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The purpose of this paper is to show the development of an image processing-based portable equipment for an automatic wear debris analysis. It can analyze both the qualitative and quantitative features of machine wear debris: size, quantity, size distribution, shape, surface texture and material composition via color.

It comprises hardware and software components which can take debris in near real-time from a machine oil sump and process it for features diagnosis. This processing provides the information of the basic features on the user screen which can further be used for machine component health diagnosis.

The developed system has the capacity to replace the existing off-line methods due to its cost effectiveness and simplicity in operation. The system is able to analyze debris basic quantitative and qualitative features greater than 50 micron and less than 300 micron.

Wear debris basic features analysis tool is developed and discussed. The portable and near real-time analysis offered by the discussed work can be more technically effective as compared to the existing off-line and online techniques.

The involvement of wear, friction and lubrication in engineering systems and industrial applications makes it imperative to study the various aspects of tribology in relation with advanced technologies and concepts. The concept of Industry 4.0 and its implementation further faces a lot of barriers, particularly in developing economies. Real-time and reliable data is an important enabler for the implementation of the concept of Industry 4.0. For availability of reliable and real-time data about various tribological systems is crucial in applying the various concepts of Industry 4.0. This paper aims to attempt to highlight the role of sensors related to friction, wear and lubrication in implementing Industry 4.0 in various tribology-related industries and equipment.

A through literature review has been done to study the interrelationships between the availability of tribology-related data and implementation of Industry 4.0 are also discussed. Relevant and recent research papers from prominent databases have been included. A detailed overview about the various types of sensors used in generating tribological data is also presented. Some studies related to the application of machine learning and artificial intelligence (AI) are also included in the paper. A discussion on fault diagnosis and cyber physical systems in connection with tribology has also been included.

Industry 4.0 and tribology are interconnected through various means and the various pillars of Industry 4.0 such as big data, AI can effectively be implemented in various tribological systems. Data is an important parameter in the effective application of concepts of Industry 4.0 in the tribological environment. Sensors have a vital role to play in the implementation of Industry 4.0 in tribological systems. Determining the machine health, carrying out maintenance in off-shore and remote mechanical systems is possible by applying online-real-time data acquisition.

The paper tries to relate the pillars of Industry 4.0 with various aspects of tribology. The paper is a first of its kind wherein the interdisciplinary field of tribology has been linked with Industry 4.0. The paper also highlights the role of sensors in generating tribological data related to the critical parameters, such as wear rate, coefficient of friction, surface roughness which is critical in implementing the various pillars of Industry 4.0.

The purpose of this study is to develop a cylindrical capacitive sensor that has the advantages of high resolution, small size and designability and can be easily installed on lubricant pipeline to monitor lubricant oil debris.

A theoretical model of the cylindrical capacitive sensor is presented to analyze several parameters’ effectiveness on the performance of sensor. Numerical simulations are then conducted to determine the optimal parameters for preliminary experiments. Experiments are finally carried out to demonstrate the detectability of developed capacitive sensors.

It is clear from experimental results that the developed capacitive sensor can monitor the debris in lubricant oil well, and the capacitance values increase almost linearly when the number and size of debris increase.

There is lot of further work to do to apply the presented method into the application. Especially, it is necessary to consider several factors’ influence on monitoring results. These factors include the flow rate of the lubricant oil, the temperature, the debris distribution and the vibration. Moreover, future work should consider the influence of the oil degradation to the capacitance change and other contaminations (e.g. water and dust).

This work conducts a feasibility study on application of capacitive sensing principle for detecting debris in aero engine lubricant oil.

The novelty of the presented capacitance sensor can be summarized into two aspects. One is that the sensor structure is simple and characterized by two coaxial cylinders as electrodes, while conventional capacitive sensors are composed of two parallel plates as electrodes. The other is that sensing mechanism and physical model of the presented sensor is verified and validated by the simulation and experiment.

On September 9, 2018, Hurricane Irma swept across south Florida, leaving a path of destruction across the entire state. Miami-Dade County, at the southern tip of the state, avoided a direct hit. However, the storm left the county and its dozens of municipalities with gigantic mounds of storm debris. As the weeks went by, the piles festered and frustration with the pace of the clean-up mounted. Two dump sites in particular drew the attention of media and community activists: a park ringed by single family homes in Liberty City, a black community in the heart of Miami; and historic Virginia Key, the only beach open to black citizens under Jim Crow segregation. This research examines three narratives -- media coverage, official explanations from local governments, and reactions on social media -- as a way to investigate how the dumping of storm debris in black spaces was justified, interrogated, and contested in the aftermath of one of the worst hurricanes to strike Miami-Dade County in over a decade. Climate change models predict the increasing frequency of super storms like Irma, and discussions of how coastal cities respond in terms of infrastructure and resiliency are growing. This investigation looks at two components of this response that have not been as widely considered: what are the institutional and citizen responses in the aftermath of these storms, and how will issues of race and historic geographic marginalization be either acknowledged or ignored as the problems associated with climate change grow ever more acute and pressing.

Computer‐aided fragmented cultural relics repair is an effective method instead of manual repair. The purpose of this paper is to provide a 3D digital patching system for computer‐aided cultural relics repair through using the scanned 3D data of fragmented cultural relics. It includes processes and tools that can be effectively used for fragmented cultural relics repair.

An automatic 3D digital patching for fragmented culture relics repair is designed. The framework includes a surface segmentation based on region dilation, feature extraction based on height‐map, pair matching and multi‐block matching.

The paper finds that the proposed 3D data patching is an efficient method for fragmented cultural relics repair.

Early and effective planning and implementation of computer‐aided fragmented cultural relics repair can significantly improve the reliability and availability of fragmented cultural relics repair.

The paper presents a uniform framework of 3D digital patching for fragmented cultural relics repair.

All disasters produce wastes of some kind, be it the trees fallen by a cyclone, a house destroyed by an earthquake, a beach coated by an oil spill, or animals killed by a flood. Postdisaster responses also produce wastes – from the human excreta of people staying in the camp to day-to-day household wastes. The issue of management of wastes created by disasters is becoming an increasingly important issue to be addressed in postdisaster response due to their scale, complexity, and cost. The cost of disaster waste management (DWM) has crossed the billion dollar mark in some of the major disasters, which is necessitating and prompting the emergence of a separate stream of expertise in DWM. In January 2011, the Joint Unit of the United Nations Environment Programme and Office for Coordination of Humanitarian Affairs (OCHA) came out with Disaster Waste Management Guidelines (2011).

WHERE are we going? The aim is to double our standard of living in the next 25 years and, as Sir Alexander Fleck, K.B.E., Chairman of Imperial Chemical Industries Ltd., so aptly staled recently, ‘The man who knows where he is going is the one who is most likely to arrive.’ One might venture to expand this statement by adding that he is still more likely to arrive if the cluttering debris of inefficient methods and movements are cleared away.

Purpose: This paper offers a first look into journalistic coverage on the enduring issue of marine litter. The presented study seeks to identify dominating news issue frames of marine pollution to analyse the prospective approaches of journalists.

Method: A content analysis of print news-of-record sources was conducted. The theoretical background of Cultural Studies and Political Consumerism Theory was employed to analyse environmental reporting in the United States and France.

Findings: The main result is that French sources focus primarily on proposed legislation and political commentary around the issue instead of ways for readers to solve the problem themselves. Journalists who assert legislation as the principal method for fighting marine debris eliminate plastic from the source. Conversely, American journalists predominantly framed the environmental threat of marine debris as a cultural issue. This individualistic approach aims to motivate privileged readers to make lifestyle changes that, notionally, will suppress global consumption of single-use plastics.

Research limitations/implications: The individualistic approach common in American news coverage aims to motivate privileged readers to make lifestyle changes that, notionally, will suppress global consumption of single-use plastics. This approach does not reflect the scientific communities overwhelming scepticism of oversimplistic solutions to this global environmental issue.

Originality/value: This foundational paper offers issue frames through which social science research on framing, rhetorical criticism and media effects of marine litter news coverage can build upon.

The objectives of this paper are to assess the sliding wear response of a zinc‐based alloy over a range of sliding speeds and pressures in oil‐lubricated condition with respect to a cast iron, to understand the role of different microconstituents in controlling the observed wear behaviour and to examine various operating material removal mechanisms.

Sliding wear tests have been carried out using a pin‐on‐disc machine in oil‐lubricated condition at different speeds and pressures. The wear response has been explained in terms of specific nature of various microconstituents of the specimen materials and substantiated through the characteristics of wear surfaces, subsurface regions and debris particles.

The wear rate increased with the sliding speed while load produced a mixed influence. Further, the friction coefficient and frictional heating were influenced by the test duration, load and speed in a mixed manner. Moreover, the zinc‐based alloy attained lower wear rate but higher friction coefficient than that of the cast iron while frictional heating followed a mixed trend.

The paper further establishes a zinc‐based alloy as a potential substitute material system to a well‐known cast iron in tribological applications and enables further understanding of the wear mechanisms.

The present paper assesses the sliding wear performance of a lighter zinc‐based alloy as an effective potential substitute material system to cast iron in tribological applications. An attempt has also been made to understand the role played by different microconstituents in controlling the wear behavior and substantiate the wear response through the characteristics of wear surfaces, subsurface regions and debris.

This paper aims to discuss the development of a defect classification system that can be used to detect and classify powder bed surface defects from captured layer images without the need for specialised computational hardware. The idea is to develop this system by making use of more traditional machine learning (ML) models instead of using computationally intensive deep learning (DL) models.

The approach that is used by this study is to use traditional image processing and classification techniques that can be applied to captured layer images to detect and classify defects without the need for DL algorithms.

The study proved that a defect classification algorithm could be developed by making use of traditional ML models with a high degree of accuracy and the images could be processed at higher speeds than typically reported in literature when making use of DL models.

This paper addresses a need that has been identified for a high-speed defect classification algorithm that can detect and classify defects without the need for specialised hardware that is typically used when making use of DL technologies. This is because when developing closed-loop feedback systems for these additive manufacturing machines, it is important to detect and classify defects without inducing additional delays to the control system.