9,431 research outputs found

    A systematic mapping of the advancing use of machine learning techniques for predictive maintenance in the manufacturing sector

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    The increasing availability of data, gathered by sensors and intelligent machines, is chang-ing the way decisions are made in the manufacturing sector. In particular, based on predictive approach and facilitated by the nowadays growing capabilities of hardware, cloud-based solutions, and new learning approaches, maintenance can be scheduled—over cell engagement and resource monitoring—when required, for minimizing (or managing) unexpected equipment failures, improving uptime through less aggressive maintenance schedules, shortening unplanned downtime, reducing excess (direct and indirect) cost, reducing long-term damage to machines and processes, and improve safety plans. With access to increased levels of data (and over learning mechanisms), companies have the capability to conduct statistical tests using machine learning algorithms, in order to uncover root causes of problems previously unknown. This study analyses the maturity level and contributions of machine learning methods for predictive maintenance. An upward trend in publications for predictive maintenance using machine learning techniques was identified with the USA and China leading. A mapping study—steady set until early 2019 data—was employed as a formal and well-structured method to synthesize material and to report on pervasive areas of research. Type of equipment, sensors, and data are mapped to properly assist new researchers in positioning new research activities in the domain of smart maintenance. Hence, in this paper, we focus on data-driven methods for predictive maintenance (PdM) with a comprehensive survey on applications and methods until, for the sake of commenting on stable proposal, 2019 (early included). An equal repartition between evaluation and validation studies was identified, this being a symptom of an immature but growing research area. In addition, the type of contribution is mainly in the form of models and methodologies. Vibrational signal was marked as the most used data set for diagnosis in manufacturing machinery monitoring; furthermore, supervised learning is reported as the most used predictive approach (ensemble learning is growing fast). Neural networks, followed by random forests and support vector machines, were identified as the most applied methods encompassing 40% of publications, of which 67% related to deep neural network with long short-term memory predominance. Notwithstanding, there is no robust approach (no one reported optimal performance over different case tests) that works best for every problem. We finally conclude the research in this area is moving fast to gather a separate focused analysis over the last two years (whenever stable implementations will appear)

    Data mining in manufacturing: a review based on the kind of knowledge

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    In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

    Yield and Reliability Analysis for Nanoelectronics

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    As technology has continued to advance and more break-through emerge, semiconductor devices with dimensions in nanometers have entered into all spheres of our lives. Accordingly, high reliability and high yield are very much a central concern to guarantee the advancement and utilization of nanoelectronic products. However, there appear to be some major challenges related to nanoelectronics in regard to the field of reliability: identification of the failure mechanisms, enhancement of the low yields of nano products, and management of the scarcity and secrecy of available data [34]. Therefore, this dissertation investigates four issues related to the yield and reliability of nanoelectronics. Yield and reliability of nanoelectronics are affected by defects generated in the manufacturing processes. An automatic method using model-based clustering has been developed to detect the defect clusters and identify their patterns where the distribution of the clustered defects is modeled by a new mixture distribution of multivariate normal distributions and principal curves. The new mixture model is capable of modeling defect clusters with amorphous, curvilinear, and linear patterns. We evaluate the proposed method using both simulated and experimental data and promising results have been obtained. Yield is one of the most important performance indexes for measuring the success of nano fabrication and manufacturing. Accurate yield estimation and prediction is essential for evaluating productivity and estimating production cost. This research studies advanced yield modeling approaches which consider the spatial variations of defects or defect counts. Results from real wafer map data show that the new yield models provide significant improvement in yield estimation compared to the traditional Poisson model and negative binomial model. The ultra-thin SiO2 is a major factor limiting the scaling of semiconductor devices. High-k gate dielectric materials such as HfO2 will replace SiO2 in future generations of MOS devices. This study investigates the two-step breakdown mechanisms and breakdown sequences of double-layered high-k gate stacks by monitoring the relaxation of the dielectric films. The hazard rate is a widely used metric for measuring the reliability of electronic products. This dissertation studies the hazard rate function of gate dielectrics breakdown. A physically feasible failure time distribution is used to model the time-to-breakdown data and a Bayesian approach is adopted in the statistical analysis

    Review of Health Prognostics and Condition Monitoring of Electronic Components

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    To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted

    Patent reform: a mixed blessing for the U.S. economy?

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    The 1980s represented a period of dramatic change in the design and enforcement of U.S. intellectual property law. Many of these changes were adopted in the hopes of stimulating private research and development and improving the technological competitiveness of American industries. This article examines the effects of an especially important aspect of these changes: many more inventions qualify for patent protection than before. While it seems logical that making patents easier to obtain will encourage more inventive activity, economic analysis reveals this is not always true, and it is less likely to be true in industries that innovate rapidly.Patents

    AI/ML Algorithms and Applications in VLSI Design and Technology

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    An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

    Smart Sustainable Manufacturing Systems

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    With the advent of disruptive digital technologies, companies are facing unprecedented challenges and opportunities. Advanced manufacturing systems are of paramount importance in making key enabling technologies and new products more competitive, affordable, and accessible, as well as for fostering their economic and social impact. The manufacturing industry also serves as an innovator for sustainability since automation coupled with advanced manufacturing technologies have helped manufacturing practices transition into the circular economy. To that end, this Special Issue of the journal Applied Sciences, devoted to the broad field of Smart Sustainable Manufacturing Systems, explores recent research into the concepts, methods, tools, and applications for smart sustainable manufacturing, in order to advance and promote the development of modern and intelligent manufacturing systems. In light of the above, this Special Issue is a collection of the latest research on relevant topics and addresses the current challenging issues associated with the introduction of smart sustainable manufacturing systems. Various topics have been addressed in this Special Issue, which focuses on the design of sustainable production systems and factories; industrial big data analytics and cyberphysical systems; intelligent maintenance approaches and technologies for increased operating life of production systems; zero-defect manufacturing strategies, tools and methods towards online production management; and connected smart factories
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