A Classification Scheme for Smart Manufacturing Systems’ Performance Metrics

Lee, Y. T.; Kumaraguru, Senthilkumaran; Jain, Sanjay; Robinson, Stefanie L.; Helu, Moneer; Hatim, Qais; Rachuri, Sudarsan; Dornfeld, David; Saldaña, Christopher; Kumara, Soundar

doi:10.1520/ssms20160012

Cited by 30 publications

(18 citation statements)

References 43 publications

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“…17 Agility, asset utilization and sustainability were considered as the metrics for the classification of SMS. 18 Similarly, there are other characteristics and technologies that have been used to define SM. Four steps toward SM have also been mentioned: 19 (1) establish forums where problem definitions can be discussed, (2) develop cyber-platforms, (3) data sharing and (4) introduce SM-friendly policies.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart manufacturing: Characteristics, technologies and enabling factors

Mittal

Khan

Romero

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part B:

405

261

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The purpose of this article is to collect and structure the various characteristics, technologies and enabling factors available in the current body of knowledge that are associated with smart manufacturing. Eventually, it is expected that this selection of characteristics, technologies and enabling factors will help compare and distinguish other initiatives such as Industry 4.0, cyber-physical production systems, smart factory, intelligent manufacturing and advanced manufacturing, which are frequently used synonymously with smart manufacturing. The result of this article is a comprehensive list of such characteristics, technologies and enabling factors that are regularly associated with smart manufacturing. This article also considers principles of ''semantic similarity'' to establish the basis for a future smart manufacturing ontology, since it was found that many of the listed items show varying overlaps; therefore, certain characteristics and technologies are merged and/or clustered. This results in a set of five defining characteristics, 11 technologies and three enabling factors that are considered relevant for the smart manufacturing scope. This article then evaluates the derived structure by matching the characteristics and technology clusters of smart manufacturing with the design principles of Industry 4.0 and cyber-physical systems. The authors aim to provide a solid basis to start a broad and interdisciplinary discussion within the research and industrial community about the defining characteristics, technologies and enabling factors of smart manufacturing.

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Section: Literature Reviewmentioning

confidence: 99%

Smart manufacturing: Characteristics, technologies and enabling factors

Mittal

Khan

Romero

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part B:

405

261

View full text Add to dashboard Cite

show abstract

“…I represent the input of neurons in hidden The input layer is used for data input, the hidden layer is used for processing data, and the output layer is used for outputting results. If the network has an input signal, the input signal is first sent to the hidden layer node and then sent to the output layer node of the next hidden layer [13]. When the signal reaches the output layer, the algorithm will correct the weights and thresholds, and the calculation process is shown in Formulas (3) and (4).…”

Section: Principles Of Mathematical Intelligentmentioning

confidence: 99%

Application of Mathematical Economic Model in Financial System in Manufacturing Industry

Tang

2021

Wireless Communications and Mobile Computing

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In the field of economic research, most of the sample data is not obtained based on controllable experiments but generated during the normal operation of the economic system. Therefore, the change of an economic variable is usually not caused by a single change of a cause variable. It is the result of a combination of multiple factors. Therefore, it is necessary to study the application of mathematical intelligent computing in computer intelligent manufacturing system. The purpose of this paper is to explore the application of mathematical intelligent computing in computer intelligent manufacturing system. For this reason, this paper uses the furnace temperature control model to carry out simulation experiment. In this simulation experiment, three algorithms of mathematical intelligent computing are mainly used, including BPES intelligent computing method, genetic algorithm, and MARS algorithm. The research results show that the superparameter optimization based on MARS has high efficiency, and the best result, the worst result, the average result, the variance, and the average time of multiple independent runs are controlled below 0.03 s. In this experiment, when the hidden layer node is 9, the prediction error value is the smallest, and the model simulation curve is basically consistent with the measured curve trend. In the simulation experiment of this paper, these three algorithms have shown good results in their respective links.

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“…Chris Evans of Mitsubishi Electric 3 defines “smartness” in manufacturing as the use of transformed information gathered as data during manufacturing processes to make better decisions that enhance productivity and efficacy while reducing waste, energy consumption, and production lead time. Another definition, used by Sudarsan Rachuri of the US Department of Energy Advanced Manufacturing Office based on Lee et al, 4 is the use of effective, secure human‐system platforms to improve decision‐making and the overall productivity and efficiency of manufacturing across a networked enterprise.…”

Section: Smart Manufacturing and Internet Of Thingsmentioning

confidence: 99%

Enhancing operational performance and productivity benefits in breweries through smart manufacturing technologies

Nimbalkar

Supekar

Meadows

et al. 2020

J Adv Manuf & Process

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This article uses case studies to demonstrate the potential of smart manufacturing (SM) and Internet of Things (IoT) technologies to enhance operational performance and productivity in industry. The analysis highlights benefits such as cost reduction, production flexibility, shorter product time-to-market, energy/ water efficiency and environmental impact reduction, and increased productivity. To illustrate the effectiveness of SM and IoT approaches, the authors sought out manufacturers currently implementing or seeking to implement SM and IoT technologies. The authors identified beer brewing (NAICS Code 312120) as a rapidly expanding industry whose members appear eager to implement SM technologies to optimize production lines by revealing bottlenecks and identifying performance-reducing nodes. This article presents two case studies based on SM and IoT technologies in breweries. It briefly describes a systematic framework introduced elsewhere by the authors and uses it to assess the energy productivity and competitiveness of SM applications in breweries. This article addresses questions concerning the information and communications technology infrastructure needed to build smart breweries and how corporations simplify the installation and deployment of SM and IoT components. This article was originally published in the

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A Classification Scheme for Smart Manufacturing Systems’ Performance Metrics

Cited by 30 publications

References 43 publications

Smart manufacturing: Characteristics, technologies and enabling factors

Smart manufacturing: Characteristics, technologies and enabling factors

Application of Mathematical Economic Model in Financial System in Manufacturing Industry

Enhancing operational performance and productivity benefits in breweries through smart manufacturing technologies

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