A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closedform mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR

Spatiotemporal dynamics and functional characteristics of the composition of the main fungal taxa in the root microhabitat of Calanthe sieboldii (Orchidaceae)

Abstract Background Endophytic fungi play a critical ecological role in the growth and development of orchids, but little is known about the spatial and temporal dynamics of fungal diversity or the ecological functions of fungi during orchid growth and reproduction. Calanthe sieboldii Decne. is listed in the Chinese National Key Protected Wild Plants as a class I protected wild plant. To understand the community characteristics of root and soil fungi of the orchid during its reproductive seasons, we investigated the community composition, spatial and temporal dynamics, and functional characteristics of the orchid microhabitat fungi by using diversity and ecological functional analyses. Results We discovered that there were three, seven, and four dominant fungal families in the orchid's roots, rhizoplane soil, and rhizosphere soil, respectively. Tulasnellaceae, Aspergillaceae, and Tricholomataceae were the dominant fungi in this endangered orchid's microhabitats. The closer the fungal community was to the orchid, the more stable and the less likely the community composition to change significantly over time. The fungal communities of this orchid's roots and rhizoplane soil varied seasonally, while those of the rhizosphere soil varied interannually. Saprophytic fungi were the most abundant in the orchid's fungal community, and the closer the distance to the orchid, the more symbiotic fungi were present. Conclusions The fungi in different parts of the root microhabitat of C. sieboldii showed different spatiotemporal dynamic patterns. The fungal community near the orchid roots was relatively stable and displayed seasonal variation, while the community further away from the roots showed greater variation. In addition, compared with the soil fungi, the dominant endophytic fungi were more stable, and these may be key fungi influencing orchid growth and development. Our study on the spatiotemporal dynamics and functions of fungi provides a basis for the comprehensive understanding and utilization of orchid endophytic fungi

A facile synthesis of perforated reduced graphene oxide for high performance electrochemical sensors

Highly active perforated reduced graphene oxide (P-rGO) was synthesized by a facile methodology based on co-deposition of graphene oxide with sacrificial Prussian blue. Electrode surface properties were characterized by SEM and EDS. The GC/P-rGO electrode exhibited a larger specific surface area than that of GCE. These findings highlighted that the signal was enhanced for both dopamine detection and selenium detection by using P-rGO as a relevant supporting substrate. The result indicated that the large number of perforated structures formed numerous electrically conductive channels in the structure, improving the electrocatalytic properties

Cloud-Based Parallel Machine Learning For Tool Wear Prediction

The emergence of cloud computing, industrial internet of things (IIoT), and new machine learning techniques have shown the potential to advance prognostics and health management (PHM) in smart manufacturing. While model-based PHM techniques provide insight into the progression of faults in mechanical components, certain assumptions on the underlying physical mechanisms for fault development are required to develop predictive models. In situations where there is a lack of adequate prior knowledge of the underlying physics, data-driven PHM techniques have been increasingly applied in the field of smart manufacturing. One of the limitations of current data-driven methods is that large volumes of training data are required to make accurate predictions. Consequently, computational efficiency remains a primary challenge, especially when large volumes of sensor-generated data need to be processed in real-time applications. The objective of this research is to introduce a cloud-based parallel machine learning algorithm that is capable of training large-scale predictive models more efficiently. The random forests (RFs) algorithm is parallelized using the MapReduce data processing scheme. The MapReduce-based parallel random forests (PRFs) algorithm is implemented on a scalable cloud computing system with varying combinations of processors and memories. The effectiveness of this new method is demonstrated using condition monitoring data collected from milling experiments. By implementing RFs in parallel on the cloud, a significant increase in the processing speed (14.7 times in terms of increase in training time) has been achieved, with a high prediction accuracy of tool wear (eight times in terms of reduction in mean squared error (MSE))

Deep Learning For Smart Manufacturing: Methods And Applications

Smart manufacturing refers to using advanced data analytics to complement physical science for improving system performance and decision making. With the widespread deployment of sensors and Internet of Things, there is an increasing need of handling big manufacturing data characterized by high volume, high velocity, and high variety. Deep learning provides advanced analytics tools for processing and analysing big manufacturing data. This paper presents a comprehensive survey of commonly used deep learning algorithms and discusses their applications toward making manufacturing “smart”. The evolvement of deep learning technologies and their advantages over traditional machine learning are firstly discussed. Subsequently, computational methods based on deep learning are presented specially aim to improve system performance in manufacturing. Several representative deep learning models are comparably discussed. Finally, emerging topics of research on deep learning are highlighted, and future trends and challenges associated with deep learning for smart manufacturing are summarized

Discussion on the exploration & development prospect of shale gas in the Sichuan Basin

The Sichuan Basin, a hotspot and one of the most successful areas for shale gas exploration and development, can largely reflect and have a big say in the future prospect of shale gas in China. Through an overall review on the progress in shale gas exploration and development in the Sichuan Basin, we obtained the following findings: (1) the Sichuan Basin has experienced the marine and terrestrial depositional evolution, resulting in the deposition of three types of organic-matter-rich shales (i.e. marine, transitional, and terrestrial), and the occurrence of six sets of favorable shale gas enrichment strata (i.e. the Sinian Doushantuo Fm, the Cambrian Qiongzhusi Fm, the Ordovician Wufeng–Silurian Longmaxi Fm, the Permian Longtan Fm, the Triassic Xujiahe Fm, and the Jurassic Zhiliujing Fm); (2) the five key elements for shale gas accumulation in the Wufeng-Longmaxi Fm are deep-water shelf facies, greater thickness of organic-rich shales, moderate thermal evolution, abundant structural fractures, reservoir overpressure; and (3) the exploration and development of shale gas in this basin still confronts two major challenges, namely, uncertain sweet spots and potential prospect of shale gas, and the immature technologies in the development of shale gas resources at a depth of more than 3500 m. In conclusion, shale gas has been discovered in the Jurassic, Triassic and Cambrian, and preliminary industrial-scale gas has been produced in the Ordovician-Silurian Fm in the Sichuan Basin, indicating a promising prospect there; commercial shale gas can be produced there with an estimated annual gas output of 30–60 billion m3; and shale gas exploration and production experiences in this basin will provide valuable theoretical and technical support for commercial shale gas development in China

Quantitative Characterization of Micro-Scale Pore-Throat Heterogeneity in Tight Sandstone Reservoir

Nanoscale pore-throat systems are widely developed in the pore-throat of tight reservoirs. The pore-throat structures of different microscales are complex and diverse with obvious microscale effects. Taking the Chang 63 tight sandstone reservoir of the Huaqing area in Ordos basin as an example, under the guidance of information entropy theory, the quantitative characterization model of pore-throat micro-scale heterogeneity in a tight oil reservoir is established based on casting thin sections, physical properties analysis, constant velocity mercury injection, and NMR technology. Moreover, the correlation between pore-throat heterogeneity and porosity, permeability and movable fluid saturation is analyzed. The results show that there are obvious differences in pore-throat heterogeneity between different reservoirs, and the throat uniformity of macro pore-fine-throat reservoir, macro pore–micro throat reservoir, and macro pore–micro throat reservoir becomes worse, successively. There is a negative correlation between porosity uniformity and porosity, permeability and movable fluid saturation. However, there is a positive correlation between throat uniformity and combined pore throat uniformity and porosity, permeability and movable fluid saturation. Therefore, the uniformity of the throat controls the seepage capacity and fluid mobility in the pore system of the Chang 63 tight sandstone reservoir in the study area. This has important theoretical and practical significance to enhance oil recovery and promote the efficient development of a tight oil and gas reservoir

Quantitative Characterization of Micro-Scale Pore-Throat Heterogeneity in Tight Sandstone Reservoir

Nanoscale pore-throat systems are widely developed in the pore-throat of tight reservoirs. The pore-throat structures of different microscales are complex and diverse with obvious microscale effects. Taking the Chang 63 tight sandstone reservoir of the Huaqing area in Ordos basin as an example, under the guidance of information entropy theory, the quantitative characterization model of pore-throat micro-scale heterogeneity in a tight oil reservoir is established based on casting thin sections, physical properties analysis, constant velocity mercury injection, and NMR technology. Moreover, the correlation between pore-throat heterogeneity and porosity, permeability and movable fluid saturation is analyzed. The results show that there are obvious differences in pore-throat heterogeneity between different reservoirs, and the throat uniformity of macro pore-fine-throat reservoir, macro pore–micro throat reservoir, and macro pore–micro throat reservoir becomes worse, successively. There is a negative correlation between porosity uniformity and porosity, permeability and movable fluid saturation. However, there is a positive correlation between throat uniformity and combined pore throat uniformity and porosity, permeability and movable fluid saturation. Therefore, the uniformity of the throat controls the seepage capacity and fluid mobility in the pore system of the Chang 63 tight sandstone reservoir in the study area. This has important theoretical and practical significance to enhance oil recovery and promote the efficient development of a tight oil and gas reservoir