scholarly journals Deep learning for the quality control of thermoforming food packages

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Núria Banús ◽  
Imma Boada ◽  
Pau Xiberta ◽  
Pol Toldrà ◽  
Narcís Bustins
Keyword(s):  
Computer Vision ◽  
Quality Control ◽  
Deep Learning ◽  
Production Line ◽  
Modular Design ◽  
Vision System ◽  
False Negative ◽  
Control Process ◽  
Food Packages ◽  
Quality Control Process

AbstractQuality control is a key process designed to ensure that only products satisfying the defined quality requirements reach the end consumer or the next step in a production line. In the food industry, in the packaging step, there are many products that are still evaluated by human operators. To automate the process and improve efficiency and effectiveness, computer vision and artificial intelligence techniques can be applied. This automation is challenging since specific strategies designed according to the application scenario are required. Focusing on the quality control of the sealing and closure of matrix-shaped thermoforming food packages, the aim of the article is to propose a deep-learning-based solution designed to automatically perform the quality control while satisfying production cadence and ensuring 100% inline inspection of the products. Particularly, the designed computer vision system and the image-based criteria defined to determine when a product has to be accepted or rejected are presented. In addition, the vision control software is described with special emphasis on the different convolutional neural network (CNN) architectures that have been considered (ResNet18, ResNet50, Vgg19 and DenseNet161, non-pre-trained and pre-trained on ImageNet) and on the specifically designed dataset. To test the solution, different experiments are carried out in the laboratory and also in a real scenario, concluding that the proposed CNN-based approach improves the efficiency and security of the quality control process. Optimal results are obtained with the pre-trained DenseNet161, achieving false positive rates that range from 0.03 to 0.30% and false negative rates that range from 0 to 0.07%, with a rejection rate between 0.64 and 5.09% of production, and being able to detect at least 99.93% of the sealing defects that occur in any production. The modular design of our solution as well as the provided description allow it to adapt to similar scenarios and to new deep-learning models to prevent the arrival of faulty products to end consumers by removing them from the automated production line.

A Machine Vision System for Inspecting Hydraulic Hose Assemblies

1994 ◽  
Author(s):  
Sudhir I. Mehta ◽  
Bruce B. Chenoweth
Keyword(s):  
Quality Control ◽  
Machine Vision ◽  
Human Error ◽  
Vision System ◽  
Control Process ◽  
Shop Floor ◽  
Machine Vision System ◽  
Quality Control Process

Abstract This paper describes a machine vision system for inspecting hydraulic hose assemblies. At present the inspection in this industry is done manually and is prone to human error. A specially designed hose gripping mechanism, a mandrel system, and a camera and lighting fixture allows the system to be integrated on a shop floor and is able to inspect various parameters of a fitting. The system allows the inspection to be done more accurately and improves the quality control process.

scholarly journals Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3987 ◽  
Author(s):  
Javier Villalba-Diez ◽  
Daniel Schmidt ◽  
Roman Gevers ◽  
Joaquín Ordieres-Meré ◽  
Martin Buchwitz ◽  
...  
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Control Process ◽  
Optical Quality ◽  
Soft Sensor ◽  
Training Data ◽  
Printing Industry ◽  
Quality Control Process

Rapid and accurate industrial inspection to ensure the highest quality standards at a competitive price is one of the biggest challenges in the manufacturing industry. This paper shows an application of how a Deep Learning soft sensor application can be combined with a high-resolution optical quality control camera to increase the accuracy and reduce the cost of an industrial visual inspection process in the Printing Industry 4.0. During the process of producing gravure cylinders, mistakes like holes in the printing cylinder are inevitable. In order to improve the defect detection performance and reduce quality inspection costs by process automation, this paper proposes a deep neural network (DNN) soft sensor that compares the scanned surface to the used engraving file and performs an automatic quality control process by learning features through exposure to training data. The DNN sensor developed achieved a fully automated classification accuracy rate of 98.4%. Further research aims to use these results to three ends. Firstly, to predict the amount of errors a cylinder has, to further support the human operation by showing the error probability to the operator, and finally to decide autonomously about product quality without human involvement.

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

Analysis of the information management system in the manufacturing process of cigarette enterprises using fuzzy AHP

2021 ◽  
pp. 1-11
Author(s):  
Song Gang ◽  
Wang Xiaoming ◽  
Wu Junfeng ◽  
Li Shufang ◽  
Liu Zhuowen ◽  
...  
Keyword(s):  
Quality Control ◽  
Production Process ◽  
Information Management ◽  
Manufacturing Process ◽  
Management System ◽  
Fuzzy Ahp ◽  
Control Process ◽  
Information Management System ◽  
Manufacturing Execution System ◽  
Quality Control Process

In view of the production quality management of filter rods in the manufacturing and execution process of cigarette enterprises, this paper analyzes the necessity of implementing the manufacturing execution system (MES) in the production process of filter rods. In this paper, the filter rod quality system of cigarette enterprise based on MES is fully studied, and the constructive information management system demand analysis, cigarette quality control process, system function module design, implementation and test effect are given. This paper utilizes the Fuzzy analytic hierarchy process to find the optimal system for processing the manufacturing of cigarette. The implementation of MSE based filter rod quality information management system for a cigarette enterprise ensures the quality control in the cigarette production process. Through visualization, real-time and dynamic way, the information management of cigarette production is completed, which greatly improves the quality of cigarette enterprise manufacturing process.

Mitocytosis, a migrasome-mediated mitochondrial quality-control process

Cell ◽  
2021 ◽  
Vol 184 (11) ◽  
pp. 2896-2910.e13
Author(s):  
Haifeng Jiao ◽  
Dong Jiang ◽  
Xiaoyu Hu ◽  
Wanqing Du ◽  
Liangliang Ji ◽  
...  
Keyword(s):  
Quality Control ◽  
Control Process ◽  
Mitochondrial Quality Control ◽  
Quality Control Process

scholarly journals A Two-Stage Quality Control Method for 2-m Temperature Observations Using Biweight Means and a Progressive EOF Analysis

2013 ◽  
Vol 141 (2) ◽  
pp. 798-808 ◽  
Author(s):  
Zhifang Xu ◽  
Yi Wang ◽  
Guangzhou Fan
Keyword(s):  
Quality Control ◽  
Control Method ◽  
Control Process ◽  
Lapse Rate ◽  
Systematic Bias ◽  
Eof Analysis ◽  
Two Stage ◽  
Quality Control Method ◽  
Quality Control Process ◽  
The Difference

Abstract The relatively smooth terrain embedded in the numerical model creates an elevation difference against the actual terrain, which in turn makes the quality control of 2-m temperature difficult when forecast or analysis fields are utilized in the process. In this paper, a two-stage quality control method is proposed to address the quality control of 2-m temperature, using biweight means and a progressive EOF analysis. The study is made to improve the quality control of the observed 2-m temperature collected by China and its neighboring areas, based on the 6-h T639 analysis from December 2009 to February 2010. Results show that the proposed two-stage quality control method can secure the needed quality control better, compared with a regular EOF quality control process. The new method is, in particular, able to remove the data that are dotted with consecutive errors but showing small fluctuations. Meanwhile, compared with the lapse rate of temperature method, the biweight mean method is able to remove the systematic bias generated by the model. It turns out that such methods make the distributions of observation increments (the difference between observation and background) more Gaussian-like, which ensures the data quality after the quality control.

Vision-Based Precision Deburring and Edge Breaking Process Quality Control

2020 ◽  
Author(s):  
Kartik Gupta ◽  
Cindy Grimm ◽  
Burak Sencer ◽  
Ravi Balasubramanian
Keyword(s):  
Computer Vision ◽  
Quality Control ◽  
Vision System ◽  
Quality Measures ◽  
Cad Model ◽  
Computer Vision System ◽  
Part Geometry ◽  
Specialized Hardware ◽  
Original Part

Abstract This paper presents a computer vision system for evaluating the quality of deburring and edge breaking on aluminum and steel blocks. This technique produces both quantitative (size) and qualitative (quality) measures of chamfering operation from images taken with an off-the-shelf camera. We demonstrate that the proposed computer vision system can detect edge chamfering geometry within a 1–2mm range. The proposed technique does not require precise calibration of the camera to the part nor specialized hardware beyond a macro lens. Off-the-shelf components and a CAD model of the original part geometry are used for calibration. We also demonstrate the effectiveness of the proposed technique on edge breaking quality control.

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