A survey of the advancing use and development of machine learning in smart manufacturing

Sharp, Michael; Ak, Ronay; Hedberg, Thomas D.

doi:10.1016/j.jmsy.2018.02.004

Cited by 211 publications

(89 citation statements)

References 34 publications

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“…Machine learning continues to gain traction and interest as a useful tool in the generation of knowledge from data [32,33], particularly within the field of manufacturing [34]; as the use of machine learning techniques enables complex, non-linear and temporal relationships to be modelled easily through the use of historical data reserves. Neural networks have been successfully implemented in a number of applications and provide a nondeterministic method of matching a number of input variables to an output, and for approximating relationships between multidimensional data.…”

Section: ) Machine Learningmentioning

confidence: 99%

Improving Human-Robot Interaction Utilizing Learning and Intelligence: A Human Factors-Based Approach

Oliff

Liu

Kumar

et al. 2020

IEEE Trans. Automat. Sci. Eng.

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Several decades of development in the fields of robotics and automation has resulted in human-robot-interaction being commonplace, and the subject of intense study. These interactions are particularly prevalent in manufacturing, where human operators have been employed in a number of robotics and automation tasks. The presence of human operators continues to be a source of uncertainty in such systems, despite the study of human factors, in an attempt to better understand these variations in performance. Concurrent developments in intelligent manufacturing present opportunities for adaptability within robotic control. This work examines relevant human factors and develops a framework for integrating the necessary elements of intelligent control and data processing to provide appropriate adaptability to robotic elements, consequently improving collaborative interaction with human colleagues. A neural network-based learning approach is used to predict the influence on human task performance and use these predictions to make informed changes to programmed behaviour, and a methodology developed to further explore the application of learning techniques to this area. The work is supported by an example case-study, in which a simulation model is used to explore the application of the developed system, and its performance in a real-world production scenario. The simulation results reveal that adaptability can be realised with some relatively simple techniques and models if applied in the right manner and that such adaptability is helpful to tackle the issue of performance disparity in manufacturing operations. NTP: This paper presents research into the application of intelligent methodologies to this problem and builds a framework to describe how this information can be captured, generated and used, within manufacturing production processes. This framework helps identify which areas require further research and serves as a basis for the development of a methodology, by which a control system may enable adaptable behaviour to reduce the impact of human performance variation and improve human-machine-interaction. The paper also presents a simulation-based case study, to support the development and evaluate the presented control system on a representative real-world problem. The methodology makes use of a machine learning approach to identify the complex influence of a number of identified human factors on human performance. This knowledge can be used to adjust the robotic behaviour to match the predicted performance of a number of different operators over a number of scenarios. The adaptability reduces performance disparity, reducing idle times and enabling leaner production through WIP reduction. Future work

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Section: ) Machine Learningmentioning

confidence: 99%

Improving Human-Robot Interaction Utilizing Learning and Intelligence: A Human Factors-Based Approach

Oliff

Liu

Kumar

et al. 2020

IEEE Trans. Automat. Sci. Eng.

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“…Work has shown that data-driven methods can, from build data, model how process parameters affect the quality of final parts [28][29] [30]. Approaches that utilise build data are advantageous because they provide great opportunities for digitalisation and smart process control, otherwise known as 'smart manufacturing' [31][32] [33].…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Automatic Fault Detection for Selective Laser Melting Using Semi-Supervised Machine Learning

Okaro¹,

Jayasinghe²,

Sutcliffe³

et al. 2018

Preprint

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Risk-averse areas such as the medical, aerospace and energy sectors have been somewhat slow towards accepting and applying Additive Manufacturing (AM) in many of their value chains. This is partly because there are still signicant uncertainties concerning the quality of AM builds. This paper introduces a machine learning algorithm for the automatic detection of faults in AM products. The approach is semi-supervised in that, during training, it is able to use data from both builds where the resulting components were certied and builds where the quality of the resulting components is unknown. This makes the approach cost ecient, particularly in scenarios where part certication is costly and time consuming. The study specically analyses Selective Laser Melting (SLM) builds. Key features are extracted from large sets of photodiode data, obtained during the building of 49 tensile test bars. Ultimate tensile strength (UTS) tests were then used to categorise each bar as `faulty' or `acceptable'. A fully supervised approach identied faulty specimens with a 77% success rate while the semi-supervised approach was able to consistently achieve similar results, despite being trained on a fraction of the available certication data. The results show that semi-supervised learning is a promising approach for the automatic certication of AM builds that can be implemented at a fraction of the cost currently required.

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“…In the manufacturing field, Industry 4.0 is characterized by the autonomous systems with cyber and physical representation [20][21][22] and advance in ICT [23] such as cloud computing [24], internet of things (IoT), internet of service (IoS) [25][26][27][28][29], agent technology [30][31][32][33][34][35], cognitive technology [36,37], big data [38,39], data mining [40,41], and artificial intelligence [42][43][44][45][46][47]. Smart manufacturing systems (SMS) built with intelligent cyber-physical systems (CPSs) are the heart of the fourth industrial revolution [21,48].…”

Section: Introductionmentioning

confidence: 99%

“…The development of manufacturing not only considers technological innovations but also economic, social and environmental effects [4,7]. In the literature, many novel paradigms known as intelligent manufacturing systems (IMS) [8][9][10][11][12][13][14][15] have been proposed such as holonic [16], biological [17], reconfigurable [18], and cloud manufacturing systems [19].In the manufacturing field, Industry 4.0 is characterized by the autonomous systems with cyber and physical representation [20][21][22] and advance in ICT [23] such as cloud computing [24], internet of things (IoT), internet of service (IoS) [25][26][27][28][29], agent technology [30][31][32][33][34][35], cognitive technology [36,37], big data [38,39], data mining [40,41], and artificial intelligence [42][43][44][45][46][47]. Smart manufacturing systems (SMS) built with intelligent cyber-physical systems (CPSs) are the heart of the fourth industrial revolution [21,48].…”

mentioning

confidence: 99%

Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context

Tran

Park²,

Nguyen

et al. 2019

Applied Sciences

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Featured Application: A proposed smart manufacturing system has the ability to adapt to manufacturing changes. Abstract:The complexity and dynamic of the manufacturing environment are growing due to the changes of manufacturing demand from mass production to mass customization that require variable product types, small lot sizes, and a short lead-time to market. Currently, the automatic manufacturing systems are suitable for mass production. To cope with the changes of the manufacturing environment, the paper proposes the model and technologies for developing a smart cyber-physical manufacturing system (Smart-CPMS). The transformation of the actual manufacturing systems to the Smart-CPMS is considered as the next generation of manufacturing development in Industry 4.0. The Smart-CPMS has advanced characteristics inspired from biology such as self-organization, self-diagnosis, and self-healing. These characteristics ensure that the Smart-CPMS is able to adapt with continuously changing manufacturing requirements. The model of Smart-CPMS is inherited from the organization of living systems in biology and nature. Consequently, in the Smart-CPMS, each resource on the shop floor such as machines, robots, transporters, and so on, is an autonomous entity, namely a cyber-physical system (CPS) which is equipped with cognitive capabilities such as perception, reasoning, learning, and cooperation. The Smart-CPMS adapts to the changes of manufacturing environment by the interaction among CPSs without external intervention. The CPS implementation uses the cognitive agent technology. Internet of things (IoT) with wireless networks, radio frequency identification (RFID), and sensor networks are used as information and communication technology (ICT) infrastructure for carrying out the Smart-CPMS.Currently, manufacturing demands are changing from mass production to mass customization, which is focused on exclusive and individualized demand [2,3]. This trend requires the manufacturing systems to have the ability to adapt to the fast change of the manufacturing environment. To face this challenge, the new trend of manufacturing system development is to apply autonomous behaviors inspired from living systems and nature to have the sustainable manufacturing systems. Industry 4.0 enables the realization of sustainable manufacturing using the ubiquitous information and communication technology (ICT) infrastructure [4]. National strategies for developing smart manufacturing systems are focused on by both developing and developed countries [5,6]. The development of manufacturing not only considers technological innovations but also economic, social and environmental effects [4,7]. In the literature, many novel paradigms known as intelligent manufacturing systems (IMS) [8][9][10][11][12][13][14][15] have been proposed such as holonic [16], biological [17], reconfigurable [18], and cloud manufacturing systems [19].In the manufacturing field, Industry 4.0 is characterized by the autonomous systems with cyber and physical representation [20]...

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A survey of the advancing use and development of machine learning in smart manufacturing

Cited by 211 publications

References 34 publications

Improving Human-Robot Interaction Utilizing Learning and Intelligence: A Human Factors-Based Approach

Improving Human-Robot Interaction Utilizing Learning and Intelligence: A Human Factors-Based Approach

Automatic Fault Detection for Selective Laser Melting Using Semi-Supervised Machine Learning

Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context

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