Analysis of AM parameters on surface roughness obtained in PLA parts printed with FFF technology

Fused filament fabrication (FFF) 3D printing technology allows very complex parts to be obtained at a relatively low cost and in reduced manufacturing times. In the present work, the effect of main 3D printing parameters on roughness obtained in curved surfaces is addressed. Polylactic acid (PLA) hemispherical cups were printed with a shape similar to that of the acetabular part of the hip prostheses. Different experiments were performed according to a factorial design of experiments, with nozzle diameter, temperature, layer height, print speed and extrusion multiplier as variables. Different roughness parameters were measured—Ra, Rz, Rku, Rsk—both on the outer surface and on the inner surface of the parts. Arithmetical mean roughness value Ra and greatest height of the roughness profile Rz are usually employed to compare the surface finish among different manufacturing processes. However, they do not provide information about the shape of the roughness profile. For this purpose, in the present work kurtosis Rku and skewness Rsk were used. If the height distribution in a roughness profile follows a normal law, the Rku parameter will take a value of 3. If the profile distribution is symmetrical, the Rsk parameter will take a value of 0. Adaptive neural fuzzy inference system (ANFIS) models were obtained for each response. Such models are often employed to model different manufacturing processes, but their use has not yet been extended to 3D printing processes. All roughness parameters studied depended mainly on layer height, followed by nozzle diameter. In the present work, as a general trend, Rsk was close to but lower than 0, while Rku was slightly lower than 3. This corresponds to slightly higher valleys than peaks, with a rounded height distribution to some degree.This research was co-financed by the European Union Regional Development Fund within the framework of the ERDF Operational Program of Catalonia 2014–2020 with a grant of 50% of total cost eligible, project BASE3D, grant number 001-P-001646

Data-Driven Fuzzy Weights-Of-Evidence Model for Identification of Potential Zeolite-Bearing Environments on Mars

The evolution of the climate and hydrochemistry of Mars is still a mystery but it must have been at least occasionally warm and wet to have formed the ancient fluvial and lacustrine landforms observed today. Terrestrial examples and geochemical modeling under proposed early Mars conditions show that zeolite minerals are likely to have formed under alkaline (pH \u3e 8) conditions with low water/rock ratio and surface temperatures below 150°C. The identification and spatial association of zeolites on the surface of Mars could thus be used to reconstruct the paleoclimate, paleohydrochemistry, and geological evolution of some locations on Mars. Previous studies identified the zeolite analcime and discuss the difficulties of identifying other zeolite species on the surface of Mars using orbital spectroscopy. We used published global mineralogical, geological, geomorphological, hydrological, physical, and elemental abundance maps and the locations of hydrous minerals detected and mapped using orbital data to create a map that delineates favorable areas to look for zeolites on Mars. We used the data-driven fuzzy-based Weights-of-Evidence method to identify and map favorable areas for zeolites on the surface of Mars up to ±40° latitude toward the poles. The final map shows that the eastern and western Arabia deposits, some sites in the Medusae Fossae formation, and some areas within and near Valles Marineris, Mawrth Vallis, highlands north of Hellas, and the Terra Cimmeria and Terra Sirenum regions would be favorable areas to look for zeolites using targeted orbital spectral analysis or future in situ observations

Process Modeling Optimization in Additive Manufacturing Using Artificial Neural Networks

The need for production has roots in human life and its history. This date back to primitive days of human life, where he or she had to apply surrounding materials in order to manufacture the tools necessary for survival and durability against any insecurity. This was legitimizing the use of any means in order to obtain the tools and reach the goals at any cost. However, with human development primarily within the knowledge and understanding domain and also with the desire of humanity for best, expectations have risen. This was the time not only the cost mattered but also the simplicity of design, massive production, and diversity, less waste, autonomy, and implementation within a shorter time gained a higher momentum. On the other hand, the conventional manufacturing method was based on subtractive manufacturing with cutting and eliminating the unwanted sections or parts of an object. The disadvantage of such a method is that it requires a complicated production process design and is accompanied by waste. However, with the rise of additive manufacturing and three-dimensional printing equipment back in the 1980s, it became possible to build parts which could have almost any shape or geometry. Moreover, this also empowered the possibility of using digital and 3D models built by computer-aided design software. Simultaneously, on the other side, the foundation and application of artificial intelligence were maturing. This was due to the demand for machines to assist human beings in the domain of knowledge reasoning, learning, and planning. These were the pillars for making machines autonomous and to benefit from such features. Accordingly, this research work studies and overviews the applications and techniques of machine learning and artificial intelligence in the domain of additive manufacturing. It aims to determine the interaction of influential parameters on the process and to find the best solutions for improving the quality and mechanical features of manufactured parts. Moreover, this research tends to enable the experts to grasp a better understanding of AM process during manufacturing and additionally intends to infuse the experts' knowledge in additive manufacturing field utilizing the artificial neural network and finally generate a model with the ability of prediction and selection for promising results

New Media, Free Expression, and the Offences Against the State Acts

New media facilitates communication and creates a common, lived experience. It also carries the potential for great harm on an individual and societal scale. Posting integrates information and emotion, with study after study finding that fear and anger transfer most readily online. Isolation follows, with insular groups forming. The result is an increasing bifurcation of society. Scholars also write about rising levels of depression and suicide that stem from online dependence and replacing analogical experience with digital interaction, as well as escalating levels of anxiety that are rooted in the validation expectation of the ‘like’ function. These changes generate instability and contribute to a volatile social environment. Significant political risks also accompany this novel genre. Hostile actors can use social media platforms to deepen political schisms, to promote certain candidates, and, as demonstrated by the recent Cambridge Analytica debacle, to swing elections. Extremist groups and terrorist organisations can use online interactions to build sympathetic audiences and to recruit adherents. Since 1939, the Offences Against the State Act (OAS) has served as the primary vehicle for confronting political violence and challenges to state authority. How effective is it in light of new media? The challenges are legion. Terrorist recruitment is just the tip of the iceberg. Social networking sites allow for targeted and global fundraising, international direction and control, anonymous power structures, and access to expertise. These platforms create spaces within which extreme ideologies can prosper, targeting individuals likely to be sympathetic to the cause, 24 hours a day, seven days a week, ad infinitum. They offer an alternative reality, subject to factual manipulation and direction—a problem exacerbated by the risk of so-called deep fakes: autonomously-generated content that makes it appear that people acted, or that certain circumstances occurred, which never did. In November 2019 the Irish Government adopted a new regulation targeting social media. The measure focuses on political advertising and to ensure that voters have access to accurate information. It does not address the myriad further risks. This chapter, accordingly, focuses on ways in which the Offences Against the State Act (OAS) and related laws have historically treated free expression as a prelude to understanding how and whether the existing provisions are adequate for challenges from new media

Integrated intelligent systems for industrial automation: the challenges of Industry 4.0, information granulation and understanding agents .

The objective of the paper consists in considering the challenges of new automation paradigm Industry 4.0 and reviewing the-state-of-the-art in the field of its enabling information and communication technologies, including Cyberphysical Systems, Cloud Computing, Internet of Things and Big Data. Some ways of multi-dimensional, multi-faceted industrial Big Data representation and analysis are suggested. The fundamentals of Big Data processing with using Granular Computing techniques have been developed. The problem of constructing special cognitive tools to build artificial understanding agents for Integrated Intelligent Enterprises has been faced

Low carbon manufacturing: Fundamentals, methodology and application case studies

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The requirement and awareness of the carbon emissions reduction in several scales and application of sustainable manufacturing have been now critically reviewed as important manufacturing trends in the 21st century. The key requirements for carbon emissions reduction in this context are energy efficiency, resource utilization, waste minimization and even the reduction of total carbon footprint. The recent approaches tend to only analyse and evaluate carbon emission contents of interested engineering systems. However, a systematic approach based on strategic decision making has not been officially defined with no standards or guidelines further formulated yet. The above requirements demand a fundamentally new approach to future applications of sustainable low carbon manufacturing. Energy and resource efficiencies and effectiveness based low carbon manufacturing (EREEbased LCM) is thus proposed in this research. The proposed EREE-based LCM is able to provide the systematic approach for integrating three key elements (energy efficiency, resource utilization and waste minimization) and taking account of them comprehensively in a scientific manner. The proposed approach demonstrates the solution for reducing carbon emissions in manufacturing systems at both the machine and shop floor levels. An integrated framework has been developed to demonstrate the feasible approach to achieve effective EREE-based LCM at different manufacturing levels including machine, shop floor, enterprise and supply chains. The framework is established in the matrix form with appropriate tools and methodologies related to the three keys elements at each manufacturing level. The theoretical model for EREE-based LCM is also presented, which consists of three essential elements including carbon dioxide emissions evaluation, an optimization method and waste reduction methodology. The preliminary experiment and simulations are carried out to evaluate the proposed concept. The modelling of EREE-based LCM has been developed for both the machine and shop floor levels. At the machine level, the modelling consists of the simulation of energy consumption due to the effect of machining set-up, the optimization model and waste minimization related to the optimized machining set-up. The simulation is established using sugeno type fuzzy logic. The learning method uses on experimental data (cutting trials) while the optimization model is created using mamdani type fuzzy logic with grey relational grade technique. At the shop floor level, the modelling is designed dependent on the cooperation with machine level modelling. The determination of the work assignment including machining set-up depends on fuzzy integer linear programming for several objectives with the evaluation of energy consumption data from machine level modelling. The simulation method is applied as the part of shop floor level modelling in order to maximize resource utilization and minimize undesired waste. The output from the shop floor level modelling is machine production a planning with preventive plan that can minimize the total carbon footprint. The axiomatic design theory has been applied to generate the comprehensive conceptual model E-R-W-C (energy, resource, waste and carbon footprint) of EREE-based LCM as a generic perspective of the systematic modelling. The implementation of EREE-based LCM on both the machine and shop floor levels are demonstrated using MATLAB toolbox and ProModel based simulation. The proposed concept, framework and modelling have been further evaluated and validated through case studies and experimental results.This work is financially supported by The Royal Thai Government

A finder and representation system for knowledge carriers based on granular computing

In one of his publications Aristotle states ”All human beings by their nature desire to know” [Kraut 1991]. This desire is initiated the day we are born and accompanies us for the rest of our life. While at a young age our parents serve as one of the principle sources for knowledge, this changes over the course of time. Technological advances and particularly the introduction of the Internet, have given us new possibilities to share and access knowledge from almost anywhere at any given time. Being able to access and share large collections of written down knowledge is only one part of the equation. Just as important is the internalization of it, which in many cases can prove to be difficult to accomplish. Hence, being able to request assistance from someone who holds the necessary knowledge is of great importance, as it can positively stimulate the internalization procedure. However, digitalization does not only provide a larger pool of knowledge sources to choose from but also more people that can be potentially activated, in a bid to receive personalized assistance with a given problem statement or question. While this is beneficial, it imposes the issue that it is hard to keep track of who knows what. For this task so-called Expert Finder Systems have been introduced, which are designed to identify and suggest the most suited candidates to provide assistance. Throughout this Ph.D. thesis a novel type of Expert Finder System will be introduced that is capable of capturing the knowledge users within a community hold, from explicit and implicit data sources. This is accomplished with the use of granular computing, natural language processing and a set of metrics that have been introduced to measure and compare the suitability of candidates. Furthermore, are the knowledge requirements of a problem statement or question being assessed, in order to ensure that only the most suited candidates are being recommended to provide assistance