Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. As an emerging infrastructure, cloud computing provides new opportunities to achieve the goals of advanced manufacturing. This paper reviews the historical development of prognosis theories and techniques and projects their future growth enabled by the emerging cloud infrastructure. Techniques for cloud computing are highlighted, as well as the influence of these techniques on the paradigm of cloud-enabled prognosis for manufacturing. Finally, this paper discusses the envisioned architecture and associated challenges of cloud-enabled prognosis for manufacturing

Prognostics and health management (PHM) technologies reduce time and costs for maintenance of products or processes through efficient and cost-effective diagnostic and prognostic activities. PHM systems use real-time and historical state information of subsystems and components to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. However, PHM is still an emerging field, and much of the published work has been either too exploratory or too limited in scope. Future smart manufacturing systems will require PHM capabilities that overcome current challenges, while meeting future needs based on best practices, for implementation of diagnostics and prognostics. This paper reviews the challenges, needs, methods, and best practices for PHM within manufacturing systems. This includes PHM system development of numerous areas highlighted by diagnostics, prognostics, dependability analysis, data management, and business. Based on current capabilities, PHM systems are shown to benefit from open-system architectures, cost-benefit analyses, method verification and validation, and standards.

Abstract-A life-cycle energy consumption analysis of aBridgeport manual mill and a Mori Seiki DuraVertical 5060 has been conducted. The use phase incorporated three manufacturing environments: a community shop, a job shop, and a commercial facility. The CO 2 -equivalent emissions were presented per machined part. While the use phase comprised the majority of the overall emissions, the manufacturing phase emissions were significant especially for the job shop, which is not as efficient as the other facilities due to its inherent need for flexibility. Since the Mori Seiki is heavier, the manufacturing phase of this machine tool had a greater impact on emissions than the Bridgeport. Transportation was small relative to the use phase, which was dominated by cutting, HVAC, and lighting. These results highlight areas for energy reductions in machine tool design as well as the importance of facility type to the manufacture of any product.

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The increasing growth of digital technologies in manufacturing has provided industry with opportunities to improve its productivity and operations. One such opportunity is the digital thread, which links product lifecycle systems so that shared data may be used to improve design and manufacturing processes. The development of the digital thread has been challenged by the inherent difficulty of aggregating and applying context to data from heterogeneous systems across the product lifecycle. This paper presents a reference four-tiered architecture designed to manage the data generated by manufacturing systems for the digital thread. The architecture provides segregated access to internal and external clients, which protects intellectual property and other sensitive information, and enables the fusion of manufacturing and other product lifecycle data. We have implemented the architecture with a contract manufacturer and used it to generate knowledge and identify performance improvement opportunities that would otherwise be unobservable to a manufacturing decision maker.