compared. All five models, except multiple linear regression, demonstrated a relatively low prediction error. Regarding the influence of the examined belt grinding parameters on the material removal, inference from some statistical models shows that the grit size has the most substantial effect. The proposed regression models can likely be applied for achieving desired material removal by defining process parameter levels without the need to conduct physical belt grinding experiments.National Research Foundation (NRF)Published versionThis work was conducted within the Rolls-Royce@NTU Corporate Lab with support from the National Research Foundation (NRF) Singapore under the Corp Lab@University Scheme