scholarly journals Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4276
Author(s):  
Kaishu Xia ◽  
Clint Saidy ◽  
Max Kirkpatrick ◽  
Noble Anumbe ◽  
Amit Sheth ◽  
...  
Keyword(s):  
Machine Vision ◽  
Communication Networks ◽  
Domain Knowledge ◽  
Manufacturing Industry ◽  
Industrial Revolution ◽  
Vision System ◽  
Semantic Integration ◽  
Self Awareness ◽  
Process Indicators ◽  
Cognitive Automation

A manufacturing paradigm shift from conventional control pyramids to decentralized, service-oriented, and cyber-physical systems (CPSs) is taking place in today’s 4th industrial revolution. Generally accepted roles and implementation recipes of cyber systems are expected to be standardized in the future of manufacturing industry. The authors intend to develop a novel CPS-enabled control architecture that accommodates: (1) intelligent information systems involving domain knowledge, empirical model, and simulation; (2) fast and secured industrial communication networks; (3) cognitive automation by rapid signal analytics and machine learning (ML) based feature extraction; (4) interoperability between machine and human. Semantic integration of process indicators is fundamental to the success of such implementation. This work proposes an automated semantic integration of data-intensive process signals that is deployable to industrial signal-based control loops. The proposed system rapidly infers manufacturing events from image-based data feeds, and hence triggers process control signals. Two image inference approaches are implemented: cloud-based ML model query and edge-end object shape detection. Depending on use cases and task requirements, these two approaches can be designated with different event detection tasks to provide a comprehensive system self-awareness. Coupled with conventional industrial sensor signals, machine vision system can rapidly understand manufacturing scenes, and feed extracted semantic information to a manufacturing ontology developed by either expert or ML-enabled cyber systems. Moreover, extracted signals are interpreted by Programmable Logical Controllers (PLCs) and field devices for cognitive automation towards fully autonomous industrial systems.

Vision Based Fault Detection of Automated Assembly Equipment

2011 ◽  
Author(s):  
Greg Szkilnyk ◽  
Kevin Hughes ◽  
Brian Surgenor
Keyword(s):  
Machine Vision ◽  
Manufacturing Industry ◽  
Vision System ◽  
Machine Vision System ◽  
Automated Assembly ◽  
Central Controller ◽  
Different Types ◽  
Machine Health ◽  
Future Work ◽  
Overall Effectiveness

Machine faults and breakdowns are a concern for the manufacturing industry. Automated assembly machines typically employ many different types of sensors to monitor machine health and feedback faults to a central controller for review by a technician or engineer. This paper describes progress with a project whose goal is to examine the effectiveness of using machine vision to detect ‘visually cued’ faults in automated assembly equipment. Tests were conducted on a laboratory scale conveyor apparatus that assembles a simple 3-part component. The machine vision system consisted of several conventional webcams and image processing in LabVIEW. Preliminary results demonstrated that the machine vision system could identify faults such as part jams and feeder jams; however the overall effectiveness was limited as this technique can only detect faults known prior to creating the vision system. Future work to create a more robust system is currently underway.

Defect Segmentation of Semiconductor Wafer Image Using k-Means Clustering

2015 ◽  
Vol 815 ◽  
pp. 374-379 ◽  
Author(s):  
N.H. Saad ◽  
N. Mohamad Sabri ◽  
A.F. Hasan ◽  
Azuwa Ali ◽  
Hariyanti Mohd Saleh
Keyword(s):  
Machine Vision ◽  
Manufacturing Industry ◽  
Clustering Algorithm ◽  
Vision System ◽  
Semiconductor Wafer ◽  
Spatial Features ◽  
Two Stages ◽  
Wafer Manufacturing ◽  
Specific Number ◽  
Human Inspection

Nowadays, quality control becomes an important issue in semiconductor manufacturing industry. The rate of production with respect to time gives a lot of issues in the industry. In most semiconductor assemblies, a lot of defects generated from various processes in semiconductor wafer manufacturing need to be inspected manually using human experts and this process required full concentration of the operators. This human inspection procedure, however, is time consuming and highly subjective. In order to overcome this problem, implementation of machine vision will be the best solution. This paper presents defect segmentation of semiconductor wafer image based on colour features with k-Means clustering algorithm which can be adopted in machine vision system. In this work, the segmentation process is carried out in two stages. The first stage comprised of clustering the pixels in the image based on their colour and spatial features. Then the clustered pixels are merged to a specific number of regions. The proposed approach is being evaluated using defected wafer image. The experimental results show that it can be used to segment the defect correctly. By using this method, it is possible to increase the computational efficiency since it will avoid feature extraction for every pixel in the image.

scholarly journals Development of Machine Vision System for Pen Parts Identification under Various Illumination Conditions in an Industry 4.0 Environment

2020 ◽  
Author(s):  
Niksa Mohammadi Bagheri ◽  
Hans Wernher van de Venn ◽  
Peiman Mosaddegh
Keyword(s):  
Machine Vision ◽  
Information And Communication Technologies ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Vision System ◽  
Automatic Identification ◽  
Digital Learning ◽  
Machine Vision System ◽  
Information And Communication ◽  
Environmental Lighting

The fourth Industrial Revolution, well-known as “Industry 4.0”, based on the integration of information and communication technologies, has introduced significant improvements in manufacturing. However, vision systems still experience various impracticalities in dealing with the effect of complex lighting on the systems platform. Therefore, a machine vision system for automatic identification of pen parts under varying lighting conditions at a digital learning factory is proposed. The developed vision system presents a straightforward approach by effectively minimizing the environmental lighting effect on the identification process. First, the obtained information of the designed vision framework is exported to a program, where a reduction of non-uniform illumination is achieved through the implementation of Retinex image enhancement techniques. Then, the color-based Fuzzy C-means (FCM) algorithm, including improved mark watershed segmentation, is employed for pen parts object classification. Finally, the position features of the selected pen part are reported. The process applied to a total number of 210 upper pen parts (caps) and 241 lower pen parts (tubes) images under different lighting scenarios. Results indicate that average parts identification precision for cap and tube parts is different and equals to 98.64% and 95.26%, respectively. The present methodology provides a promising scheme that can be feasibly adapted for other industrial Color-based object recognition applications.

Multifunctional stereoscopic machine vision system with multiple 3D outputs

2020 ◽  
Keyword(s):  
Machine Vision ◽  
International Symposium ◽  
Fast Track ◽  
Vision System ◽  
Machine Vision System ◽  
Stereoscopic Displays ◽  
Electronic Imaging

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Stereoscopic Displays and Applications proceedings.

scholarly journals A real-time machine-vision system for monitoring the textile raising process

2005 ◽  
Vol 56 (8-9) ◽  
pp. 831-842 ◽  
Author(s):  
Monica Carfagni ◽  
Rocco Furferi ◽  
Lapo Governi
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Vision System ◽  
Machine Vision System ◽  
Time Machine

scholarly journals The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Supakorn Harnsoongnoen ◽  
Nuananong Jaroensuk
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Low Cost ◽  
Measuring System ◽  
Coefficient Of Determination ◽  
Water Displacement ◽  
Non Invasive ◽  
Future Potential ◽  
Non Destructive ◽  
Egg Freshness

AbstractThe water displacement and flotation are two of the most accurate and rapid methods for grading and assessing freshness of agricultural products based on density determination. However, these techniques are still not suitable for use in agricultural inspections of products such as eggs that absorb water which can be considered intrusive or destructive and can affect the result of measurements. Here we present a novel proposal for a method of non-destructive, non-invasive, low cost, simple and real—time monitoring of the grading and freshness assessment of eggs based on density detection using machine vision and a weighing sensor. This is the first proposal that divides egg freshness into intervals through density measurements. The machine vision system was developed for the measurement of external physical characteristics (length and breadth) of eggs for evaluating their volume. The weighing system was developed for the measurement of the weight of the egg. Egg weight and volume were used to calculate density for grading and egg freshness assessment. The proposed system could measure the weight, volume and density with an accuracy of 99.88%, 98.26% and 99.02%, respectively. The results showed that the weight and freshness of eggs stored at room temperature decreased with storage time. The relationship between density and percentage of freshness was linear for the all sizes of eggs, the coefficient of determination (R2) of 0.9982, 0.9999, 0.9996, 0.9996 and 0.9994 for classified egg size classified 0, 1, 2, 3 and 4, respectively. This study shows that egg freshness can be determined through density without using water to test for water displacement or egg flotation which has future potential as a measuring system important for the poultry industry.

scholarly journals A Bayesian Control Chart for Monitoring Process Variance

2021 ◽  
Vol 11 (6) ◽  
pp. 2729
Author(s):  
Chien-Hua Lin ◽  
Ming-Che Lu ◽  
Su-Fen Yang ◽  
Ming-Yung Lee
Keyword(s):  
Service Quality ◽  
Control Chart ◽  
Manufacturing Industry ◽  
Industrial Revolution ◽  
Service Industry ◽  
Service Process ◽  
Time Varying ◽  
Exponential Process ◽  
Free Process ◽  
Process Variance

Automation in the service industry is emerging as a new wave of industrial revolution. Standardization and consistency of service quality is an important part of the automation process. The quality control methods widely used in the manufacturing industry can provide service quality measurement and service process monitoring. In particular, the control chart as an online monitoring technique can be used to quickly detect whether a service process is out of control. However, the control of the service process is more difficult than that of the manufacturing process because the variability of the service process comes from widespread and complex factors. First of all, the distribution of the service process is usually non-normal or unknown. Moreover, the skewness of the process distribution can be time-varying, even if the process is in control. In this study, a Bayesian procedure is applied to construct a Phase II exponential weighted moving average (EWMA) control chart for monitoring the variance of a distribution-free process. We explore the sampling properties of the new monitoring statistic, which is suitable for monitoring the time-varying process distribution. The average run lengths (ARLs) of the proposed Bayesian EWMA variance chart are calculated, and they show that the chart performs well. The simulation studies for a normal process, exponential process, and the mixed process of normal and exponential distribution prove that our chart can quickly detect any shift of a process variance. Finally, a numerical example of bank service time is used to illustrate the application of the proposed Bayesian EWMA variance chart and confirm the performance of the process control.

Application of Machine Vision System in the Precision Inspection

2012 ◽  
Vol 546-547 ◽  
pp. 1382-1386
Author(s):  
Yin Xia Liu ◽  
Ping Zhou
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Machine Vision System ◽  
Technical Problems ◽  
Application Program ◽  
Precision Inspection ◽  
Machine Parts

In order to promote the application and development of machine vision, The paper introduces the components of a machine vision system、common lighting technique and machine vision process. And the key technical problems are also briefly discussed in the application. A reference idea for application program of testing the quality of the machine parts is offered.

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