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The research on predictive maintenance of rotating machines, the most important element in manufacturing facilities, has been very active. The widespread availability of smart factory solutions has led to improved data collection from machines and processes and is able to provide key information. For our purpose, the collected information enables the maintenance system to predict the remaining useful life using deep learning models. The introduction of multi-layer perceptron of signal processing originating from bearings, in time series data, has been discussed in many publications. However, estimating accuracy for the remaining useful life is determined by the selection of the feature domain and the concatenation network model. Herein, we introduce a convolutional Autoencoder based on multi-domain ensemble learning in order to include various feature domains and a concatenation network operated by latent space into a single neural network. The performance of the proposed model is evaluated by using a simple health indicator and a PRONOSTIA dataset and compared with a simple concatenation model, 2-stage Autoencoder, and a recurrent neural network.
Yong-Keun Park; Min-Kyung Kim; Jumyung Um. A One-Stage Ensemble Framework based on Convolutional Autoencoder for Remaining Useful Life Estimation. 2021, 1 .
AMA StyleYong-Keun Park, Min-Kyung Kim, Jumyung Um. A One-Stage Ensemble Framework based on Convolutional Autoencoder for Remaining Useful Life Estimation. . 2021; ():1.
Chicago/Turabian StyleYong-Keun Park; Min-Kyung Kim; Jumyung Um. 2021. "A One-Stage Ensemble Framework based on Convolutional Autoencoder for Remaining Useful Life Estimation." , no. : 1.
The paper describes problems with the current additive manufacturing chain before considering additive manufacturing as part of a modern manufacturing chain. Additive manufacturing can be used for near net-shape for finishing, for repair or for adding special features which cannot be made with traditional manufacturing. This paper describes how STEP-NC deals with these different scenarios in terms of accuracy, multi-material and variation of slice direction. The possibilities of multi-material objects also raises questions about the design of such objects and how these need to be handled by an advanced controller. The paper also describes non-planar slicing. Curved direction and cylindrical direction are shown to improve the accuracy of curved structure additive manufacturing. STEP-NC using boundary representation has better capability of depicting complex internal structures for additive processes. By using exact model of the final product represented by STEP-NC, the paper demonstrates improvements in data size reduction, slicing accuracy, and precise manipulation of internal structure.
Jumyung Um; Joung Min Park; Ian Anthony Stroud. STEP-NC Based Squashing Slicing Algorithm for Multi-Material and Multi-Directional Additive Process. 2021, 1 .
AMA StyleJumyung Um, Joung Min Park, Ian Anthony Stroud. STEP-NC Based Squashing Slicing Algorithm for Multi-Material and Multi-Directional Additive Process. . 2021; ():1.
Chicago/Turabian StyleJumyung Um; Joung Min Park; Ian Anthony Stroud. 2021. "STEP-NC Based Squashing Slicing Algorithm for Multi-Material and Multi-Directional Additive Process." , no. : 1.
Automatic robot gripper system which involves the automated object recognition of work-in-process in production line is the key technology of the upcoming manufacturing facility achieving Industry 4.0. Automatic robot gripper enables the manufacturing system to be autonomous, self-recognized, and adaptable by using artificial intelligence of robot programming dealing with arbitrary shapes of work-in-processes. This paper specifically explores the chain of key technologies, such as 3D object recognition with CAD and point cloud data, reinforcement learning of robot arm, and customized 3D printed gripper, in order to enhance the intelligence of the robot controller system. And it also proposes the integration with 3D point cloud based object recognition and game-engine based reinforcement learning. The result of the prototype of the intelligent robot gripping system developed by the proposed method with a 4 degree-of-freedom robot arm is explained in this paper.
Joungmin Park; Sangyoon Lee; Jaewoon Lee; Jumyung Um. GadgetArm—Automatic Grasp Generation and Manipulation of 4-DOF Robot Arm for Arbitrary Objects Through Reinforcement Learning. Sensors 2020, 20, 6183 .
AMA StyleJoungmin Park, Sangyoon Lee, Jaewoon Lee, Jumyung Um. GadgetArm—Automatic Grasp Generation and Manipulation of 4-DOF Robot Arm for Arbitrary Objects Through Reinforcement Learning. Sensors. 2020; 20 (21):6183.
Chicago/Turabian StyleJoungmin Park; Sangyoon Lee; Jaewoon Lee; Jumyung Um. 2020. "GadgetArm—Automatic Grasp Generation and Manipulation of 4-DOF Robot Arm for Arbitrary Objects Through Reinforcement Learning." Sensors 20, no. 21: 6183.
As part of the fourth industrial revolution, the movement to apply various enabling technologies under the name of Industry 4.0 is being promoted worldwide. Because of the wide range of applications and the capacity of manufacturing workpieces flexibly, machine tools are regarded as essential industrial elements. Hence, much research has been concerned with applying various enabling technologies such as cyber-physical systems to machine tools. To realize a machine tool suitable for Industry 4.0, development should be done in a systematic manner rather than the ad-hoc application of enabling technologies. In this paper, we propose a functional architecture for the Industry 4.0 version of machine tools, namely smart machine tool system. To reflect the voices of various stakeholders, stakeholder requirements are identified and transformed into design considerations. The design considerations are incorporated into the conceptual model and functional modeling, both of which are used to derive the functional architecture. The implementation procedure and an illustrative case study are presented for the application of the functional architecture.
Byeongwoo Jeon; Joo-Sung Yoon; Jumyung Um; Suk-Hwan Suh. The architecture development of Industry 4.0 compliant smart machine tool system (SMTS). Journal of Intelligent Manufacturing 2020, 31, 1837 -1859.
AMA StyleByeongwoo Jeon, Joo-Sung Yoon, Jumyung Um, Suk-Hwan Suh. The architecture development of Industry 4.0 compliant smart machine tool system (SMTS). Journal of Intelligent Manufacturing. 2020; 31 (8):1837-1859.
Chicago/Turabian StyleByeongwoo Jeon; Joo-Sung Yoon; Jumyung Um; Suk-Hwan Suh. 2020. "The architecture development of Industry 4.0 compliant smart machine tool system (SMTS)." Journal of Intelligent Manufacturing 31, no. 8: 1837-1859.
As Additive manufacturing (AM) technologies and connected machines are ever more widespread across most industries, there is a need for a better understanding about the standardization activities related to information technology (IT) within the scope of AM. The purpose of this paper is to provide an overview of existing standardization activities related to IT aspects of AM, including but not limited to data transfer and exchange. The paper covers the IT standardization activities in organizations, including ISO TC261, ASTM International, ISO TC 184, ISO/IEC JTC 1, Web3D Consortium, IEC committees, IEEE committees, 3MF Consortium and Khronos, which have the most active and ongoing projects. From this study, it can be observed that the manufacturing sector has generated the majority of standardization work, followed by industry R&D areas and the medical field. This paper also discusses two future IT standards for AM service platforms and for the medical industry.
Byoung Nam Lee; Eujin Pei; Jumyung Um. An overview of information technology standardization activities related to additive manufacturing. Progress in Additive Manufacturing 2019, 4, 345 -354.
AMA StyleByoung Nam Lee, Eujin Pei, Jumyung Um. An overview of information technology standardization activities related to additive manufacturing. Progress in Additive Manufacturing. 2019; 4 (3):345-354.
Chicago/Turabian StyleByoung Nam Lee; Eujin Pei; Jumyung Um. 2019. "An overview of information technology standardization activities related to additive manufacturing." Progress in Additive Manufacturing 4, no. 3: 345-354.
Due to concerns about energy use in production systems, energy-efficient processes have received much interest from the automotive industry recently. Remote laser welding is an innovative assembly process, but has a critical issue with the energy consumption. Robot companies provide only the average energy use in the technical specification, but process parameters such as robot movement, laser use, and welding path also affect the energy use. Existing literature focuses on measuring energy in standardized conditions in which the welding process is most frequently operated or on modularizing unified blocks in which energy can be estimated using simple calculations. In this paper, the authors propose an integrated approach considering both process variation and machine specification and multiple methods’ comparison. A deep learning approach is used for building the neural network integrated with the effects of process parameters and machine specification. The training dataset used is experimental data measured from a remote laser welding robot producing a car back door assembly. The proposed estimation model is compared with a linear regression approach and shows higher accuracy than other methods.
Jumyung Um; Ian Anthony Stroud; Yong-Keun Park. Deep Learning Approach of Energy Estimation Model of Remote Laser Welding. Energies 2019, 12, 1799 .
AMA StyleJumyung Um, Ian Anthony Stroud, Yong-Keun Park. Deep Learning Approach of Energy Estimation Model of Remote Laser Welding. Energies. 2019; 12 (9):1799.
Chicago/Turabian StyleJumyung Um; Ian Anthony Stroud; Yong-Keun Park. 2019. "Deep Learning Approach of Energy Estimation Model of Remote Laser Welding." Energies 12, no. 9: 1799.
Flexibility in mass-customized manufacturing can be supported significantly by the introduction of Cyber-Physical Production System and the connection of production modules to AI (artificial intelligence) Cloud services. Even though there exist standardized protocols from device to IT system, there are still challenges for the synchronization between cyber-model and physical object, and the application of decision making in the cyber-model. Although high performance machine learning services make the Cloud a preferred computation node, possible unstable connection with manufacturing resources enforce new service distribution approaches in the network. This paper proposes an Edge Computing architecture which is the mediator between machines, by providing local Cloud services with fast response time and preprocessing resources for a vast amount of data. As an illustrative example the selected Edge service pre-processes data form an augmented reality device in order to communicate with the cyber-model in real time. The Edge platform controls the computing resources and prioritizes all processes of Edge Services for a dynamic update of production lines and human-machine-interaction.
Jumyung Um; Volkan Gezer; Achim Wagner; Martin Ruskowski. Edge Computing in Smart Production. Advances in Intelligent Systems and Computing 2019, 144 -152.
AMA StyleJumyung Um, Volkan Gezer, Achim Wagner, Martin Ruskowski. Edge Computing in Smart Production. Advances in Intelligent Systems and Computing. 2019; ():144-152.
Chicago/Turabian StyleJumyung Um; Volkan Gezer; Achim Wagner; Martin Ruskowski. 2019. "Edge Computing in Smart Production." Advances in Intelligent Systems and Computing , no. : 144-152.
This paper presents a production scheduling system that optimizes operations of a modular factory. The proposed system consists of a database, a scheduling optimizer and an interface connecting two other components. A scheduling model for optimal schedules plays a key role in the scheduling system. In this research, we aim to develop the scheduling model using constraint programming, and design a data mart storing the related operational data. In particular, we set the modular factory environment to the hybrid flow shop and apply the constraint programming for scheduling. We provide a case study to test the performance of the proposed scheduling model with the synthesized dataset based on a modular factory environment in SmartFactoryKL.
Hoonseok Park; Jumyung Um; Jae-Yoon Jung; Martin Ruskowski. Developing a Production Scheduling System for Modular Factory Using Constraint Programming. Advances in Intelligent Systems and Computing 2019, 126 -133.
AMA StyleHoonseok Park, Jumyung Um, Jae-Yoon Jung, Martin Ruskowski. Developing a Production Scheduling System for Modular Factory Using Constraint Programming. Advances in Intelligent Systems and Computing. 2019; ():126-133.
Chicago/Turabian StyleHoonseok Park; Jumyung Um; Jae-Yoon Jung; Martin Ruskowski. 2019. "Developing a Production Scheduling System for Modular Factory Using Constraint Programming." Advances in Intelligent Systems and Computing , no. : 126-133.
The trend of mass customization requires highly flexible production systems, which pose a new challenge for workers. Numerous concepts using augmented reality technologies have been developed to offer support in manual assembly processes. Newer AR devices are equipped with more and more functions, such as microphones, speakers or multiple camera systems, which can be used to help workers. However, wearable devices offer only limited computational power and limited flexibility of their interface platform. This poses a challenge to heavy data transactions from various different sources and restrict the use for real-time responses, which are often necessary for applications in a factory. To overcome this limitation, the author of this paper proposes a combination of wearable devices with edge devices located on the shop floor. An augmented reality platform was constructed with modularized services by using the digital twin of the machine components and machine learning algorithms for object recognition. This is integrated in the factory environment to synchronize real machine components with their digital twin.
Jumyung Um; Jens Popper; Martin Ruskowski. Modular augmented reality platform for smart operator in production environment. 2018 IEEE Industrial Cyber-Physical Systems (ICPS) 2018, 720 -725.
AMA StyleJumyung Um, Jens Popper, Martin Ruskowski. Modular augmented reality platform for smart operator in production environment. 2018 IEEE Industrial Cyber-Physical Systems (ICPS). 2018; ():720-725.
Chicago/Turabian StyleJumyung Um; Jens Popper; Martin Ruskowski. 2018. "Modular augmented reality platform for smart operator in production environment." 2018 IEEE Industrial Cyber-Physical Systems (ICPS) , no. : 720-725.
Jumyung Um; Stephan Weyer; Fabian Quint. Plug-and-Simulate within Modular Assembly Line enabled by Digital Twins and the use of AutomationML. IFAC-PapersOnLine 2017, 50, 15904 -15909.
AMA StyleJumyung Um, Stephan Weyer, Fabian Quint. Plug-and-Simulate within Modular Assembly Line enabled by Digital Twins and the use of AutomationML. IFAC-PapersOnLine. 2017; 50 (1):15904-15909.
Chicago/Turabian StyleJumyung Um; Stephan Weyer; Fabian Quint. 2017. "Plug-and-Simulate within Modular Assembly Line enabled by Digital Twins and the use of AutomationML." IFAC-PapersOnLine 50, no. 1: 15904-15909.
Marie-Hélène Stoltz; Vaggelis Giannikas; Duncan McFarlane; James Strachan; Jumyung Um; Rengarajan Srinivasan. Augmented Reality in Warehouse Operations: Opportunities and Barriers. IFAC-PapersOnLine 2017, 50, 12979 -12984.
AMA StyleMarie-Hélène Stoltz, Vaggelis Giannikas, Duncan McFarlane, James Strachan, Jumyung Um, Rengarajan Srinivasan. Augmented Reality in Warehouse Operations: Opportunities and Barriers. IFAC-PapersOnLine. 2017; 50 (1):12979-12984.
Chicago/Turabian StyleMarie-Hélène Stoltz; Vaggelis Giannikas; Duncan McFarlane; James Strachan; Jumyung Um; Rengarajan Srinivasan. 2017. "Augmented Reality in Warehouse Operations: Opportunities and Barriers." IFAC-PapersOnLine 50, no. 1: 12979-12984.
Jumyung Um; Klaus Fischer; Torsten Spieldenner; Dennis Kolberg. Development a Modular Factory with Modular Software Components. Procedia Manufacturing 2017, 11, 922 -930.
AMA StyleJumyung Um, Klaus Fischer, Torsten Spieldenner, Dennis Kolberg. Development a Modular Factory with Modular Software Components. Procedia Manufacturing. 2017; 11 ():922-930.
Chicago/Turabian StyleJumyung Um; Klaus Fischer; Torsten Spieldenner; Dennis Kolberg. 2017. "Development a Modular Factory with Modular Software Components." Procedia Manufacturing 11, no. : 922-930.
Byeong Woo Jeon; Jumyung Um; Soo Cheol Yoon; Suh Suk-Hwan. An architecture design for smart manufacturing execution system. Computer-Aided Design and Applications 2016, 14, 472 -485.
AMA StyleByeong Woo Jeon, Jumyung Um, Soo Cheol Yoon, Suh Suk-Hwan. An architecture design for smart manufacturing execution system. Computer-Aided Design and Applications. 2016; 14 (4):472-485.
Chicago/Turabian StyleByeong Woo Jeon; Jumyung Um; Soo Cheol Yoon; Suh Suk-Hwan. 2016. "An architecture design for smart manufacturing execution system." Computer-Aided Design and Applications 14, no. 4: 472-485.
The Smart Factory is an important topic worldwide as a means for achieving Industry 4.0 in the manufacturing domain. Contemporary research on the Smart Factory has been concerned with application of the so-called Internet of Things (IoT) to the shop floor. However, IoT in this context is often restricted to solving local problems such as managing product information, collaborative information exchange, and increasing productivity. To take full advantage of the potential of the IoT in manufacturing systems, it is necessary that the information service perspective should receive keen attention. This paper proposes a reference architecture for the information service bus or middleware for the Smart Factory that can be used for information acquisition, analysis, and application for the various stakeholders at the levels of Machine, Factory, and Enterprise Resource Planning. To reflect the real voice of the industry, real industrial problems have been identified, transformed into requirements, and incorporated into the information architecture; i.e., Smart Factory Information Service Bus. The implementation process of the reference architecture is also presented and illustrated via case studies.
Soocheol Yoon; Jumyung Um; Suk-Hwan Suh; Ian Stroud; Joo-Sung Yoon. Smart Factory Information Service Bus (SIBUS) for manufacturing application: requirement, architecture and implementation. Journal of Intelligent Manufacturing 2016, 30, 363 -382.
AMA StyleSoocheol Yoon, Jumyung Um, Suk-Hwan Suh, Ian Stroud, Joo-Sung Yoon. Smart Factory Information Service Bus (SIBUS) for manufacturing application: requirement, architecture and implementation. Journal of Intelligent Manufacturing. 2016; 30 (1):363-382.
Chicago/Turabian StyleSoocheol Yoon; Jumyung Um; Suk-Hwan Suh; Ian Stroud; Joo-Sung Yoon. 2016. "Smart Factory Information Service Bus (SIBUS) for manufacturing application: requirement, architecture and implementation." Journal of Intelligent Manufacturing 30, no. 1: 363-382.
Remote laser welding which has benefits for both productivity and energy saving is receiving increased attention for automotive assembly lines. Introducing this innovative equipment requires a redesign process for assembly elements, for example to substitute resist spot welding with remote laser welding. The mating surfaces related to the welding need to be updated as well, but this is complicated when the shape design includes manufacturing and assembly elements based on current practice which hide the design intent. The challenge is to establish guidelines with the design parameters of automotive door frames and the process parameters of the joining process in order to maintain the design intent. In order to maintain the original design intent of the process planner, the author proposes a propagation model for the production stage which is defined as the interplay between parameters and quantitative comparison. The process parameters of remote laser welding are applied to the flange of the door frame. Also, the shape of each flange is synchronised with the changes according to the result of a quantitative comparison of the productivity and eco-efficiency. A design guideline tool to redesign the flanges was developed inside a commercial CAD system to show the proposed concept. Five flanges of a car door were subjected to redesign for introducing 4-kw remote laser welding with considerations of eco-efficiency, process time and weight change. The tools are shown to illustrate how to maintain design intent in the redesign process, and the concept was realised in a commercial CAD system.
Jumyung Um; Ian Anthony Stroud. Design guidelines for remote laser welding in automotive assembly lines. The International Journal of Advanced Manufacturing Technology 2016, 89, 1039 -1051.
AMA StyleJumyung Um, Ian Anthony Stroud. Design guidelines for remote laser welding in automotive assembly lines. The International Journal of Advanced Manufacturing Technology. 2016; 89 (1-4):1039-1051.
Chicago/Turabian StyleJumyung Um; Ian Anthony Stroud. 2016. "Design guidelines for remote laser welding in automotive assembly lines." The International Journal of Advanced Manufacturing Technology 89, no. 1-4: 1039-1051.
Jumyung Um; Matthieu Rauch; Jean-Yves Hascoët; Ian Stroud. STEP-NC compliant process planning of additive manufacturing: remanufacturing. The International Journal of Advanced Manufacturing Technology 2016, 88, 1215 -1230.
AMA StyleJumyung Um, Matthieu Rauch, Jean-Yves Hascoët, Ian Stroud. STEP-NC compliant process planning of additive manufacturing: remanufacturing. The International Journal of Advanced Manufacturing Technology. 2016; 88 (5-8):1215-1230.
Chicago/Turabian StyleJumyung Um; Matthieu Rauch; Jean-Yves Hascoët; Ian Stroud. 2016. "STEP-NC compliant process planning of additive manufacturing: remanufacturing." The International Journal of Advanced Manufacturing Technology 88, no. 5-8: 1215-1230.
Jumyung Um; Suk-Hwan Suh; Ian Stroud. STEP-NC machine tool data model and its applications. International Journal of Computer Integrated Manufacturing 2016, 29, 1058 -1074.
AMA StyleJumyung Um, Suk-Hwan Suh, Ian Stroud. STEP-NC machine tool data model and its applications. International Journal of Computer Integrated Manufacturing. 2016; 29 (10):1058-1074.
Chicago/Turabian StyleJumyung Um; Suk-Hwan Suh; Ian Stroud. 2016. "STEP-NC machine tool data model and its applications." International Journal of Computer Integrated Manufacturing 29, no. 10: 1058-1074.
There is increasing need for manufacturing organisations to implement lean, just-in-time, make-to-order systems, mainly due to the cost pressures and varying customer preferences. This creates unexpected disturbances within the manufacturing systems, causing delays in delivery time. In order to quickly identify and react to disturbances, it is vital to capture real-time dynamic information related to the parts in production, resources, inventory levels and quality information. In many literatures, product intelligence achieves the fundamental requirements of managing disturbance. Current challenge, however, is that existing researches focused on developing a tracking system dealing with specific disturbance. In this paper, we present a systematic guideline for implementing such a system. The proposed guideline uses principles of product intelligence and combines them with the characteristics of disturbances and the associated information requirements. A case example is also presented to illustrate the developed concepts.
Jumyung Um; Rengarajan Srinivasan; Alan Thorne; Duncan McFarlane. Smart tracking to enable disturbance tolerant manufacturing through enhanced product intelligence. 2015 IEEE 13th International Conference on Industrial Informatics (INDIN) 2015, 1354 -1360.
AMA StyleJumyung Um, Rengarajan Srinivasan, Alan Thorne, Duncan McFarlane. Smart tracking to enable disturbance tolerant manufacturing through enhanced product intelligence. 2015 IEEE 13th International Conference on Industrial Informatics (INDIN). 2015; ():1354-1360.
Chicago/Turabian StyleJumyung Um; Rengarajan Srinivasan; Alan Thorne; Duncan McFarlane. 2015. "Smart tracking to enable disturbance tolerant manufacturing through enhanced product intelligence." 2015 IEEE 13th International Conference on Industrial Informatics (INDIN) , no. : 1354-1360.
Due to concerns about environmental protection, product life cycle management for end-of-life has received increasing attention in many industrial sectors. To support these functions, crucial issues have been studied to realize a product recovery management system. Until the present time most research has been concerned with decision making under the assumption that all the relevant and accurate information about a product is available by some means. However, these pieces of research ended in technological attempts because of the development complexity of implementation using ubiquitous computing devices such as identification chips and embedded systems to get data from products. An efficient development method is necessary in order to overcome this limitation. In this paper we overview a generic architecture based on ubiquitous computing technology. This is followed by how to develop such an innovative system by proposing a systematic approach called ubiquitous information engineering. To show the effectiveness of the architecture and approach a prototype for remanufacturing an industrial product has been developed. Through development of the proposed approach enough functions can be derived to collect information from a product. The study shows that major factors influencing development complexity are found and that information standards support network development in end-of-life activities.
Jumyung Um; Suk-Hwan Suh. Design method for developing a product recovery management system based on life cycle information. International Journal of Precision Engineering and Manufacturing-Green Technology 2015, 2, 173 -187.
AMA StyleJumyung Um, Suk-Hwan Suh. Design method for developing a product recovery management system based on life cycle information. International Journal of Precision Engineering and Manufacturing-Green Technology. 2015; 2 (2):173-187.
Chicago/Turabian StyleJumyung Um; Suk-Hwan Suh. 2015. "Design method for developing a product recovery management system based on life cycle information." International Journal of Precision Engineering and Manufacturing-Green Technology 2, no. 2: 173-187.
Since eco-efficiency of manufacturing resource has been emphasized, various sensors to measure energy consumption have been developed and machine tool builders also provide data of energy consumption of their own products. Due to the variety and complexity of machine tools, however, an enormous amount of data is generated and can lead to uncertainties in further interpretation. The data relating to energy consumption can be classified into process parameters and machine specifications. In order to estimate the energy use that a new machine tool utilizes, the relationship with various performance indicators of the machine tool and a process plan should be examined. The challenge is how to link the machine specifications and process plan in order to obtain actual energy consumption. This paper proposes an approach for deriving an energy estimation model from general key performance indicators of the sustainability of machine tools. For the detailed application, the proposed methodology is applied to the laser welding process of an automotive assembly line and the milling process of an aircraft part manufacturer. The paper describes the methodology for finding the parameters necessary for calculating energy use and to develop the energy estimation model by utilizing experimental data
Jumyung Um; Adam Gontarz; Ian Stroud. Developing Energy Estimation Model Based on Sustainability KPI of Machine Tools. Procedia CIRP 2015, 26, 217 -222.
AMA StyleJumyung Um, Adam Gontarz, Ian Stroud. Developing Energy Estimation Model Based on Sustainability KPI of Machine Tools. Procedia CIRP. 2015; 26 ():217-222.
Chicago/Turabian StyleJumyung Um; Adam Gontarz; Ian Stroud. 2015. "Developing Energy Estimation Model Based on Sustainability KPI of Machine Tools." Procedia CIRP 26, no. : 217-222.