The Effect of Retort Processing Factors on the Severity of Film Surface Impression

Retort processing conditions of temperature, overpressure, and sterilization time were used to determine the impression depth on the pouch surface from contact with the retort rack during retort processing, also referred to as a waffling defect. Retortable flexible pouches were filled with 1,000 mL of water and processed in a horizontal water spray retort. A confocal laser scanning microscope was used to measure severity of the waffling defects. Data collected during this study showed that higher temperatures resulted in higher measured impression depth values (p\u3c0.05) in all of the tested combinations. However, when the pouches were retorted at different temperatures, different effects of retort overpressure were observed. At a low temperature (111 deg. C), the higher overpressure resulted in higher severity of the waffling defects (p\u3c0.05). In contrast, at a high temperature (131 deg. C), the higher overpressure resulted in lower severity of the waffling defects (p\u3c0.05). When samples were retorted at different sterilization time settings, there was only one condition where a statistical difference in the impression depth values was observed (p\u3c0.05). The longer sterilization time resulted in higher measured impression depth (p\u3c0.05) only if the samples were retorted at a high temperature of 131 deg. C with a low overpressure of 26 psig. There was no statistical difference (p\u3e0.05) in measured impression depth when samples were retorted at different sterilization time settings in other tested combinations. The difference in variance of the impression depth values between the samples retorted at different processing factor combinations was also studied. Processing factor combinations with a high temperature of 131 deg. C and a long sterilization time of 60 minutes, at any overpressure setting, resulted in a higher variance of waffling defect severity (p\u3c0.05) compared to other combinations. The relationship between retort processing factors and waffling defect severity was explained using a prediction equation. The lack of fit of the proposed equation was not significant (p\u3e0.05), while the hypothesis test (overall F-test) showed that the overall model was statistically significant (p\u3c0.05). Therefore, the proposed equation is useful in predicting the average impression depth value when retort temperature, overpressure, and sterilization time are known. This study showed how retort processing factors affect the severity of waffling defects and proposed a method to predict the waffling defect severity. This information could allow pouch manufacturers to develop retortable pouches and food processors to create new retort processes to reduce waffling

Infrared Thermography For Seal Defects Detection On Packaged Products: Unbalanced Machine Learning Classification With Iterative Digital Image Restoration

Non-destructive and online defect detection on seals is increasingly being deployed in packaging processes, especially for food and pharmaceutical products. It is a key control step in these processes as it curtails the costs of these defects. To address this cause, this paper highlights a combination of two cost-effective methods, namely machine learning algorithms and infrared thermography. Expectations can, however, be restricted when the training data is small, unbalanced, and subject to optical imperfections. This paper proposes a classification method that tackles these limitations. Its accuracy exceeds 93% with two small training sets, including 2.5 to 10 times fewer negatives. Its algorithm has a low computational cost compared to deep learning approaches, and does not need any prior statistical studies on defects characterization

Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques

A correct food tray sealing is required to preserve food properties and safety for consumers. Traditional food packaging inspections are made by human operators to detect seal defects. Recent advances in the field of food inspection have been related to the use of hyperspectral imaging technology and automated vision-based inspection systems. A deep learning-based approach for food tray sealing fault detection using hyperspectral images is described. Several pixel-based image fusion methods are proposed to obtain 2D images from the 3D hyperspectral image datacube, which feeds the deep learning (DL) algorithms. Instead of considering all spectral bands in region of interest around a contaminated or faulty seal area, only relevant bands are selected using data fusion. These techniques greatly improve the computation time while maintaining a high classification ratio, showing that the fused image contains enough information for checking a food tray sealing state (faulty or normal), avoiding feeding a large image datacube to the DL algorithms. Additionally, the proposed DL algorithms do not require any prior handcraft approach, i.e., no manual tuning of the parameters in the algorithms are required since the training process adjusts the algorithm. The experimental results, validated using an industrial dataset for food trays, along with different deep learning methods, demonstrate the effectiveness of the proposed approach. In the studied dataset, an accuracy of 88.7%, 88.3%, 89.3%, and 90.1% was achieved for Deep Belief Network (DBN), Extreme Learning Machine (ELM), Stacked Auto Encoder (SAE), and Convolutional Neural Network (CNN), respectively

Real-time quality control of heat sealed bottles

The present document describes a system for controlling the quality of heat sealed bottles. The system detects defective seals to identify bottles that can not be sold. A prototype was developed to validate and test the system proposed. In the production line, the bottles are filled with a toxic substance and can only be sold when properly sealed. A leak can be harmful to humans and the environment. Because the seals are not visible from outside the bottle, images from each seal are obtained using a thermal camera. The hot glue used in the sealing process makes the seal visible in the infrared image. The image is cleaned and converted to black and white only keeping the seal in the final image. Black pixels present the value 0 and white pixels present the value 1. Then a signature composed by two arrays containing the sum of the number of white pixels in each column and in each row is calculated. Both arrays present a U shape when the bottle is sealed. The signature is then fed to an artificial neural network which was trained to identify correctly sealed bottles. The classification results are stored in a database. The trained neural net presented an accuracy of 98.7 % and an F1 score of 96.0 % in the testing phase. The results shows the inspection process is effective in identifying defective seals and because it is automated it can be scaled up to large bottle processing plants. All classified images can be seen though a web application where a user has the option of validating the operation and identifying errors which will be individually fitted to improve the machine learning model performance. The system is non invasive, automated, and can be applied to common conveyor belts currently used in industrial plants. It can also be adapted to detect different prob lems in bottles of different shapes.Nesta dissertação é descrito um sistema de controlo de qualidade de selos em garrafas. Foi contruído um protótipo com o objetivo de testar e validar o funcionamento do sistema. Na linha de produção, as garrafas são cheias com uma substância tóxica e apenas podem ser vendidas quando corretamente seladas pois uma fuga põe em risco a saúde do utilizador. A dificuldade deste processo deve-se ao facto de o selo não ser visível pois encontra-se debaixo da tampa opaca da garrafa. Dado o uso de cola quente no processo de selagem, com uma câmara térmica é possível obter uma imagem do selo. Esta imagem é depois processada com o intuito de isolar o selo na imagem final. Da imagem final gera-se uma assinatura que consiste na juncão de duas listas contendo a soma do número de pixels brancos por coluna e por linha. Ambas as listas apresentam uma forma de ‘U’ quando a garrafa está corretamente selada. Uma rede neuronal utiliza a assinatura para classificar a imagem, identificando garrafas mal seladas. O resultado obtido é registado numa base de dados. A rede neuronal treinada apresentou uma accuracy de 98,7 % e um F1 score de 96,0 % na fase de treino mostrando que é eficiente na identificação de selos defeituosos. O sistema inclui a possibilidade de validar as classificações usando uma aplicação web onde é possível analisar o histórico de imagens. Quando uma imagem incorretamente classificada é identificada, esta deve ser selecionada e novamente treinada para corrigir o erro e permitir que o modelo tenha capacidade de aprendizagem. Este método não é invasivo nem destrutivo, é automatizado e pode ser usado na produção de produtos diferentes desde que o processo de selagem seja semelhante

Design of equipment safety & reliability for an aseptic liquid food packaging line through maintenance engineering

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The organisation of maintenance, in the Aseptic Liquid Food (ALF) industry, represents an important management task that enables a company to pursue higher manufacturing effectiveness and improved market share. This research is concerned with the process to design and implement maintenance tasks. These two complementary processes (design and implementation) have been thought and designed to answer the particular needs of food industry regarding product safety and equipment reliability. Numerous maintenance engineering researchers have focused on maintenance engineering and reliability techniques highlighting the contribution of maintenance in achieving world class manufacturing and competitive advantage. Their outcome emphasizes that maintenance is not a “necessary evil” because of costs associated, but it can be considered an “investment” that produces an added value which generates a real company profit. The existing maintenance engineering techniques pursue equipment reliability at minimum cost; but in food industry, food safety represents the most critical issue to address and solve. The research methodology chosen is based on case studies coming from ALF industries. These show that low maintenance effectiveness could have dramatic effects on final consumers and on the company’s image and underline the need of a maintenance design and implementation process that takes into consideration all critical factors relevant to liquid food industry. The analysis of measurable indicators available, represents a tool necessary to show the status of critical performance indicators and reveals the urgency of a research necessary to address and solve the maintenance problems in food industry. The literature review underlines the increasing regulations in place in food industry and that no literature is available to define a maintenance design and implementation process for ALF and in general for food industry. The literature review enabled also the gap existing between theory and real maintenance status, in the ALF, to be identified and the aim of the research was to explore this gap. The analysis of case studies and Key Performance Indicators (KPI’s) available highlights the problem and the literature review provides the knowledge necessary to identify the process to design and implement maintenance procedures for ALF industry. The research findings provide a useful guide to identify the process to design maintenance tasks able to put under control food safety and equipment reliability issues. Company’s restraining forces and cultural inertia, that work against new maintenance procedures, have been analysed and a maintenance implementation process have been designed to avoid losing the benefits produced by the design phase. The analysis of condition monitoring systems shows devices and techniques useful to improve product safety, equipment reliability, and then maintenance effectiveness. This research aimed to fill the gap in the existing literature showing the solution to manage both food safety and production effectiveness issues in food industry. It identifies a maintenance design process able to capture all conceivable critical factors in food industry and to provide the solution to design reliable task lists. Furthermore, the maintenance implementation process shows the way to maximize the maintenance design outcome through the empowerment of equipment operators and close cooperation with maintenance and quality specialists. The new maintenance design and implementation process represents the answer to the research problem and a reliable solution that allows the food industry to improve food safety and production effectiveness

Design of equipment safety & reliability for an aseptic liquid food packaging line through maintenance engineering

The organisation of maintenance, in the Aseptic Liquid Food (ALF) industry, represents an important management task that enables a company to pursue higher manufacturing effectiveness and improved market share. This research is concerned with the process to design and implement maintenance tasks. These two complementary processes (design and implementation) have been thought and designed to answer the particular needs of food industry regarding product safety and equipment reliability. Numerous maintenance engineering researchers have focused on maintenance engineering and reliability techniques highlighting the contribution of maintenance in achieving world class manufacturing and competitive advantage. Their outcome emphasizes that maintenance is not a “necessary evil” because of costs associated, but it can be considered an “investment” that produces an added value which generates a real company profit. The existing maintenance engineering techniques pursue equipment reliability at minimum cost; but in food industry, food safety represents the most critical issue to address and solve. The research methodology chosen is based on case studies coming from ALF industries. These show that low maintenance effectiveness could have dramatic effects on final consumers and on the company’s image and underline the need of a maintenance design and implementation process that takes into consideration all critical factors relevant to liquid food industry. The analysis of measurable indicators available, represents a tool necessary to show the status of critical performance indicators and reveals the urgency of a research necessary to address and solve the maintenance problems in food industry. The literature review underlines the increasing regulations in place in food industry and that no literature is available to define a maintenance design and implementation process for ALF and in general for food industry. The literature review enabled also the gap existing between theory and real maintenance status, in the ALF, to be identified and the aim of the research was to explore this gap. The analysis of case studies and Key Performance Indicators (KPI’s) available highlights the problem and the literature review provides the knowledge necessary to identify the process to design and implement maintenance procedures for ALF industry. The research findings provide a useful guide to identify the process to design maintenance tasks able to put under control food safety and equipment reliability issues. Company’s restraining forces and cultural inertia, that work against new maintenance procedures, have been analysed and a maintenance implementation process have been designed to avoid losing the benefits produced by the design phase. The analysis of condition monitoring systems shows devices and techniques useful to improve product safety, equipment reliability, and then maintenance effectiveness. This research aimed to fill the gap in the existing literature showing the solution to manage both food safety and production effectiveness issues in food industry. It identifies a maintenance design process able to capture all conceivable critical factors in food industry and to provide the solution to design reliable task lists. Furthermore, the maintenance implementation process shows the way to maximize the maintenance design outcome through the empowerment of equipment operators and close cooperation with maintenance and quality specialists. The new maintenance design and implementation process represents the answer to the research problem and a reliable solution that allows the food industry to improve food safety and production effectiveness.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

NASA Tech Briefs, Februrary 2013

Topics covered include: Measurements of Ultra-Stable Oscillator (USO) Allan Deviations in Space; Gaseous Nitrogen Orifice Mass Flow Calculator; Validation of Proposed Metrics for Two-Body Abrasion Scratch Test Analysis Standards; Rover Low Gain Antenna Qualification for Deep Space Thermal Environments; Automated, Ultra-Sterile Solid Sample Handling and Analysis on a Chip; Measuring and Estimating Normalized Contrast in Infrared Flash Thermography; Spectrally and Radiometrically Stable, Wideband, Onboard Calibration Source; High-Reliability Waveguide Vacuum/Pressure Window; Methods of Fabricating Scintillators With Radioisotopes for Beta Battery Applications; Magnetic Shield for Adiabatic Demagnetization Refrigerators (ADR); CMOS-Compatible SOI MESFETS for Radiation-Hardened DC-to-DC Converters; Silicon Heat Pipe Array; Adaptive Phase Delay Generator; High-Temperature, Lightweight, Self-Healing Ceramic Composites for Aircraft Engine Applications; Treatment to Control Adhesion of Silicone-Based Elastomers; High-Temperature Adhesives for Thermally Stable Aero-Assist Technologies; Rockballer Sample Acquisition Tool; Rock Gripper for Sampling, Mobility, Anchoring, and Manipulation; Advanced Magnetic Materials Methods and Numerical Models for Fluidization in Microgravity and Hypogravity; Data Transfer for Multiple Sensor Networks Over a Broad Temperature Range; Using Combustion Synthesis to Reinforce Berms and Other Regolith Structures; Visible-Infrared Hyperspectral Image Projector; Three-Axis Attitude Estimation With a High-Bandwidth Angular Rate Sensor Change_Detection.m; AGATE: Adversarial Game Analysis for Tactical Evaluation; Ionospheric Simulation System for Satellite Observations and Global Assimilative; Modeling Experiments (ISOGAME); An Extensible, User- Modifiable Framework for Planning Activities; Mission Operations Center (MOC) - Precipitation Processing System (PPS) Interface Software System (MPISS); Automated 3D Damaged Cavity Model Builder for Lower Surface Acreage Tile on Orbiter; Mixed Linear/Square-Root Encoded Single-Slope Ramp Provides Low-Noise ADC with High Linearity for Focal Plane Arrays; RUSHMAPS: Real-Time Uploadable Spherical Harmonic Moment Analysis for Particle Spectrometers; Powered Descent Guidance with General Thrust-Pointing Constraints; X-Ray Detection and Processing Models for Spacecraft Navigation and Timing; and Extreme Ionizing-Radiation-Resistant Bacteriu

NASA Tech Briefs, November 2002

Topics include: a technology focus on engineering materials, electronic components and systems, software, mechanics, machinery/automation, manufacturing, bio-medical, physical sciences, information sciences book and reports, and a special section of Photonics Tech Briefs

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included