Automatic machine status prediction in the era of Industry 4.0: Case study of machines in a spring factory

Kuo, Chung‐Hao; Ting, Kuo-Cheng; Chen, Yi-Chung; Yang, Don‐Lin; Chen, Hsi-Min

doi:10.1016/j.sysarc.2017.10.007

Cited by 33 publications

(30 citation statements)

References 19 publications

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“…• Achieving high effectiveness and efficiency of production systems (improved resources allocation, selective human intervention, automatically optimized production planning, improved supply chain management) (Witkowski, 2017). • Advanced quality assurance, introducing modern "intelligent quality control systems", early failure prediction system, cost-effective quality monitoring techniques (Kuo et al, 2017). • Lean production system, where all kinds of waste including time, materials, human power, and inventory level are in optimum values (Mrugalska and Wyrwicka, 2017).…”

Section: Methodology Of This Studymentioning

confidence: 99%

Industry 4.0 as a Key Enabler toward Successful Implementation of Total Quality Management Practices

Sader

Husti

Daróczi

2019

Period. Polytech. Soc. Man. Sci.

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Industry 4.0 refers to the new technological development occurred at the industrial production systems. It evolved as a result of integrating Internet of Things, Cyber-Physical Systems, Big-Data, Artificial Intelligence, and Cloud Computing in the industrial systems. This integration aided new capabilities to achieve a higher level of business excellence, efficiency, and effectiveness. Total Quality Management (TQM) is a managerial approach to achieve an outstanding business excellence. There are several approaches to apply TQM principles at any organization. Industry 4.0 could be utilized as a key enabler for TQM especially by integrating its techniques with the TQM best practices. This paper suggests a theoretical framework for integrating Industry 4.0 features with the TQM principles (according to ISO 9000:2015 standards family) in order to open the door for further research to address the real impact of utilizing Industry 4.0 for serving the TQM implementation approaches.

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Section: Methodology Of This Studymentioning

confidence: 99%

Industry 4.0 as a Key Enabler toward Successful Implementation of Total Quality Management Practices

Sader

Husti

Daróczi

2019

Period. Polytech. Soc. Man. Sci.

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“…Previous research [9] shows that the most common physical features in machinery are motion, vibration, noise, and temperature. To detect these features, we can use triaxial accelerometers (for motion and vibration), microphones (for noise), and thermocouples (for temperature).…”

Section: Related Work 21 Sensors Commonly Used In Machinerymentioning

confidence: 99%

“…In consideration of this, many researchers have been developing inexpensive ways to help these manufacturers achieve Industry 4.0. For instance, Kuo et al [4] [9] proposed methods that combine machine maintenance personnel experience, add-on triaxial accelerometers, and artificial neural networks for machinery fault detection and successfully applied these methods to spring factories. Kuo et al [5]successfully developed an RFID-based component management system that records what components a machine contains and what processing task it is performing, thereby greatly increasing component management efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…The existing method that is closest to the scheduling approach which we propose is the multi-tasking multi-machine scheduling algorithm for single-stage multi-criteria production presented by Kuo et al [9].The method that they proposed can efficiently assist factories in scheduling single-stage work orders and is particularly helpful for processes with only a single stage, such as the manufacturing of springs or screws. However, it cannot be applied to factories with multi-stage manufacturing processes, such as household appliance factories or semiconductor fabs.…”

Section: Introductionmentioning

confidence: 99%

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Multi-tasking multi-machine scheduling system for multi-stage multi-criteria production

et al. 2018

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In recent years, many major manufacturers have been incorporating Industry 4.0 technologies such as preventive fault detection, automated scheduling algorithms, and component management to increase productivity and reduce production costs. Achieving this objective requires a substantial amount of working capital to acquire large quantities of new machinery, equipment to extract data from the machinery, and high-priced big data analysis software. However, most factories in the world are small-or medium-sized companies and have not enough capital to replace their machinery or purchase big data analysis software. It is therefore almost impossible for these factories to reach the goal of Industry 4.0. Furthermore, most of the conventional automated production scheduling methods only consider a single criterion in scheduling, which is not applicable for actual situations. This study therefore proposed a multi-tasking multi-machine scheduling system for multi-stage multi-criteria production to address various shortcomings in existing methods. To achieve this goal, we proposed a novel concept based on skyline queries to assist in the scheduling process. Also, a data structure of "heap" is applied in this work to accelerate the scheduling process. The experimental results demonstrated the validity of the proposed approach.

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“…For many different legacy machines, various operation statuses detecting method by current signal is proposed to identify the actual operating condition [4]. For no sensor-embedded machine, Cheng-Ju Kuo et al [5] used add-on simple sensors to obtain various feature values from that machine, and designed procedures to predict status of machines to help factories with legacy machines to overcome barriers to build Industry 4.0 function. For match the main trend to build the high degree of automation, the economic efficiency of highly productive machine is determined by the manufacturing equipment availability.…”

Section: Introductionmentioning

confidence: 99%

Embedded smart box for legacy machines to approach to I 4.0 in smart manufacturing

Fan

Chang

2018

MATEC Web Conf.

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Abstract. This paper introduces the design of a Linux-based embedded controller which includes machine state detection application for legacy machines and manufacturing line. For Industrie 4.0 (I4.0), it is important to acquire, manipulate, and transmit machine operating states or physical data to form useful information. However, many existing legacy machines lack of controller or sensor(s) to response to their operating status. Some machine controllers cannot be connected to provide internal parameter(s) by means of communication. Gathering machine operating state should be the first priority to approach to I4.0. This paper adopts widely used Raspberry PI as the core platform to build Embedded Smart Box (ESB). It uses external sensors to detect the machine operating status to compute the machine's availability (one of Overall Equipment Efficiency factors) and measures current to calculate the power consumption. In this research, the combination of embedded system and sensors can be a smart box for legacy machines. Such cost-effective design would help users to take the useful data from the machines and construct the base of I4.0 system even without the existence of the controller. This embedded-based design methodology has great potential implications that might fundamentally change the legacy factories into I4.0 smart one.

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Automatic machine status prediction in the era of Industry 4.0: Case study of machines in a spring factory

Cited by 33 publications

References 19 publications

Industry 4.0 as a Key Enabler toward Successful Implementation of Total Quality Management Practices

Industry 4.0 as a Key Enabler toward Successful Implementation of Total Quality Management Practices

Multi-tasking multi-machine scheduling system for multi-stage multi-criteria production

Embedded smart box for legacy machines to approach to I 4.0 in smart manufacturing

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