Hong Siang Chua

In the field of Industry 4.0 and smart manufacturing, intelligent Tool Condition Monitoring (TCM) is essential for enabling predictive maintenance and ensuring product quality in machining operations. This research presents the development of a data-driven TCM system capable of acquiring and analyzing machine data for tool life optimization. The study utilizes hybrid data acquisition architecture, integrating internal machine data (Spindle Load) with external sensing technologies, including vibration sensors and a threephase smart energy meter, combined via an (internet of things) IoT gateway and Node-Red platform. Experimental testbeds were conducted on a FANUC ROBODRILL $\boldsymbol{\alpha}$-D21MiB Plus equipped with 5-Axis Rotary Table using SAE 1020 carbon steel to investigate tool wear progression under Conventional and High-Speed Machining (HSM) strategies. Direct offline measurements were performed using a Vision Measuring Machine (VMM) to categorize tool conditions for preexperiment and post-experiment. Three Machine Learning classification models were evaluated: Random Forest, Gradient Boosting, and Support Vector Machine (SVM). The Random Forest classifier achieved the highest reliability with an accuracy of 73.82 %, effectively distinguishing between operational modes and wear states. The poor performance of SVM model (34.70 %) highlighted the high non-linearity and scatter in the dataset.

To keep an eye on the status of high-quality processes for fraction nonconforming, the synthetic double sampling (SDS) np chart is a helpful tool. The SDS np chart is a hybrid between the double sampling (DS) np chart and the conforming run length (CRL) chart. The performance of a control chart is typically judged solely using the average run length (ARL). However, as the shape of the run length (RL) distribution varies with the magnitude of the shift in the process fraction nonconforming, the ARL no longer provides clear interpretation of a chart’s performance. Subsequently, enhanced DS np charts that use median run length (MRL) and expected median run length (EMRL) measures, including SDS np with MRL have recently been proposed for addressing this setback. To broaden the functionality of SDS np, in this work, the unexplored use of EMRL as alternative performance measure is developed by means of Markov chain model. Additionally, in both the zero-state (ZS) and steady-state (SS) modes, the novel optimal designs algorithms are described for computing the optimal charting parameters of the SDS np chart, for both MRL₁ and EMRL₁ minimizations, without any unfavourable feature of bilateral sensitivity. Both the MRL and EMRL performances of SDS np, synthetic np, and DS np charts are compared. Optimal designs charting parameters and sensitivity analyses are provided to aid the practical application of SDS np chart.

Intrinsic U-shaped fiber optic sensors (FOSs) are well established in the field. With greater penetration depth and evanescent power, these sensors exhibit profound sensitivity and have supported a wide range of applications. Several sensing mechanisms have been integrated with U-shaped FOSs for physical, chemical, bio, and refractive index (RI) sensing applications. Sensor performance optimization is based on the principles of the associated sensing mechanisms. This paper is the second part of a review on U-shaped FOSs and discusses the sensing mechanisms, applications, possible improvements, and future scope of the U-shaped FOSs. Besides, this paper aims to provide researchers with a general idea of the actual implementation of U-shaped FOSs based on the theoretical model discussed in Part I of this review.

A wide range of geometrical deformations of fiber optic sensors (FOSs) have been explored to improve their performance. In particular, fiber optics bent into a U-shape present various advantages over other approaches and considerably improved performance in terms of sensitivity. The improvement in sensitivity can be understood as a result of the enhanced penetration depth of the evanescent field and higher evanescent power in the cladding or surrounding medium. Consequently, a larger portion of the propagating light experiences changes due to variations in the local refractive index (RI). In view of their significant benefits, U-shaped FOSs deserve a thorough investigation. This review outlines the theoretical model and design considerations along with the fabrication process of U-shaped FOSs. In addition, this paper aims to provide researchers with guidelines on the factors to consider in designing and optimizing U-shaped FOSs.

Malaysia, Sarawak state in particular produces superior quality black pepper compared to other producing countries. While Sarawak black pepper deserves higher price in the global market, from year 2015 onwards, its price has been plummeting due to global oversupply of pepper. Physical, chemical and biological profiling of Sarawak pepper is conducted to differentiate Sarawak pepper against adulteration and to justify its higher market price. However, the analysis of pepper was performed using time-consuming, non-portable, expensive and invasive laboratory equipment and methodologies. This paper reports an attempt to develop a black pepper geo-tracing system using metal oxide semiconductor (MOS) gas sensors array. The pneumatic system to contain and purge the sample volatile organic compounds (VOCs) is designed, simulated, fabricated and proven to be contain and purge sample VOCs effectively. A total of 200 black pepper samples from Malaysia and India are sampled using non-invasive method for the MOS gas sensors response and the sensors response are analyzed using Principal Component Analysis (PCA). The PCA scatter plots shows that Malaysia and India black pepper can be discriminated based on the MOS gas sensor response towards different VOCs composition in the black pepper samples. The PCA result are then used for geo-tracing of a total of 40 samples (20 from Malaysia; 20 from India), which shows an overall accuracy of 92.5%. The geo-tracing system developed performs rapidly with 260-600 s sampling cycle compared to gas chromatography-mass spectroscopy (GC-MS) equipment which requires at least 30 minutes sampling time, and is portable, cheaper and non-invasive.

Turbidity is the degree to which the optical clarity of water is reduced by impurities in the water. High turbidity values in rivers and lakes promote the growth of pathogen, reduce dissolved oxygen levels and reduce light penetration. The conventional ways of on-site turbidity measurements involve the use of optical sensors similar to those used in commercial turbidimeters. However, these instruments require frequent maintenance due to biological fouling on the sensors. Thus, image processing was proposed as an alternative technique for continuous turbidity measurement to reduce frequency of maintenance. The camera was kept out of water to avoid biofouling while other parts of the system submerged in water can be coated with anti-fouling surface. The setup developed consisting of a webcam, a light source, a microprocessor and a motor used to control the depth of a reference object. The image processing algorithm quantifies the relationship between the number of circles detected on the reference object and the depth of the reference object. By relating the quantified data to turbidity, the setup was able to detect turbidity levels from 20 NTU to 380 NTU with measurement error of 15.7 percent. The repeatability and sensitivity of the turbidity measurement was found to be satisfactory.