Risk Analysis Using Job Safety Analysis-Fuzzy Integration for Ship Maintenance Operation

Shipyard is an industry engaged in the maintenance and repair of ships and constructing a new ship. In ship repair operations, there are many activities in this operation. Propeller inspection, blasting, replating, welding, general work, electric work is an activity in ship repair operation. This research proposed a methodology to risk analysis ship maintenance operation, integrating Job Safety Analysis (JSA) with Bayesian Network (BN) and Fuzzy Inferences System (FIS). JSA method is used to find the hazards and the consequences of the maintenance operation. BN is developed for probability calculating of likelihood factors. Meanwhile, the FIS is used as a method to calculate the risk level. The FIS using the Mamdani algorithm based on the expert’s knowledge and experience. The integration of three methods is used to complete the risk assessment for replating activities. The proposed method is used to find out the risk level of replating activity on ship maintenance. Based on the result, the proposed model is more accurate, precise, and flexible depending on the basic factor that influences the operation. It would help to reduce the potential accident on the operation. This proposed method could be the other option as a tool to calculate risk assessment in other operations

Application of ”ART SIMULATOR” for Manufacturing Similarity Identification in Group Technology Design - Chapter 10

This chapter 10 carried out the exceptional implementation of ART-1 neural network in the analysis of the manufacturing similarity of the cylindrical parts within the group technology design. Established concept of the group technology design begins from the complex part of the group or the group representative. Group representative has all the geometrical elements of the parts in group, and manufacturing procedure may be applied to the machining of any part in the group. The complex part may be realistic or a hypothetical one. The ART-1 artificial neural network provided manufacturing classification according to the geometrical similarities of work-pieces for the group of cylindrical parts. For the manufacturing similarity identification within the group technology design, software package "ART Simulator" is developed and presented in this chapter

Damage detection and damage evolution monitoring of composite materials for naval applications using acoustic emission testing

Maritime transport has profound importance for the world economy. Vessels of all sizes constantly transport large numbers of passengers and goods across the sea, often under adverse operational conditions. Vessels need to exhibit high levels of reliability, availability, maintainability and safety (RAMS). However, at the same time their performance needs to be optimised ensuring the lowest possible fuel consumption with the maximum operational capacity and range without compromising RAMS. Sweating of naval assets and profitability should be maximised for the operator ensuring investment in future projects and supporting the growth of maritime transport and world economy as a whole. Vessels have been traditionally manufactured using naval steel grades such AH, DH and EH. Smaller leisure and specialised purpose vessels such as patrol boats, etc. have been built using fibre-reinforced composite (FRC) materials. This trend is gradually penetrating the market of larger commercial vessels including freight and cruise ships. However, these are still the early days and further investigation of the optimum FRC manufacturing techniques and mechanical properties together with an in-depth understanding of the damage mechanics are required before such materials can become more commonplace. This project has investigated different glass FRCs using different manufacturing techniques. Glass fibres are preferred due to their lower cost in comparison with carbon fibres. The use of carbon FRCs in maritime applications is limited to the fabrication of racing and high performance speedboat vessels. Samples manufactured under laboratory conditions have been compared with those manufactured by a shipyard. It has been seen that the in-house samples had generally superior performance. Steel-to-composite joints have also been assessed including different designs. The effect of different features in the design such as drilled holes and bolts on the mechanical performance of the manufactured samples has also been evaluated. The damage mechanisms involved during damage propagation and features causing damage initiation have been considered. Damage initiation and subsequent evolution have been monitored using acoustic emission (AE). Various signal processing approaches have been employed (manual and automatic) for optimum evaluation of the AE data obtained in a semiquantitative manner. It has been shown that AE could be applied effectively for structural health monitoring of naval structures in the field. Several factors and parameters that need to be considered during acquisition and analysis have been successfully determined. The key results of the study together with mechanical testing and characterisation of samples employed are presented in summarised form within the present thesis

New Trends in the Use of Artificial Intelligence for the Industry 4.0

Industry 4.0 is based on the cyber-physical transformation of processes, systems and methods applied in the manufacturing sector, and on its autonomous and decentralized operation. Industry 4.0 reflects that the industrial world is at the beginning of the so-called Fourth Industrial Revolution, characterized by a massive interconnection of assets and the integration of human operators with the manufacturing environment. In this regard, data analytics and, specifically, the artificial intelligence is the vehicular technology towards the next generation of smart factories.Chapters in this book cover a diversity of current and new developments in the use of artificial intelligence on the industrial sector seen from the fourth industrial revolution point of view, namely, cyber-physical applications, artificial intelligence technologies and tools, Industrial Internet of Things and data analytics. This book contains high-quality chapters containing original research results and literature review of exceptional merit. Thus, it is in the aim of the book to contribute to the literature of the topic in this regard and let the readers know current and new trends in the use of artificial intelligence for the Industry 4.0

Assessing the suitability of ship design for human factors issues associated with evacuation and normal operations

Evaluating ship layout for human factors (HF) issues using simulation software such as maritimeEXODUS can be a long and complex process. The analysis requires the identification of relevant evaluation scenarios; encompassing evacuation and normal operations; the development of appropriate measures which can be used to gauge the performance of crew and vessel and finally; the interpretation of considerable simulation data. In this paper we present a systematic and transparent methodology for assessing the HF performance of ship design which is both discriminating and diagnostic. The methodology is demonstrated using two variants of a hypothetical naval ship

Utilising a modern quality function deployment process in ship modularisation

Rising passenger numbers in the leisure cruise industry has resulted in cruise ships sailing in full capacity. This trend is estimated to grow further by 42% until 2027 causing the cruise companies to order around 120 new ships to be delivered by 2027. The increasing order numbers have put most of the shipyards around the world that build cruise ships to run in full capacity. However, to increase their productivity while staying competitive, the shipyards have to take a different approach to build ships. One such approach is building modularly designed ships. This thesis study forms a part of the case company’s efforts to explore the modular ship design process by utilising a tailor-made Quality Function Deployment approach. Specifically, the study looks into finding a robust approach of generating product requirements by incorporating customers’ desires and wishes with the help of the QFD process recommended by the newly standardised ISO 16355 series of standards. Study of the modular design process, the case company’s internal design process along with the QFD approach recommended by the ISO standard, the author creates a draft QFD process, which is tested out in the shipyard along with the technical experts to get insights on the draft approach. The study also analyses the suggestions to the classical QFD by the ISO documents and recommends the better alternative since not many case studies have been made using the ISO recommended QFD approach. The feedback obtained along with the observations made helped to create a robust tailored QFD approach for the case company to incorporate in their modular product development efforts. Further, the author recommends solutions to eliminate or reduce the impact of the challenges the case company might face while implementing the recommended QFD approach in the new modular design process

Can we Pretrain a SotA Legal Language Model on a Budget From Scratch?

Even though many efficient transformers have been proposed, only few such models are available for specialized domains. Additionally, since the pretraining process is extremely costly in general – but even more so as the sequence length increases – it is often only in reach of large research labs. One way of making pretraining cheaper is the Replaced Token Detection (RTD) task, by providing more signal during training compared to MLM, since the loss can be computed over all tokens. In this work, we train Longformer models with the efficient RTD task on long-context legal data to showcase that pretraining efficient LMs is possibl using less than 12 GPU days. We evaluate the trained models on challenging summarization tasks requiring the model to summarize complex long texts. We find that both the small and base models outperform their baselines on the in-domain BillSum and out-of-domain PubMed tasks in their respective parameter range. We publish our models as a resource for researcher and practitioners

ARCHITECTURE FOR A CBM+ AND PHM CENTRIC DIGITAL TWIN FOR WARFARE SYSTEMS

The Department of the Navy’s continued progression from time-based maintenance into condition-based maintenance plus (CBM+) shows the importance of increasing operational availability (Ao) across fleet weapon systems. This capstone uses the concept of digital efficiency from a digital twin (DT) combined with a three-dimensional (3D) direct metal laser melting printer as the physical host on board a surface vessel. The DT provides an agnostic conduit for combining model-based systems engineering with a digital analysis for real-time prognostic health monitoring while improving predictive maintenance. With the DT at the forefront of prioritized research and development, the 3D printer combines the value of additive manufacturing with complex systems in dynamic shipboard environments. To demonstrate that the DT possesses parallel abilities for improving both the physical host’s Ao and end-goal mission, this capstone develops a DT architecture and a high-level model. The model focuses on specific printer components (deionized [DI] water level, DI water conductivity, air filters, and laser motor drive system) to demonstrate the DT’s inherent effectiveness towards CBM+. To embody the system of systems analysis for printer suitability and performance, more components should be evaluated and combined with the ship’s environment data. Additionally, this capstone recommends the use of DTs as a nexus into more complex weapon systems while using a deeper level of design of experiment.Outstanding ThesisCivilian, Department of the NavyCommander, United States NavyCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyApproved for public release. Distribution is unlimited

Innovation in manufacturing through digital technologies and applications: Thoughts and Reflections on Industry 4.0

The rapid pace of developments in digital technologies offers many opportunities to increase the efficiency, flexibility and sophistication of manufacturing processes; including the potential for easier customisation, lower volumes and rapid changeover of products within the same manufacturing cell or line. A number of initiatives on this theme have been proposed around the world to support national industries under names such as Industry 4.0 (Industrie 4.0 in Germany, Made-in-China in China and Made Smarter in the UK). This book presents an overview of the state of art and upcoming developments in digital technologies pertaining to manufacturing. The starting point is an introduction on Industry 4.0 and its potential for enhancing the manufacturing process. Later on moving to the design of smart (that is digitally driven) business processes which are going to rely on sensing of all relevant parameters, gathering, storing and processing the data from these sensors, using computing power and intelligence at the most appropriate points in the digital workflow including application of edge computing and parallel processing. A key component of this workflow is the application of Artificial Intelligence and particularly techniques in Machine Learning to derive actionable information from this data; be it real-time automated responses such as actuating transducers or informing human operators to follow specified standard operating procedures or providing management data for operational and strategic planning. Further consideration also needs to be given to the properties and behaviours of particular machines that are controlled and materials that are transformed during the manufacturing process and this is sometimes referred to as Operational Technology (OT) as opposed to IT. The digital capture of these properties and behaviours can then be used to define so-called Cyber Physical Systems. Given the power of these digital technologies it is of paramount importance that they operate safely and are not vulnerable to malicious interference. Industry 4.0 brings unprecedented cybersecurity challenges to manufacturing and the overall industrial sector and the case is made here that new codes of practice are needed for the combined Information Technology and Operational Technology worlds, but with a framework that should be native to Industry 4.0. Current computing technologies are also able to go in other directions than supporting the digital ‘sense to action’ process described above. One of these is to use digital technologies to enhance the ability of the human operators who are still essential within the manufacturing process. One such technology, that has recently become accessible for widespread adoption, is Augmented Reality, providing operators with real-time additional information in situ with the machines that they interact with in their workspace in a hands-free mode. Finally, two linked chapters discuss the specific application of digital technologies to High Pressure Die Casting (HDPC) of Magnesium components. Optimizing the HPDC process is a key task for increasing productivity and reducing defective parts and the first chapter provides an overview of the HPDC process with attention to the most common defects and their sources. It does this by first looking at real-time process control mechanisms, understanding the various process variables and assessing their impact on the end product quality. This understanding drives the choice of sensing methods and the associated smart digital workflow to allow real-time control and mitigation of variation in the identified variables. Also, data from this workflow can be captured and used for the design of optimised dies and associated processes

Proceedings of the 4th International Conference on Innovations in Automation and Mechatronics Engineering (ICIAME2018)

The Mechatronics Department (Accredited by National Board of Accreditation, New Delhi, India) of the G H Patel College of Engineering and Technology, Gujarat, India arranged the 4th International Conference on Innovations in Automation and Mechatronics Engineering 2018, (ICIAME 2018) on 2-3 February 2018. The papers presented during the conference were based on Automation, Optimization, Computer Aided Design and Manufacturing, Nanotechnology, Solar Energy etc and are featured in this book