scholarly journals Quality Control in Automated Manufacturing Processes – Combined Features for Image Processing

10.14311/868 ◽  
2006 ◽  
Vol 46 (5) ◽  
Author(s):  
B. Kuhlenkötter ◽  
X. Zhang ◽  
C. Krewet
Keyword(s):  
Image Processing ◽  
Quality Control ◽  
Support Vector ◽  
Manufacturing Processes ◽  
Automated Manufacturing ◽  
Defect Classification ◽  
Production Processes ◽  
Combined Features ◽  
Important Challenge ◽  
Selection Of

In production processes the use of image processing systems is widespread. Hardware solutions and cameras respectively are available for nearly every application. One important challenge of image processing systems is the development and selection of appropriate algorithms and software solutions in order to realise ambitious quality control for production processes. This article characterises the development of innovative software by combining features for an automatic defect classification on product surfaces. The artificial intelligent method Support Vector Machine (SVM) is used to execute the classification task according to the combined features. This software is one crucial element for the automation of a manually operated production process. 

scholarly journals Pre-trained classification of scalp conditions using image processing

2020 ◽  
Vol 20 (1) ◽  
pp. 138
Author(s):  
Shafaf Ibrahim ◽  
Zarith Azuren Noor Azmy ◽  
Nur Nabilah Abu Mangshor ◽  
Nurbaity Sabri ◽  
Ahmad Firdaus Ahmad Fadzil ◽  
...  
Keyword(s):  
Image Processing ◽  
Region Of Interest ◽  
Conventional Technique ◽  
Input Image ◽  
Support Vector ◽  
Suitable Treatment ◽  
New Treatment ◽  
Processing Techniques ◽  
Selection Of

<span>Scalp problems may occur due to the miscellaneous factor, which includes genetics, stress, abuse and hair products. The conventional technique for scalp and hair treatment involves high operational cost and complicated diagnosis. Besides, it is becoming progressively important for the payer to investigate the value of new treatment selection in the management of a specific scalp problem. As they are generally expensive and inconvenient, there is an increasing need for an affordable and convenient way of monitoring scalp conditions. Thus, this paper presents a study of pre-trained classification of scalp conditions using image processing techniques. Initially, the scalp image went through the pre-processing such as image enhancement and greyscale conversion. Next, three features of color, texture, and shape were extracted from each input image, and stored in a Region of Interest (ROI) table. The knowledge of the values of the pre-trained features is used as a reference in the classification process subsequently. A technique of Support Vector Machine (SVM) is employed to classify the three types of scalp conditions which are alopecia areata (AA), dandruff and normal. A total of 120 images of the scalp conditions were tested. The classification of scalp conditions indicated a good performance of 85% accuracy. It is expected that the outcome of this study may automatically classify the scalp condition, and may assist the user on a selection of suitable treatment available.</span>

scholarly journals Application of Algorithm of Discipline D2 of G8D Method in Solving Selected Problems of Quality Control Management

2019 ◽  
Vol 1 (1) ◽  
pp. 599-606
Author(s):  
Joanna Cyganiuk ◽  
Adam Idzikowski ◽  
Piotr Kuryło ◽  
Andrzej Tomporowski ◽  
Weronika Kruszelnicka
Keyword(s):  
Quality Control ◽  
Problem Solving ◽  
Quality Management ◽  
Manufacturing Processes ◽  
Universal Method ◽  
Management Tools ◽  
Projection Welding ◽  
Production Processes ◽  
Problem Description ◽  
Control Management

AbstractThe G8D method is a universal method for solving problems arising in production processes, also used in optimisation of these processes. The method allows the detection and elimination of any drawbacks occurring in manufacturing processes and ensuring the safety of these processes.In the article, the authors have presented one of the sensitive and critical disciplines of the G8D problem solving method in production processes, i.e. discipline D2 - “problem description”. The authors have presented the algorithm of procedure in the discipline D2 as well as the quality management tools that can be used to correctly “describe the problem”. The authors have also discussed the procedure for the discipline D2 in the “problem description” for the case of the projection welding of a nut.

scholarly journals Model-Based Tools for Pharmaceutical Manufacturing Processes

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 49 ◽  
Author(s):  
René Schenkendorf ◽  
Dimitrios Gerogiorgis ◽  
Seyed Mansouri ◽  
Krist Gernaey
Keyword(s):  
Quality Control ◽  
Pharmaceutical Manufacturing ◽  
Manufacturing Processes ◽  
Active Pharmaceutical Ingredients ◽  
Pharmaceutical Ingredients ◽  
Model Based ◽  
Production Processes ◽  
Highly Sensitive

Active pharmaceutical ingredients (APIs) are highly valuable, highly sensitive products resulting from production processes with strict quality control specifications and regulations that are required for the safety of patients [...]

Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine

2013 ◽  
Vol 38 (2) ◽  
pp. 374-379 ◽  
Author(s):  
Zhi-Li PAN ◽  
Meng QI ◽  
Chun-Yang WEI ◽  
Feng LI ◽  
Shi-Xiang ZHANG ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector ◽  
Tobacco Leaves ◽  
Region Classification

scholarly journals The Lean Six Sigma Algorithm—A Pathway for Decreasing the Continuous Improvement Projects Failure Rate

Proceedings ◽  
2020 ◽  
Vol 63 (1) ◽  
pp. 47
Author(s):  
Karam Al-Akel ◽  
Liviu-Onoriu Marian
Keyword(s):  
Failure Rate ◽  
Continuous Improvement ◽  
Six Sigma ◽  
Lean Six Sigma ◽  
Manufacturing Processes ◽  
Timely Manner ◽  
Improvement Project ◽  
Project Failures ◽  
Specific Order ◽  
Selection Of

Even if Lean and Six Sigma tools are available for large audiences, many of the continuous improvement projects fail due to the lack of a pathway that ensures appropriate results in a timely manner. We would like to address this universal issue by generating, testing and validating an algorithm that improves manufacturing processes in a controlled manner. With a selection of the most valuable set of tools and concepts implemented in a specific order, a guideline for successful project implementation is proposed. Decreasing the overall number of continuous improvement project failures is the main scope of our algorithm and suggested methodology.

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3068
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-González ◽  
Radim Burget
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
The Other ◽  
Support Vector ◽  
Learning Approaches ◽  
Microscopic Images ◽  
Trained Neural Network ◽  
Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

scholarly journals Research on Automated Defect Classification Based on Visual Sensing and Convolutional Neural Network-Support Vector Machine for GTA-Assisted Droplet Deposition Manufacturing Process

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 639
Author(s):  
Chen Ma ◽  
Haifei Dang ◽  
Jun Du ◽  
Pengfei He ◽  
Minbo Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Manufacturing Process ◽  
Support Vector ◽  
Defect Classification ◽  
Droplet Deposition ◽  
Network Support ◽  
Visual Sensing ◽  
Low Efficiency

This paper proposes a novel metal additive manufacturing process, which is a composition of gas tungsten arc (GTA) and droplet deposition manufacturing (DDM). Due to complex physical metallurgical processes involved, such as droplet impact, spreading, surface pre-melting, etc., defects, including lack of fusion, overflow and discontinuity of deposited layers always occur. To assure the quality of GTA-assisted DDM-ed parts, online monitoring based on visual sensing has been implemented. The current study also focuses on automated defect classification to avoid low efficiency and bias of manual recognition by the way of convolutional neural network-support vector machine (CNN-SVM). The best accuracy of 98.9%, with an execution time of about 12 milliseconds to handle an image, proved our model can be enough to use in real-time feedback control of the process.

scholarly journals Quality Control in Production Processes

2016 ◽  
Vol 19 (3) ◽  
pp. 77-83 ◽  
Author(s):  
Miroslav Prístavka ◽  
Martina Kotorová ◽  
Radovan Savov
Keyword(s):  
Quality Control ◽  
Quality Improvement ◽  
Quality Management ◽  
Statistical Methods ◽  
Developed Countries ◽  
Basic Knowledge ◽  
Root Cause ◽  
Production Processes ◽  
Basic Quality ◽  
Quality Tools

AbstractThe tools for quality management are used for quality improvement throughout the whole Europe and developed countries. Simple statistics are considered one of the most basic methods. The goal was to apply the simple statistical methods to practice and to solve problems by using them. Selected methods are used for processing the list of internal discrepancies within the organization, and for identification of the root cause of the problem and its appropriate solution. Seven basic quality tools are simple graphical tools, but very effective in solving problems related to quality. They are called essential because they are suitable for people with at least basic knowledge in statistics; therefore, they can be used to solve the vast majority of problems.

A Target Selection Algorithm of Seeker Based on the Prior Information and SVM

2013 ◽  
Vol 734-737 ◽  
pp. 3071-3074
Author(s):  
Guo Dong Zhang ◽  
Zhong Liu
Keyword(s):  
Prior Information ◽  
Simulation Experiment ◽  
Target Selection ◽  
Support Vector ◽  
Selection Algorithm ◽  
Distribution Law ◽  
Surface Ship ◽  
False Target ◽  
Target Characteristics ◽  
Selection Of

Aiming at the phenomenon that the chaff and corner reflector released by surface ship can influence the selection of missile seeker, this paper proposed a multi-target selection method based on the prior information of false targets distribution and Support Vector Machine (SVM). By analyzing the false targets distribution law we obtain two classification principles, which are used to train the SVM studies the true and false target characteristics. The trained SVM is applied to the seeker in the target selection. This method has advantages of simple programming and high classification accuracy, and the simulation experiment in this paper confirms the correctness and effectiveness of this method.

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