Almon Chai

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.

The constitutive equation model for a particular aluminum thin film deposited using Physical Vapor Deposition (PVD) process used in wafer fabrication Back End of Line (BEOL) has been determined by matching the data from experimental nanoindentation and Finite Element Analysis (FEA) simulation. The model used is a derivative of the Voce isotropic hardening model which is defined by three coefficients. The Linear Hardening Coefficient, Saturation Flow Stress and Saturation Exponent has been determined to be 21937 MPa, 135 MPa and 25 respectively.

Text recognition plays an important role in recognizing texts presented in the images as they provide important information. Scene text recognition has been an active research topic with rapid growth of development to improve the performance of text recognition with better reliability and accuracy. However, scene text recognition is challenging due to images containing inconsistent lighting, low resolution and blurriness. In addition, scene texts are usually taken from outdoor signboards, signage and road signs, which contain various orientation and fancy font styles to attract attention. Various researchers have proposed methods for recognizing different orientations of scene texts, such as horizontal texts, curved texts and rotated texts. However, to data there is a lack of research in recognizing vertical texts in natural scene images. In this research, a model for effective automatic recognition of vertical texts in natural scene images has been proposed, consisting of two major processes which are text localization and segmentation and text recognition. This proposed model recognizes three different types of vertical scene texts, which are top-to-bottom vertical texts, bottom-to-top vertical texts and horizontal-stacked vertical texts.

Introduction Orientation and mobility (O&M) specialists assess the functional vision and O&M skills of people with mobility problems, usually relating to low vision or blindness. There are numerous O&M assessment checklists but no measures that reduce qualitative assessment data to a single comparable score suitable for assessing any O&M client, of any age or ability, in any location. Functional measures are needed internationally to align O&M assessment practices, guide referrals, profile O&M clients, plan appropriate services and evaluate outcomes from O&M programmes (eg, long cane training), assistive technology (eg, hazard sensors) and medical interventions (eg, retinal implants). This study aims to validate two new measures of functional performance vision-related outcomes in orientation and mobility (VROOM) and orientation and mobility outcomes (OMO) in the context of ordinary O&M assessments in Australia, with cultural comparisons in Malaysia, also developing phone apps and online training to streamline professional assessment practices. Methods and analysis This multiphase observational study will employ embedded mixed methods with a qualitative/quantitative priority: corating functional vision and O&M during social inquiry. Australian O&M agencies (n=15) provide the sampling frame. O&M specialists will use quota sampling to generate cross-sectional assessment data (n=400) before investigating selected cohorts in outcome studies. Cultural relevance of the VROOM and OMO tools will be investigated in Malaysia, where the tools will inform the design of assistive devices and evaluate prototypes. Exploratory and confirmatory factor analysis, Rasch modelling, cluster analysis and analysis of variance will be undertaken along with descriptive analysis of measurement data. Qualitative findings will be used to interpret VROOM and OMO scores, filter statistically significant results, warrant their generalisability and identify additional relevant constructs that could also be measured. Ethics and dissemination Ethical approval has been granted by the Human Research Ethics Committee at Swinburne University (SHR Project 2016/316). Dissemination of results will be via agency reports, journal articles and conference presentations.

Falling sphere viscometer is one of the most commonly used techniques in experimentally determining fluid viscosity. This method requires the measurement of the terminal velocity of a falling sphere inside the fluid, which remains a challenging and costly task, especially if high precision is required. This paper presents the development of a low-cost visionbased falling sphere viscometer. The velocity measurement is performed by incorporating two linear image sensors which allow faster frame rate. A specific object detection algorithm based on background subtraction is developed to analyze the image data in real-time. The experimental setup and implementation are also presented.

The simulation of a miniature conceptual seven degree-of-freedom (DOF) robotic slave arm that is able to imitate the seven basic motions of a human arm is performed. The studies on the operating envelope are presented in this paper. The miniature robotic slave arm, a part of the master-slave arm system, is modeled in Solidworks to study the motion of the arm during operation. Simulation is done in Solidworks to justify that the robotic arm designed is able to perform the seven basic motions of a human arm to the intended range of motion. Simulation is also performed for different combination of joint motions to study the maximum reachable distance, the safe operation envelope, of the robotic arm for the X, Y and Z-axis.

The conceptual design and proposed control methodology for a master-slave system that consists of an upper limb exoskeleton that acquires motion data to predict the motion of the user to control a robotic arm that mimics the motion of the user is presented. The exoskeleton master-slave unit in its conceptual design is also shown, with proposed electromyography (EMG) signals from sixteen muscles that are related to the seven basic motions of the human arm and accelerometers attached on the exoskeleton to predict motion of the user. The proposed control methodology for the master-slave system consists of four stages: data acquisition, data processing, data analysis and motion controls. This concept is developed to support motion prediction to aid rehabilitation and power assist.

This paper describes the work performed on a small-scale dryer designed to allow agricultural product to be dried by farmers via usage of slanted plates for grains transportation. This work involves the prototype design, computational simulation using Comsol Multiphysics and study of the results. The computational simulation results are plotted in temperature contour-plots based on the prototype modelling and boundary conditions. The results illustrate the difference in opening and closing of grains inlet and also effects of the temperature distribution within the dryer. Air-flow relative humidity is also introduced to allow observation of the drying air-flow on the grains. The simulation results are shown and elaborated here. From the results, it is evident the grains can be dried as the injected heated air-flow is projected onto the plates. It can also be concluded here that farmers may need to close the grains inlet of the dryer to prevent heat loss and non-uniform distribution of heat.