Smart Manufacturing

This book is a collection of 11 articles that are published in the corresponding Machines Special Issue “Smart Manufacturing”. It represents the quality, breadth and depth of the most updated study in smart manufacturing (SM); in particular, digital technologies are deployed to enhance system smartness by (1) empowering physical resources in production, (2) utilizing virtual and dynamic assets over the Internet to expand system capabilities, (3) supporting data-driven decision-making activities at various domains and levels of businesses, or (4) reconfiguring systems to adapt to changes and uncertainties. System smartness can be evaluated by one or a combination of performance metrics such as degree of automation, cost-effectiveness, leanness, robustness, flexibility, adaptability, sustainability, and resilience. This book features, firstly, the concepts digital triad (DT-II) and Internet of digital triad things (IoDTT), proposed to deal with the complexity, dynamics, and scalability of complex systems simultaneously. This book also features a comprehensive survey of the applications of digital technologies in space instruments; a systematic literature search method is used to investigate the impact of product design and innovation on the development of space instruments. In addition, the survey provides important information and critical considerations for using cutting edge digital technologies in designing and manufacturing space instruments

Smart Manufacturing—Theories, Methods, and Applications

(First paragraph) Smart manufacturing (SM) distinguishes itself from other system paradigms by introducing ‘smartness’ as a measure to a manufacturing system; however, researchers in different domains have different expectations of system smartness from their own perspectives. In this Special Issue (SI), SM refers to a system paradigm where digital technologies are deployed to enhance system smartness by (1) empowering physical resources in production, (2) utilizing virtual and dynamic assets over the internet to expand system capabilities, (3) supporting data-driven decision making at all domains and levels of businesses, or (4) reconfiguring systems to adapt changes and uncertainties in dynamic environments. System smartness is measured by one or a combination of system performance metrics, such as the degree of automation, cost-effectiveness, leanness, robustness, flexibility, adaptability, sustainability, and resilience. This SI aims to present the most representative works in advancing the theories, methods, and applications of SM

Component criticality analysis for improved ship machinery reliability

Redundancy in ship systems is provided to ensure operational resilience through equipment backups, which ensure system availability and offline repairs of machinery. The electric power generation system of ships provides the most utility of all systems; hence, it is provided with a good level of standby units to ensure reliable operations. Nonetheless, the occurrence of undesired blackouts is common onboard ships and portends a serious danger to ship security and safety. Therefore, understanding the contributing factors affecting system reliability through component criticality analysis is essential to ensuring a more robust maintenance and support platform for efficient ship operations. In this regard, a hybrid reliability and fault detection analysis using DFTA and ANN was conducted to establish component criticality and related fault conditions. A case study was conducted on a ship power generation system consisting of four marine diesel power generation plants onboard an Offshore Patrol Vessel (OPV). Results from the reliability analysis indicate an overall low system reliability of less than 70 percent within the first 24 of the 78 operational months. Component criticality-using reliability importance measures obtained through DFTA was used to identify all components with more than a 40 percent contribution to subsystem failure. Additionally, machine learning was used to aid the reliability analysis through feature engineering and fault identification using Artificial Neural Network classification. The ANN has identified a failure pattern threshold at about 200 kva, which can be attributed to overheating, hence establishing a link between component failure and generator performance