5,447 research outputs found
Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm
Dendritic cells are antigen presenting cells that provide a vital link
between the innate and adaptive immune system, providing the initial detection
of pathogenic invaders. Research into this family of cells has revealed that
they perform information fusion which directs immune responses. We have derived
a Dendritic Cell Algorithm based on the functionality of these cells, by
modelling the biological signals and differentiation pathways to build a
control mechanism for an artificial immune system. We present algorithmic
details in addition to experimental results, when the algorithm was applied to
anomaly detection for the detection of port scans. The results show the
Dendritic Cell Algorithm is sucessful at detecting port scans.Comment: 21 pages, 17 figures, Information Fusio
A comparison of processing techniques for producing prototype injection moulding inserts.
This project involves the investigation of processing techniques for producing low-cost moulding inserts used in the particulate injection moulding (PIM) process. Prototype moulds were made from both additive and subtractive processes as well as a combination of the two. The general motivation for this was to reduce the entry cost of users when considering PIM.
PIM cavity inserts were first made by conventional machining from a polymer block using the pocket NC desktop mill. PIM cavity inserts were also made by fused filament deposition modelling using the Tiertime UP plus 3D printer.
The injection moulding trials manifested in surface finish and part removal defects. The feedstock was a titanium metal blend which is brittle in comparison to commodity polymers. That in combination with the mesoscale features, small cross-sections and complex geometries were considered the main problems. For both processing methods, fixes were identified and made to test the theory. These consisted of a blended approach that saw a combination of both the additive and subtractive processes being used.
The parts produced from the three processing methods are investigated and their respective merits and issues are
discussed
Reducing risk in pre-production investigations through undergraduate engineering projects.
This poster is the culmination of final year Bachelor of Engineering Technology (B.Eng.Tech) student projects
in 2017 and 2018. The B.Eng.Tech is a level seven qualification that aligns with the Sydney accord for a three-year engineering degree and hence is internationally benchmarked. The enabling mechanism of these projects is the industry connectivity that creates real-world projects and highlights the benefits of the investigation of process at the technologist level.
The methodologies we use are basic and transparent, with enough depth of technical knowledge to ensure the industry partners gain from the collaboration process. The process we use minimizes the disconnect between the student and the industry supervisor while maintaining the academic freedom of the student and the commercial sensitivities of the supervisor.
The general motivation for this approach is the reduction of the entry cost of the industry to enable consideration of new technologies and thereby reducing risk to core business and shareholder profits.
The poster presents several images and interpretive dialogue to explain the positive and negative aspects of the student process
Automatic Structural Scene Digitalization
In this paper, we present an automatic system for the analysis and labeling
of structural scenes, floor plan drawings in Computer-aided Design (CAD)
format. The proposed system applies a fusion strategy to detect and recognize
various components of CAD floor plans, such as walls, doors, windows and other
ambiguous assets. Technically, a general rule-based filter parsing method is
fist adopted to extract effective information from the original floor plan.
Then, an image-processing based recovery method is employed to correct
information extracted in the first step. Our proposed method is fully automatic
and real-time. Such analysis system provides high accuracy and is also
evaluated on a public website that, on average, archives more than ten
thousands effective uses per day and reaches a relatively high satisfaction
rate.Comment: paper submitted to PloS On
Efficient Iterative Processing in the SciDB Parallel Array Engine
Many scientific data-intensive applications perform iterative computations on
array data. There exist multiple engines specialized for array processing.
These engines efficiently support various types of operations, but none
includes native support for iterative processing. In this paper, we develop a
model for iterative array computations and a series of optimizations. We
evaluate the benefits of an optimized, native support for iterative array
processing on the SciDB engine and real workloads from the astronomy domain
Employing Explainable Artificial Intelligence (XAI) Methodologies to Analyze the Correlation between Input Variables and Tensile Strength in Additively Manufactured Samples
This research paper explores the impact of various input parameters,
including Infill percentage, Layer Height, Extrusion Temperature, and Print
Speed, on the resulting Tensile Strength in objects produced through additive
manufacturing. The main objective of this study is to enhance our understanding
of the correlation between the input parameters and Tensile Strength, as well
as to identify the key factors influencing the performance of the additive
manufacturing process. To achieve this objective, we introduced the utilization
of Explainable Artificial Intelligence (XAI) techniques for the first time,
which allowed us to analyze the data and gain valuable insights into the
system's behavior. Specifically, we employed SHAP (SHapley Additive
exPlanations), a widely adopted framework for interpreting machine learning
model predictions, to provide explanations for the behavior of a machine
learning model trained on the data. Our findings reveal that the Infill
percentage and Extrusion Temperature have the most significant influence on
Tensile Strength, while the impact of Layer Height and Print Speed is
relatively minor. Furthermore, we discovered that the relationship between the
input parameters and Tensile Strength is highly intricate and nonlinear, making
it difficult to accurately describe using simple linear models
Developing of Ultrasound Experimental Methods using Machine Learning Algorithms for Application of Temperature Monitoring of Nano-Bio-Composites Extrusion
In industry fiber degradation during processing of biocomposite in the extruder is a problem that requires a reliable solution to save time and money wasted on producing damaged material. In this thesis, We try to focus on a practical solution that can monitor the change in temperature that causes fiber degradation and material damage to stop it when it occurs. Ultrasound can be used to detect the temperature change inside the material during the process of material extrusion. A monitoring approach for the extruder process has been developed using ultrasound system and the techniques of machine learning algorithms. A measurement cell was built to form a dataset of ultrasound signals at different temperatures for analysis. Machine learning algorithms were applied through machine-learning algorithm’s platform to classify the dataset based on the temperature. The dataset was classified with accuracy 97% into two categories representing over and below damage temperature (190oc) ultrasound signal. This approach could be used in industry to send an alarm or a temperature control signal when material damage is detected. Biocomposite is at the core of automotive industry material research and development concentration. Melt mixing process was used to mix biocomposite material with multi-walled carbon nanotubes (MWCNTs) for the purpose of enhancing mechanical and thermal properties of biocomposite. The resulting composite nano-bio- composite was tested via different types of thermal and mechanical tests to evaluate its performance relative to biocomposite. The developed material showed enhancement in mechanical and thermal properties that considered a high potential for applications in the future
Process monitoring for material extrusion additive manufacturing: a state-of-the-art review
Qualitative uncertainties are a key challenge for the further industrialization of additive manufacturing. To solve this challenge, methods for measuring the process states and properties of parts during additive manufacturing are essential. The subject of this review is in-situ process monitoring for material extrusion additive manufacturing. The objectives are, first, to quantify the research activity on this topic, second, to analyze the utilized technologies, and finally, to identify research gaps. Various databases were systematically searched for relevant publications and a total of 221 publications were analyzed in detail. The study demonstrated that the research activity in this field has been gaining importance. Numerous sensor technologies and analysis algorithms have been identified. Nonetheless, research gaps exist in topics such as optimized monitoring systems for industrial material extrusion facilities, inspection capabilities for additional quality characteristics, and standardization aspects. This literature review is the first to address process monitoring for material extrusion using a systematic and comprehensive approach
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