Development of tools utilization monitoring system on labor-intensive manufacturing industries

Harja, Herman Budi; Prakosa, Tri; Raharno, Sri; Yuwana, Yatna; Nurhadi, Indra; Hartono, R. W. Tri; Zulfahmi, Muhammad; Pane, M Y P; Yusuf, Moshood A.

doi:10.1063/1.5138309

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…However, it was not easy to deploy direct human activity recognition systems, such as RFID or visual inspection, due to the personal privacy protection policy of the local production company. Hence, instead of direct human activity measurements, the movements of the manual/semi-automatic machines operated by the workers were measured in this research, as mentioned by Harja et al [14].…”

Section: Activities Capturing Systemmentioning

confidence: 99%

“…The IoT installation should obey the local labor law, avoid influencing the workers' regular jobs, and respect the employee's personal privacy. Harja et al [14] recorded the information of the working station instead of the workers' activity. In the smart labor-intensive factory paradigm proposed by Kim and Moon [15], the manufacturing information collectors, including the RFID on the products and the digital readers, are applied.…”

Section: Introductionmentioning

confidence: 99%

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Using a Flexible IoT Architecture and Sequential AI Model to Recognize and Predict the Production Activities in the Labor-Intensive Manufacturing Site

et al. 2021

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Under the pressures of global market uncertainty and rapid production changes, the labor-intensive industries demand instant manufacturing site information and accurate production forecasting. This research applies sensor modules with noise reduction, information abstracting, and wireless transmission functions to form a flexible internet of things (IoT) architecture for acquiring field information. Moreover, AI models are used to reveal human activities and predict the output of a group of workstations. The IoT architecture has been implemented in the actual shoe making site. Although there is a 5% missing data issue due to network transmission, neural network models can successfully convert the IoT data to machine utilization. By analyzing the field data, the actual collaboration among the worker team can be revealed. Furthermore, a sequential AI model is applied to learn to capture the characteristics of the team working. This AI model only requires training by 15 min of IoT data, then it can predict the current and next few days’ productions within 10% error. This research confirms that implementing the IoT architecture and applying the AI model enables instant manufacturing monitoring of labor-intensive manufacturing sites and accurate production forecasting.

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Section: Activities Capturing Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using a Flexible IoT Architecture and Sequential AI Model to Recognize and Predict the Production Activities in the Labor-Intensive Manufacturing Site

et al. 2021

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“…The manufacturing sector is witnessing a rapid digital transition to smart manufacturing with more emphasis on efficiency and effectiveness [1], [2]. The development of smart manufacturing intended to result in intelligent and autonomous products and production processes such as cyber-physical systems (CPSs), cloud computing, the Internet of Things (IoT), the Internet of Services (IoS), big data, robotics, and augmented reality under a single system [2][3][4]. Towards the so-called industry 4.0, monitoring the degradation of machine tools' components becomes a key feature so that smooth productivity is achieved [4].…”

Section: Introductionmentioning

confidence: 99%

Study on Predictive Maintenance of V-Belt in Milling Machines Using Machine Learning

Rahman

Erikyatna

Soegiharto

2022

JMEST

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Towards industry 4.0, monitoring the degradation of machine tools’ components becomes a key feature so that smooth productivity is achieved. To preserve the functionality and performance of the machine tools, proper maintenance activities must be planned and carried out. V-belt is important component in machine tools that transmits power from the electric motor spindle in order to machine to work and cut desired material properly. The purpose of this research is to develop a predictive maintenance system for v-belt milling machine Krisbow 31N2F using machine learning. The machine learning algorithm models using multiple and simple linear regression algorithm was developed in an open-source program. The test results show that the machine learning model has a high accuracy value in both the training data and the testing data. The multiple linear regression model has MSE value of 5.8830x10-6 and MAE value of 0.002. The Simple linear regression model has an MSE value of 0.0004x10-6 and MAE value of 0.162. The results shows that the use of the linear regression algorithm as a support for determining the prediction of RUL v-belt milling machine model 31N2F (BS) is successfully carried out.

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Development of Virtual Model for Cyber-Physical Screw Turbine

Harja

Setiawan

Erdani

et al. 2022

2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)

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Development of tools utilization monitoring system on labor-intensive manufacturing industries

Cited by 3 publications

References 7 publications

Using a Flexible IoT Architecture and Sequential AI Model to Recognize and Predict the Production Activities in the Labor-Intensive Manufacturing Site

Using a Flexible IoT Architecture and Sequential AI Model to Recognize and Predict the Production Activities in the Labor-Intensive Manufacturing Site

Study on Predictive Maintenance of V-Belt in Milling Machines Using Machine Learning

Development of Virtual Model for Cyber-Physical Screw Turbine

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