Smart manufacturing systems: state of the art and future trends

Qu, Yuanju; Ming, Xinguo; Liu, Z. W.; Zhang, Xianyu; Hou, Zhufeng

doi:10.1007/s00170-019-03754-7

Cited by 167 publications

(77 citation statements)

References 121 publications

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“…The system guides the individual worker through the entire assembly process with digital instructions and appropriate visualization. As stated in Qu et al [30] the functions of SMSs (smart manufacturing systems) are as follows: Self-sensing function (capturing the data and the critical information from the environment), Self-organizing function (the capacity of solving the emergent requirements), Self-deciding function (data-driven decision making process in manufacturing), and Self-adaptive function. Self-adaptive function operates the behavior of SMSs' elements, which are based on the real-time sensing data and information.…”

Section: Methodsmentioning

confidence: 99%

A Smart Algorithm for Personalizing the Workstation in the Assembly Process

Turk¹,

Pipan²,

Šimić³

et al. 2020

Applied Sciences

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Due to increasing competition in the global market and to meet the need for rapid changes in product variability, it is necessary to introduce self-configurable and smart solutions within the entire process chain, including manual assembly to ensure the more efficient and ergonomic performance of the manual assembly process. This paper presents a smart assembly system including newly developed smart manual assembly workstation controlled by a smart algorithm. The smart assembly workstation is self-configurable according to the anthropometry of the individual worker, the complexity of the assembly process, the product characteristics, and the product structure. The results obtained by a case study show that is possible to organize manual assembly process with rapid adaptation of the smart assembly system to new products and workers characteristics, to achieve ergonomic working conditions through Digital Human Modelling (DHM), to minimize assembly time, and to prevent error during the assembly process. The proposed system supports the manual assembly process redesign to ensure a better working environment and aims to have an important value for applying the smart algorithms to manual assembly workstations in human-centered manufacturing systems.

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Section: Methodsmentioning

confidence: 99%

A Smart Algorithm for Personalizing the Workstation in the Assembly Process

Turk¹,

Pipan²,

Šimić³

et al. 2020

Applied Sciences

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“…As soon as the pre-configured data source connectors have been deployed, they can be executed (see number (5)). The execution of the connectors allows to extract the selected data from semantic model referenced data located in the data sources addressed in number (2) (for example monitoring data, meta data, context information or historical data along the life-cycle of I40 components).…”

Section: A Brief Introduction In the Methodology For Semantic Modmentioning

confidence: 99%

A DIN Spec 91345 RAMI 4.0 Compliant Data Pipelining Model: An Approach to Support Data Understanding and Data Acquisition in Smart Manufacturing Environments

et al. 2020

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“…L (a) (u) can be rewritten, if we suppose that there are k observations (or data) (Note that in the following, we can omit the state a, because we know it). (9) where i indexes the observations. For convenience, we can write Equation 9 more compactly using matrix notation:…”

Section: ) Conditional Linear Gaussian Bayesian Network For Regressionmentioning

confidence: 99%

Prediction for Manufacturing Factors in a Steel Plate Rolling Smart Factory Using Data Clustering-Based Machine Learning

Park¹,

Kim²,

Kim

et al. 2020

IEEE Access

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A Steel Plate Rolling Mill (SPM) is a milling machine that uses rollers to press hot slab inputs to produce ferrous or non-ferrous metal plates. To produce high-quality steel plates, it is important to precisely detect and sense values of manufacturing factors including plate thickness and roll force in each rolling pass. For example, the estimation or prediction of the in-process thickness is utilized to select the control values (e.g., roll gap) in the next pass of rolling. However, adverse manufacturing conditions can interfere with accurate detection for such manufacturing factors. Although the state-of-the-art gamma-ray camera can be used for measuring the thickness, the outputs from it are influenced by adverse manufacturing conditions such as the high temperature of plates, followed by the evaporation of lubricant water. Thus, it is inevitable that there is noise in the thickness estimation. Furthermore, installing such thickness measurements for each passing step is costly. The precision of the thickness estimation, therefore, significantly affects the cost and quality of the final product. In this paper, we present machine learning (ML) technologies and models that can be used to predict the in-process thickness in the SPM operation, so that the measurement cost for the inprocess thickness can be significantly reduced and high-quality steel plate production can be possible. To do so, we investigate most-known technologies in this application. In particular, Data Clustering based Machine Learning (DC-ML), combining clustering algorithms and supervised learning algorithms, is introduced. To evaluate DC-ML, two experiments are conducted and show that DC-ML is well suited to the prediction problems in the SPM operation. In addition, the source code of DC-ML is provided for the future study of machine learning researchers. INDEX TERMS Intelligent manufacturing systems, machine learning, regression analysis, steel industry, thickness control.

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Smart manufacturing systems: state of the art and future trends

Cited by 167 publications

References 121 publications

A Smart Algorithm for Personalizing the Workstation in the Assembly Process

A Smart Algorithm for Personalizing the Workstation in the Assembly Process

A DIN Spec 91345 RAMI 4.0 Compliant Data Pipelining Model: An Approach to Support Data Understanding and Data Acquisition in Smart Manufacturing Environments

Prediction for Manufacturing Factors in a Steel Plate Rolling Smart Factory Using Data Clustering-Based Machine Learning

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