Research on the Importance of Sampling Inspection Method in Product Quality Inspection

2021 ◽  
Keyword(s):  
Product Quality ◽  
Quality Inspection ◽  
Inspection Method ◽  
Sampling Inspection

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.

Ultrasonic Attenuation Based Inspection Method for Scale-up Production of A206–Al2O3 Metal Matrix Nanocomposites

2015 ◽  
Vol 137 (1) ◽  
Author(s):  
Jianguo Wu ◽  
Shiyu Zhou ◽  
Xiaochun Li
Keyword(s):  
Metal Matrix ◽  
High Performance ◽  
Ultrasonic Attenuation ◽  
Hypothesis Test ◽  
Scale Up ◽  
Al2o3 Nanoparticles ◽  
Quality Inspection ◽  
Ultrasonic Dispersion ◽  
Inspection Method ◽  
Metal Matrix Nanocomposites

A206–Al2O3 metal matrix nanocomposite (MMNC) is a promising high performance material with potential applications in various industries, such as automotive, aerospace, and defense. Al2O3 nanoparticles dispersed into molten Al using ultrasonic cavitation technique can enhance the nucleation of primary Al phase to reduce its grain size and modify the secondary intermetallic phases. To enable a scale-up production, an effective yet easy-to-implement quality inspection technique is needed to effectively evaluate the resultant microstructure of the MMNCs. At present the standard inspection technique is based on the microscopic images, which are costly and time-consuming to obtain. This paper investigates the relationship between the ultrasonic attenuation and the microstructures of pure A206 and Al2O3 reinforced MMNCs with/without ultrasonic dispersion. A hypothesis test based on an estimated attenuation variance was developed and it could accurately differentiate poor samples from good ones. This study provides useful guidelines to establish a new quality inspection technique for A206–Al2O3 nanocomposites using ultrasonic nondestructive testing method.

A CS-AdaBoost-BP model for product quality inspection

2020 ◽  
Author(s):  
Zengyuan Wu ◽  
Caihong Zhou ◽  
Fei Xu ◽  
Wengao Lou
Keyword(s):  
Product Quality ◽  
Quality Inspection ◽  
Bp Model

Shape Defect Detection using Local Standard Deviation and Rule-Based Classifier for Bottle Quality Inspection

2017 ◽  
Vol 8 (1) ◽  
pp. 107
Author(s):  
Norhashimah Mohd Saad ◽  
Nor Nabilah Syazana Abdul Rahma ◽  
Abdul Rahim Abdullah ◽  
Mohd Juzaila Abd Latif
Keyword(s):  
Standard Deviation ◽  
Product Quality ◽  
Quality Inspection ◽  
Rule Based ◽  
Low Contrast ◽  
Contrast Gain ◽  
Local Standard ◽  
Local Standard Deviation ◽  
Rule Based Classifier ◽  
Ace Algorithm

This paper presents shape analysis using Local Standard Deviation (LSD) technique to detect shape defect of the bottle for product quality inspection. The proposed analysis framework includes segmentation, feature extraction, and classification. The shape of the bottle was segmented using LSD technique in order to obtain higher enhancement at the low contrast area and low enhancement at the high contrast area. <span lang="EN-MY">The contrast gain that was applied in Adaptive Contrast Enhancement (ACE) algorithm, was presented inversely proportional to LSD in order to detect and eliminate background noise at the bottle edge. After the segmentation process, the parameters of the bottle shape such as height, width, area, and extent were extracted and applied in classification stage. The rule-based classifier was used to classify the shape of the bottle either good or defect. The offline experimental results exhibit superior segmentation on performance with 100% accuracy for 100 sample images. This shows that the LSD could be an effective technique to monitor the product quality.</span>

Application of Nondestructive Testing in Quality Controlling of High-Density Polyethylene Silicore Plastic Duct

2013 ◽  
Vol 470 ◽  
pp. 693-696
Author(s):  
Bai Yan Gong ◽  
Yu Hong Lu ◽  
Juan Ren
Keyword(s):  
Nondestructive Testing ◽  
Product Quality ◽  
High Density Polyethylene ◽  
High Density ◽  
Important Indicator ◽  
Breaking Elongation ◽  
Sampling Inspection ◽  
Weight Impact ◽  
Falling Weight

Some key quality problems has been exposed in several years of quality supervision sampling inspection of industry products for HDPE silicore plastic duct, such as dimension, breaking elongation, performance of falling weight impact and ring stiffness. Quality consistency is an important indicator in evaluating product quality. Nondestructive testing can be used to evaluate the quality of the whole pan and batch of silicore plastic duct, and it will play important role in product quality controlling.

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