Shang Liang Chen

Data fusion neural network for tool condition monitoring in CNC milling machining

Several data fusion methods are addressed in this research to integrate the detected data for the neural network applications of on-line monitoring of the tool condition in CNC milling machining. One dynamometer and one accelerometer were used in the experiments. The collected signals were pre-processed to extract the feature elements for the purpose of effectively monitoring the tool wear condition. Different data fusion methods were adopted to integrate the obtained feature elements before they were applied into the learning procedure of the neural networks. The training-efficiency and test-performance of the data fusion methods were then analyzed. The convergence speed and the test error were recorded and used to represent the training efficiency and test performance of the different data fusion methods. From an analysis of the results of the calculations based on the experimental data, it was found that the performance of the monitoring system could be significantly improved with suitable selection of the data fusion method.

CLB: A novel load balancing architecture and algorithm for cloud services ☆

Abstract Cloud services are widely used in manufacturing, logistics, digital applications, and document processing. Cloud services must be able to handle tens of thousands of concurrent requests and to enable servers to seamlessly provide the amount of load balancing capacity required to respond to incoming application traffic in addition to allowing users to obtain information quickly and accurately. In the past, researchers have proposed the use of static load balancing or server response times to evaluate load balancing capacity, a lack of which may cause a server to load unevenly. In this study, a dynamic annexed balance method is used to solve this problem. Cloud load balancing (CLB) takes into consideration both server processing power and computer loading, thus making it less likely that a server will be unable to handle excessive computational requirements. Finally, two algorithms in CLB are also addressed with experiments to prove the proposed approach is innovative.

A New Approach to Integrate Internet-of-Things and Software-as-a-Service Model for Logistic Systems: A Case Study

Cloud computing is changing the ways software is developed and managed in enterprises, which is changing the way of doing business in that dynamically scalable and virtualized resources are regarded as services over the Internet. Traditional manufacturing systems such as supply chain management (SCM), customer relationship management (CRM), and enterprise resource planning (ERP) are often developed case by case. However, effective collaboration between different systems, platforms, programming languages, and interfaces has been suggested by researchers. In cloud-computing-based systems, distributed resources are encapsulated into cloud services and centrally managed, which allows high automation, flexibility, fast provision, and ease of integration at low cost. The integration between physical resources and cloud services can be improved by combining Internet of things (IoT) technology and Software-as-a-Service (SaaS) technology. This study proposes a new approach for developing cloud-based manufacturing systems based on a four-layer SaaS model. There are three main contributions of this paper: (1) enterprises can develop their own cloud-based logistic management information systems based on the approach proposed in this paper; (2) a case study based on literature reviews with experimental results is proposed to verify that the system performance is remarkable; (3) challenges encountered and feedback collected from T Company in the case study are discussed in this paper for the purpose of enterprise deployment.

TFT-LCD Mura defects automatic inspection system using linear regression diagnostic model

The TFT-LCD panel is one of the most important and promising products in the recent years. Mura defects can be created on the display panel during its production. In this research, a linear regression diagnostic model is incorporated with digital image processing theory to automatically inspect for Mura defects. A bivariate polynomial regression model is used to simulate the brightness of background images that is used in the diagnosis of outliers and influential points. The partitions of the candidate Mura defect regions are segmented using Niblacks threshold criteria. The candidate Mura defects are further evaluated. The quantified level is defined in terms of concepts already reported in the literature. Based on Webers law and a visual perception model, the just-noticeable intensity difference index of the Mura features can be obtained and it can be subsequently used to quantify the Mura defect level. With the obtained defect level, Mura defects can be identified for exactly labelling of the perfect and imperfect LCD panels. Experiments were performed on 13 TFT-LCD panels. There are ten bad panels and three good panels in these 13 samples as determined by human visual inspection. Each bad panel has at least one Mura defect. After the automated inspection process, the results showed that the proposed method could separate the good and bad panels accurately. Compared with human visual inspection, the Mura detection rate of the distinct size and shapes can attain over 90.9 per cent correct detection and the achieved correction rate of Mura defects on each panel can be improved by 100 per cent.

In-process monitoring of the cutting front of CO2 laser cutting with off-axis optical fibre

A new method is presented in this study to test a way that can effectively provide detailed information on the surface morphology during CO2 laser cutting by directly measuring the emitted light from the cutting front. The system consists of a copper tube, glass fibre, polymer fibre and photodiode sensor. In this study, the laser power was modulated from 50 Hz to 300 Hz to disturb the natural burning cycle during gas assisted CO2 laser cutting. The optical fibre was set at 75° to the cutting direction. The wave frequency of the detected signal was very close to the striation frequency of the cut surface, which shows that the sensing system designed and developed in this research can effectively in-process monitor the CO2 laser cutting process.

Tracking control for a synchronized dual parallel linear motor machine tool

This paper analyses and compares several different synchronized controllers and motion command schemes, and implements them using a PC-based gantry-type machine tool. First, an integral-proportional (IP) controller and a proportional controller are applied to the velocity and position loop of a single-axis system respectively. The tracking control accuracy of the motor is advanced by the fastened response and improved lag. Next, for the dual-axis synchronized system, the equation of the cross-coupled controller (CCC) parameters can be acquired by analysing the tracking error and the synchronized error of the dual linear motors. The equation can be further applied to integrate the synchronized motion and reduce the synchronized error. The experiment results have proved these theories to be successful in reducing synchronized error and realizing the performance demand of tracking control.

A Machine Vision Based Automatic Optical Inspection System for Measuring Drilling Quality of Printed Circuit Boards

In this paper, we develop and put into practice an automatic optical inspection (AOI) system based on machine vision to check the holes on a printed circuit board (PCB). We incorporate the hardware and software. For the hardware part, we combine a PC, the three-axis positioning system, a lighting device, and charge-coupled device cameras. For the software part, we utilize image registration, image segmentation, drill numbering, drill contrast, and defect displays to achieve this system. Results indicated that an accuracy of 5

The development of an intelligent monitoring and caution system for pressure ulcer prevention

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Geometric error measurement and compensation of a six-degree-of-freedom hybrid positioning stage

could be achieved in errors of the PCB holes allowing comparisons to be made. This is significant in inspecting the missing, the multi-hole, and the incorrect location of the holes. However, previous work only focuses on one or other feature of the holes. Our research is able to assess multiple features: missing holes, incorrectly located holes, and excessive holes. Equally, our results could be displayed as a bar chart and target plot. This has not been achieved before. These displays help users to analyze the causes of errors and immediately correct the problems. In addition, this AOI system is valuable for checking a large number of holes and finding out the defective ones on a PCB. Meanwhile, we apply a 0.1-mm image resolution, which is better than others used in industry. We set a detecting standard based on 2-mm diameter of circles to diagnose the quality of the holes within 10 s.

Development of a multisensor embedded intelligent home environment monitoring system based on digital signal processor and Wi-Fi

An intelligent remote monitoring and caution system was designed and developed for prevention of pressure ulcer. The developed system uses a ZigBee network infrastructure with pressure sensors to monitor pressured positions for mobility-impaired persons on the bed. The main purpose of this study is intended to increase ulcer prevention awareness, and to facilitate the fulfillment of pressure ulcer management procedures. With the support of the developed system, the incidence of pressure ulcers can be controlled, which will increase both the quality of healthcare and life quality of the patient with impaired mobility who are hospitalized or stay in long-term care facilities.