Retrofitting old CNC turning with an accelerometer at a remote location towards Industry 4.0

Herwan, Jonny; Kano, Seisuke; Ryabov, Oleg; Sawada, Hiroyuki; Kasashima, Nagayoshi; Misaka, Takashi

doi:10.1016/j.mfglet.2019.08.001

Cited by 20 publications

(15 citation statements)

References 11 publications

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“…In the reviewed studies, pose and position estimation was carried out with either inertial or camera-based sensors (i.e., RGB, infrared, depth or optical cameras), or in combination with each other ( Table 3 ). Inertial sensors have been widely employed across all industry sectors (49.2% of the reviewed works), whether the tracked object was an automated tool, the end effector of a robot [ 30 , 37 , 64 ], or the operator [ 27 , 36 , 39 ]. In 30.5% of the reviewed studies, camera-based off-the-shelf devices such as RGB, IR and depth cameras, mostly coming from the gaming industry (e.g., Microsoft Kinect and Xbox 360), were successfully employed for human activity tracking, and gesture or posture classification [ 25 , 77 ].…”

Section: Resultsmentioning

confidence: 99%

Motion Capture Technology in Industrial Applications: A Systematic Review

Menolotto

Komaris

Tedesco

et al. 2020

Sensors

172

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The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition.

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Section: Resultsmentioning

confidence: 99%

Motion Capture Technology in Industrial Applications: A Systematic Review

Menolotto

Komaris

Tedesco

et al. 2020

Sensors

172

View full text Add to dashboard Cite

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“…Some authors have used retrofitting to improve maintenance operations. For Instance, Cattaneo and Macchi [16] have retrofitted an old drilling machine realizing a DT for estimate Remaining Useful Life; Herwan et al [17] and Hesser et al [18] used artificial neural networks (ANN) for detecting the tool wear in a CNC machine after retrofitting; the latter show how the ANNs give better results than support vector machine (SVM) and k-nearest neighbors (KNN) models in tools wear prediction. Strauß et al [19] have retrofitted a heavy lift Electric Monorail System (EMS) at the BMW Group with a low-cost sensor and have used machine learning algorithms for predictive maintenance; this work shows how supervised models, such as the ANNs, are the best choice when labeled fault data are available.…”

Section: Literature Reviewmentioning

confidence: 99%

Retrofitting a Process Plant in an Industry 4.0 Perspective for Improving Safety and Maintenance Performance

2021

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The transformation from traditional industry to Industry 4.0 can bring many benefits in various spheres, from efficiency to safety. However, this transition involves adopting technologically advanced machinery with a high level of digitization and communication. The costs and time to replace obsolete machines could be unsustainable for many companies while retrofitting the old machinery. To make them ready to the Industry 4.0 context, they may represent an alternative to the replacement. Even if there are many studies related to retrofitting applied to machinery, there are very few studies related to the literature process industry sector. In this work, we propose a case study of a two-phase mixing plant that needed to be enhanced in the safety and maintainability conditions with reasonable times and costs. In this regard, the Digital Twin techniques and Deep Learning algorithms will be tested to predict and detect future faults, not only already visible and existing malfunctions. This approach strength is that, with limited investments and reasonable times, it allows the transformation of an old plant into a smart plant capable of communicating quickly with operators to increase its safety and maintainability.

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“…In such works, they retrofit a sensor, collect the data and analyze the possibility of predicting the machine faults in real-time. For instance, Herwan et al proposed retrofitting a CNC machine with an accelerometer to predict tool wear [4]. Similarly, Hesser et al suggested retrofitting a milling machine with a vibration sensor to collect data and predict tool wear [5].…”

Section: State-of-the-artmentioning

confidence: 99%

Augmented Reality Based Machine Monitoring for Legacy Machines: a retrofitting use case

Kancharla¹,

Bekaert²,

Lannoo³

et al. 2021

2021 XXX International Scientific Conference Electronics (ET)

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Monitoring a machine and the insight it provides for appropriate maintenance is of prime importance for the modern industry. While this is fully supported in Industry 4.0, many manufacturing units today are unable to use its technologies. One of the major reasons behind that is the lack of onboard sensors to capture and communicate appropriate data from a legacy machine. Retrofitting is considered an efficient solution to include legacy machines in an industry 4.0 ecosystem by adding external sensing and connectivity. As a proof of concept, in this paper we consider an induction motor for retrofitting, a major component of any manufacturing unit. We further show how retrofitting can help in making legacy machines compliant to the new technologies from Industry 4.0. Finally, visualization of the collected data is performed over a web based Augmented Reality application. The choice for a web-based Augmented Reality application was made to enable monitoring of the sensor data over a multitude of devices.

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Retrofitting old CNC turning with an accelerometer at a remote location towards Industry 4.0

Cited by 20 publications

References 11 publications

Motion Capture Technology in Industrial Applications: A Systematic Review

Motion Capture Technology in Industrial Applications: A Systematic Review

Retrofitting a Process Plant in an Industry 4.0 Perspective for Improving Safety and Maintenance Performance

Augmented Reality Based Machine Monitoring for Legacy Machines: a retrofitting use case

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