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2021 | OriginalPaper | Buchkapitel

Industry 4.0: Why Machine Learning Matters?

verfasst von : T. H. Gan, J. Kanfoud, H. Nedunuri, A. Amini, G. Feng

Erschienen in: Advances in Condition Monitoring and Structural Health Monitoring

Verlag: Springer Singapore

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Abstract

Machine Learning is at the forefront of the Industry 4.0 revolution. Both the amount and complexity of data generated by sensors make interpretation and ingestion of data beyond human capability. It is impossible for engineers to optimise an extremely complex production line and to understand which unit or machine is responsible for quality or productivity degradation! It is extremely difficult for engineers to process monitoring or inspection data as it requires a protocol, a trained and certified engineer as well as experience! It is extremely difficult to guarantee the quality of every single product particularly at high production rates! Artificial Intelligence can help answering the above questions. Indeed, machine learning can be used for predictive or condition-based maintenance. Even without knowing the design of the machine (i.e., gearbox stages, bearing design, etc.), a machine learning algorithm is capable to monitor deviation of monitoring sensors features compared to a healthy state. Machine learning can be used to monitor the quality of production by allowing the automation of the quality control process. Monitoring additive manufacturing process to detect defects during printing and allow mitigation through real-time machining and reprinting of the defective area. Ensuring the quality of very complex and sensitive production processes such as MEMS, electronic wafers, solar cells or OLED screens. Brunel Innovation Centre (BIC) is working on developing algorithms combining statistical, signal/image processing for features extraction and deep learning for automated defect recognition for quality control and for predictive maintenance. Brunel Innovation Centre is also working on integrating those technologies into the digital twin.

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Literatur
1.
Zurück zum Zitat Taylor FW (1911) The principles of scientific management. Harper and Brothers, New York Taylor FW (1911) The principles of scientific management. Harper and Brothers, New York
2.
Zurück zum Zitat Ford H, Crowther S (1922) My life and work, x edn. Dbouleday, New York Ford H, Crowther S (1922) My life and work, x edn. Dbouleday, New York
4.
Zurück zum Zitat Paolo C et al (2018) Product lifecycle management to support Industry 4.0. In: 15th IFIP WG 5.1 international conference, proceedings, Turin, Italy, vol 540. Springer Paolo C et al (2018) Product lifecycle management to support Industry 4.0. In: 15th IFIP WG 5.1 international conference, proceedings, Turin, Italy, vol 540. Springer
5.
Zurück zum Zitat Wang S et al (2016) Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput Netw 101:158–168 Wang S et al (2016) Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput Netw 101:158–168
7.
Zurück zum Zitat Collins PC et al (2014) Progress toward an integration of process–structure–property–performance models for “three-dimensional (3-D) printing” of titanium alloys. Jom 66(7):1299–1309 Collins PC et al (2014) Progress toward an integration of process–structure–property–performance models for “three-dimensional (3-D) printing” of titanium alloys. Jom 66(7):1299–1309
8.
Zurück zum Zitat Xiong J et al (2014) Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. J Intell Manuf 25(1):157–163 Xiong J et al (2014) Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. J Intell Manuf 25(1):157–163
9.
Zurück zum Zitat Glaessgen E, Stargel D (2012) The digital twin paradigm for future NASA and US Air Force vehicles. In: 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, Honululo, Hawai Glaessgen E, Stargel D (2012) The digital twin paradigm for future NASA and US Air Force vehicles. In: 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, Honululo, Hawai
10.
Zurück zum Zitat Carbonell JG et al (1983) An overview of machine learning, pp 3–23. Morgan Kaufmann, New York Carbonell JG et al (1983) An overview of machine learning, pp 3–23. Morgan Kaufmann, New York
11.
Zurück zum Zitat Kateris D et al (2014) A machine learning approach for the condition monitoring of rotating machinery. J Mech Sci Technol 28(1):61–71 Kateris D et al (2014) A machine learning approach for the condition monitoring of rotating machinery. J Mech Sci Technol 28(1):61–71
12.
Zurück zum Zitat Köksal G et al (2011) A review of data mining applications for quality improvement in manufacturing industry. Expert Syst Appl 38(10):13448–13467 Köksal G et al (2011) A review of data mining applications for quality improvement in manufacturing industry. Expert Syst Appl 38(10):13448–13467
13.
Zurück zum Zitat Min Q et al (2019) Machine learning based digital twin framework for production optimization in petrochemical industry. Int J Inf Manag 49:502–519 Min Q et al (2019) Machine learning based digital twin framework for production optimization in petrochemical industry. Int J Inf Manag 49:502–519
14.
Zurück zum Zitat ur Rahman MH et al (2016) Big data reduction framework for value creation in sustainable enterprises. Int J Inf Manag 36(6):917–928 ur Rahman MH et al (2016) Big data reduction framework for value creation in sustainable enterprises. Int J Inf Manag 36(6):917–928
15.
Zurück zum Zitat Tellaeche A, Ramón A (2016) Machine learning algorithms for quality control in plastic molding industry. In: IEEE 18th conference on emerging technologies & factory automation, Cagliari, Italy Tellaeche A, Ramón A (2016) Machine learning algorithms for quality control in plastic molding industry. In: IEEE 18th conference on emerging technologies & factory automation, Cagliari, Italy
16.
Zurück zum Zitat Monostori L (2003) AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing. Eng Appl Artif Intell 16(4):277–291CrossRef Monostori L (2003) AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing. Eng Appl Artif Intell 16(4):277–291CrossRef
Metadaten
Titel
Industry 4.0: Why Machine Learning Matters?
verfasst von
T. H. Gan
J. Kanfoud
H. Nedunuri
A. Amini
G. Feng
Copyright-Jahr
2021
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-15-9199-0_37

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