An Internet-Based Quality Control Laboratory via Integration of Remote Robotic Operation and Nondestructive Ultrasound Evaluation

2008 ◽  
Author(s):  
Richard Chiou ◽  
Vladimir Genis ◽  
Warren Rosen ◽  
Anthony Moulton ◽  
Yongjin Kwon
Keyword(s):  
Internet Access ◽  
Flaw Detector ◽  
Inspection System ◽  
Quality Inspection ◽  
Industrial Companies ◽  
Quality Control Laboratory ◽  
Ultrasound Evaluation ◽  
Increase Productivity ◽  
The One ◽  
Ethernet Connection

This paper discusses the integration of a remote robot laboratory with nondestructive ultrasound evaluation (NDE) experiments. A remotely automated quality inspection system is designed to analyze dimensions as well as detect internal flaws of parts via an Internet-based NDE system. The remote quality inspection system includes: Internet controllable robot via Ethernet connection, multiple Web-cameras, Ultrasonic Automatic Flaw Detector, LabVIEW module, and computers with Internet access capable of remote connection. The uniqueness of the project lies in making this process Internet-based and remote robot operated. An Internet-based procedure such as the one we are developing will allow industrial companies involved in NDE procedures to increase productivity and profits by allowing an employee to monitor multiple operations over the Internet without having to be at a specified location. In addition, the utilization of remotely controlled robots for educational purposes is expected to increase the degree of immersive presence of the students engaging in such Internet-based laboratory exercises as well as the level of online interactivity between the faculty and students.

scholarly journals Product Inspection Methodology via Deep Learning: An Overview

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5039
Author(s):  
Tae-Hyun Kim ◽  
Hye-Rin Kim ◽  
Yeong-Jun Cho
Keyword(s):  
Deep Learning ◽  
Product Quality ◽  
Inspection System ◽  
Quality Inspection ◽  
Learning Models ◽  
Unified Framework ◽  
Learning Techniques ◽  
Inspection Systems ◽  
System Maintenance ◽  
Test Scenarios

In this study, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. In addition, we explain the steps for building a deep-learning-based inspection system in detail. Second, we address connection schemes that efficiently link deep learning models to product inspection systems. Finally, we propose an effective method that can maintain and enhance a product inspection system according to improvement goals of the existing product inspection systems. The proposed system is observed to possess good system maintenance and stability owing to the proposed methods. All the proposed methods are integrated into a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compare and analyze the performance of the methods in various test scenarios. We expect that our study will provide useful guidelines to readers who desire to implement deep-learning-based systems for product inspection.

scholarly journals Implementation and evaluation of an echo state network for a quality inspection system for laser welding

Procedia CIRP ◽  
2021 ◽  
Vol 99 ◽  
pp. 496-501
Author(s):  
Ivan Vishev ◽  
Claus-Philipp Feuring ◽  
Oliver Bringmann
Keyword(s):  
Laser Welding ◽  
Inspection System ◽  
Echo State Network ◽  
Quality Inspection

scholarly journals Multi-Layer Quality Inspection System Framework for Industry 4.0

2021 ◽  
Vol 15 (5) ◽  
pp. 641-650
Author(s):  
Victor Azamfirei ◽  
◽  
Anna Granlund ◽  
Yvonne Lagrosen
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Production Control ◽  
Measurement Instrument ◽  
Future Research ◽  
Inspection System ◽  
Work Piece ◽  
Quality Inspection ◽  
Industrial Case Study ◽  
Key Factor

In the era of market globalisation, the quality of products has become a key factor for success in the manufacturing industry. The growing demand for customised products requires a corresponding adjustment of processes, leading to frequent and necessary changes in production control. Quality inspection has been historically used by the manufacturing industry to detect defects before customer delivery of the end product. However, traditional quality methods, such as quality inspection, suffer from large limitations in highly customised small batch production. Frameworks for quality inspection have been proposed in the current literature. Nevertheless, full exploitation of the Industry 4.0 context for quality inspection purpose remains an open field. Vice-versa, for quality inspection to be suitable for Industry 4.0, it needs to become fast, accurate, reliable, flexible, and holistic. This paper addresses these challenges by developing a multi-layer quality inspection framework built on previous research on quality inspection in the realm of Industry 4.0. In the proposed framework, the quality inspection system consists of (a) the work-piece to be inspected, (b) the measurement instrument, (c) the actuator that manipulates the measurement instrument and possibly the work-piece, (d) an intelligent control system, and (e) a cloud-connected database to the previous resources; that interact with each other in five different layers, i.e., resources, actions, and data in both the cyber and physical world. The framework is built on the assumption that data (used and collected) need to be validated, holistic and on-line, i.e., when needed, for the system to effectively decide upon conformity to surpass the presented challenges. Future research will focus on implementing and validating the proposed framework in an industrial case study.

scholarly journals A High Precision Quality Inspection System for Steel Bars Based on Machine Vision

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2732 ◽  
Author(s):  
Xinman Zhang ◽  
Jiayu Zhang ◽  
Mei Ma ◽  
Zhiqi Chen ◽  
Shuangling Yue ◽  
...  
Keyword(s):  
Machine Vision ◽  
High Precision ◽  
Absolute Error ◽  
Detection Methods ◽  
Inspection System ◽  
Quality Inspection ◽  
Construction Site ◽  
High Definition ◽  
Steel Bar ◽  
Steel Bars

Steel bars play an important role in modern construction projects and their quality enormously affects the safety of buildings. It is urgent to detect whether steel bars meet the specifications or not. However, the existing manual detection methods are costly, slow and offer poor precision. In order to solve these problems, a high precision quality inspection system for steel bars based on machine vision is developed. We propose two algorithms: the sub-pixel boundary location method (SPBLM) and fast stitch method (FSM). A total of five sensors, including a CMOS, a level sensor, a proximity switch, a voltage sensor, and a current sensor have been used to detect the device conditions and capture image or video. The device could capture abundant and high-definition images and video taken by a uniform and stable smartphone at the construction site. Then data could be processed in real-time on a smartphone. Furthermore, the detection results, including steel bar diameter, spacing, and quantity would be given by a practical APP. The system has a rather high accuracy (as low as 0.04 mm (absolute error) and 0.002% (relative error) of calculating diameter and spacing; zero error in counting numbers of steel bars) when doing inspection tasks, and three parameters can be detected at the same time. None of these features are available in existing systems and the device and method can be widely used to steel bar quality inspection at the construction site.

scholarly journals Inclusion of Workers with Disabilities in Production 4.0: Legal Foundations in Europe and Potentials Through Worker Assistance Systems

2019 ◽  
Vol 11 (21) ◽  
pp. 5978 ◽  
Author(s):  
Benedikt G. Mark ◽  
Sarah Hofmayer ◽  
Erwin Rauch ◽  
Dominik T. Matt
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Sector ◽  
People With Disabilities ◽  
Critical Discussion ◽  
The Other ◽  
Industrial Companies ◽  
Employees With Disabilities ◽  
Legal Foundations ◽  
Assistance Systems ◽  
The One

The inclusion of employees with disabilities in production is an issue that has rarely been addressed by scientists from the manufacturing sector. In this article, we examine to what extent the trend towards Industry 4.0 offers potential for the inclusion of people with disabilities in Production 4.0. First, we examine relevant legal foundations and restrictions in Europe and in more detail in Austria, Italy, and Norway. Next, based on a literature review, we examine which technological aids in the form of worker assistance systems derived from Industry 4.0 can make jobs in the manufacturing sector accessible for people with disabilities. Three types of assistance systems have been examined: sensorial aid systems, physical aid systems, and cognitive aid systems. In a concluding discussion of the results, we finally summarize the implications on management and policies as well as the potential and limitations of identified worker assistance technologies. On the one hand, the study is intended to draw the attention of researchers and industrial companies to new technological possibilities for the inclusion of people with disabilities in production. On the other hand, difficulties and grievances due to the legal foundations are pointed out to stimulate a critical discussion here as well.

Sign in / Sign up

Export Citation Format

Share Document