- Email: [email protected]
Artificial neural network based framework for cyber nano manufacturing
Accepted Manuscript Letters Artificial Neural Network based framework for Cyber Nano Manufacturing Huraish Almakaeel, Ahmed Albalawi, Salil Desai PII: DOI: Reference:
S2213-8463(17)30094-9 https://doi.org/10.1016/j.mfglet.2017.12.013 MFGLET 116
To appear in:
Manufacturing Letters
Received Date: Revised Date: Accepted Date:
31 October 2017 18 December 2017 18 December 2017
Please cite this article as: H. Almakaeel, A. Albalawi, S. Desai, Artificial Neural Network based framework for Cyber Nano Manufacturing, Manufacturing Letters (2017), doi: https://doi.org/10.1016/j.mfglet.2017.12.013
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Artificial Neural Network based framework for Cyber Nano Manufacturing
Huraish Almakaeel Department of Industrial and Systems Engineering, North Carolina A&T State University, 419 McNair Hall, 1601, East Market St. Greensboro, NC, 27411. E-mail: [email protected] Ahmed Albalawi Department of Industrial and Systems Engineering, North Carolina A&T State University, 419 McNair Hall, 1601, East Market St. Greensboro, NC, 27411. E-mail: [email protected] Salil Desai1 Department of Industrial and Systems Engineering, North Carolina A&T State University, 422-B McNair Hall, 1601, East Market St Greensboro, NC, 27411. E-mail: [email protected] Phone/fax: (336) 285 3725/ (336) 334 7729
1
Corresponding Author
1
Abstract Nanomanufacturing plays an important role for high performance products in several applications. The challenge for fabricating products with nanomaterials is the inability to interconnect and interface with nano/micro manufacturing equipment. This paper presents a framework for cyber nanomanufacturing. Input part designs of nano/micro scale components are evaluated with an artificial neural network (ANN) based smart agent to predict optimal nanomanufacturing processes. An internet-of-things (IoT) based cyber-interface simulator is implemented to simulate real-time machine availability. Further, an application program interface (API) is developed to integrate the ANN smart agent and IoT simulator outcomes to predict dynamic machine allocations in real-time. Keywords:
artificial
neural
network,
cyber-physical
systems,
internet-of-things
nanomanufacturing, smart agent. 1.0
Introduction Cyber manufacturing refers to the utilization of internet technologies and software
architecture to conduct collaborative manufacturing activities in interdisciplinary areas across geographical regions [1–3]. Cyber-physical manufacturing systems have the potential to transform the manufacturing industry in a significant fashion by sharing manufacturing resources with customers over the Internet [4–6]. However, key manufacturing technologies such as Nano Manufacturing (NM) require precision control, distributed networking and cloud computing backbone to enhance their accessibility via service hubs [7,8]. Nanomanufacturing has encompassed major products and shows great promise for several applications [9–13]. The major challenge for achieving products fabricated with nano materials is the ability to interconnect and 2
interface with nano/micro manufacturing equipment. Cyberspace is an ideal way to connect disparate units of nano/micro scale facilities to meet the growing user needs. This paper provides a framework for seamless integration of a nano/micro scale platform over the cyber network with the development of three different sub-systems. Our team has developed a smart agent system which can dynamically allocate nano/micro resources to different input jobs over the cyber network.
2.0 Cyber Nanomanufacturing Framework The proposed Cyber Nano Manufacturing framework consists of three sub-systems which include: (1) Artificial Neural Network based Smart Agent, (2) Cyber Interface Simulator, and (3) Dynamic Machine Identification System. The first system utilizes artificial neural networks (ANN) algorithm to predict the optimal nano/micro manufacturing process based on a set of input design requirements. The second sub-system includes the development of a Cyber Interface Simulator which utilizes the internet of things (IoT) program for real-time allocation of nano/micro manufacturing resources. An application program interface (API) was developed to integrate inputs from the ANN algorithm and IoT interface to allocate nanomanufacturing resources over the cyber network.
3
Figure 1: Framework for cyber nanomanufacturing Figure 1 shows a schematic of the cyber nanomanufacturing (NM) framework. The input part characteristics were extracted to populate a datasheet. CAD inputs from the nano/micro scale digital design were processed for feature extraction. In addition, user inputs were obtained for each part design. A knowledge base was generated by compilation of nano/micro manufacturing literature, subject matter feedback and best practices from NM original equipment manufacturers (OEMs). These include process capability data sheets from original equipment manufacturers (OEMs) and expert opinion documented in technical journals and published media. An IoT device simulator was used to retrieve information on the nano manufacturing
4
process availability and capability. These inputs were preprocessed and compiled in a datasheet to be fed to the smart cyber agent based on artificial neural network algorithm (ANN). The ANN algorithm was used to classify each part design based on inputs from the knowledge base and the IoT device interface. 3.1 Artificial Neural Network (ANN) Based Expert System
Figure 2: Artificial Neural Network based Smart Agent System Typically, high-end nano and micro machines operate on a standalone basis limiting their application potential to a wider user base. The advent of cyber networking and distributed computing has permitted connectivity of both users and service providers with a variety of options to process their nano and micro scale designs. However, a key impediment is the ability to evaluate nano and micro scale designs for their assignment to different nano/micro manufacturing technologies. This is due to the fact that an input part design can be processed on several different techniques based on its topological features, geometric complexity, process throughput and other user specified constraints. In addition, the nano/micro machine
5
assignment/selection is also dependent on the process capability data for each equipment. Thus, the selection of appropriate nano/micro manufacturing process for each input part design is a complex multicriteria decision making problem. Further, selecting the appropriate machine assignment has high sensitivity to minor changes in part design, user specifications and machine capability. Our team has implemented an artificial neural network based smart agent system that translates input CAD designs, user specifications and knowledge base for selecting optimal nano/micro manufacturing processes. Artificial neural network is a self-adaptive flexible, powerful, and data-driven tool. It is an algorithm that can compute nonlinear and complex underlying characteristics with a high degree of accuracy. It has the advantage of being implemented in parallel architectures such as graphical processing units (GPUs), thereby shrinking the solution processing time for real-time implementation on cyber networks. Therefore, artificial neural networks can be successfully used to support nanoscale manufacturing. Figure 2 shows the input variables that are extracted from the input CAD design and user specifications for each part design. The output variables were the five candidate processes which include: Dip Pen Nanolithography (DPN), Nanoimprint Lithography (NIL), Photolithography (PHO), Pulse Laser Deposition (PLD), and Self Assembly (SA). These nano/micro manufacturing processes were selected based on their unique process capabilities to process a wide range of nano/micro designs. The Levenberg-Marquardt training algorithm was implemented in MATLAB source code to evaluate 200 part designs by iteratively updating their weight and bias values to minimize the mean square error. The ANN was trained for over 500 epochs to improve its performance to an average accuracy of 92% with a high correlation coefficient between the output and target values (R-value = 0.934). The ANN based smart agent system provided an unbiased and effective method of predicting optimal nano/micro machine
6
based on input part design. This system is flexible to include user-defined criteria and can be extended further to include a higher number of input variables, output processes and CAD designs. 3.2 Cyber Interface Simulator (IoT)
Figure 3: Cyber Interface Simulator for internet of things (IoT) In cyber-physical systems, the assignment of input part designs to different NM machines relies not only on the accuracy of the ANN based smart agent but also on the availability of appropriate process capability. Thus, to simulate NM machine availability over the cyber 7
network we have developed an Internet of Things (IoT) interface. The Node-Red [14,15] IoT simulator was chosen to create a Cyber Interface Simulator as shown in Figure 3. Different nodes were programmed to simulate input trigger event, machine availability generator and dashboard interface. An input trigger was provided to initiate time for an interval of 2 hours to check the availability of nano machines on the cyber network. A function generator was coded using JavaScript to generate the different machines available on the network based on a distribution function. The dashboard interface developed (Figure 3) displayed the number of machines available on the network with their time history plot. A database was populated with the machine availability array (Figure 3) for each time period. The cyber interface simulator was executed to record data sets for the machines available on the network for different time periods (e.g. 24 hrs., 48 hrs., etc.). It is important to note that the IoT interface developed was able to capture variations in both the number of machines available and the type of machine available over the network. The machine availability function generator can also be updated to simulate a variety of distribution functions that represent real-time resource availability on the cyber network.
8
3.3
Dynamic Nano Machine Identification System
Figure 4: Dynamic Nano Machine Identification System The dynamic nano machine identification system was developed to integrate the output from both the ANN based smart agent and the cyber interface simulator as show in Figure 4. As described in section 3.1, the ANN based smart agent predicted the assignment of the optimal nano/micro manufacturing process for an input part design. However, the actual assignment of an input part to a specific machine is also dependent on the resource availability over the cyber network which was obtained from the IoT simulator. The dynamic machine identification system evaluates the compatibility of two systems to predict real-time (dynamic) nano/micro machine assignment. An application program interface (API) was coded in Java programming language to compute optimal assignment between ANN and IoT (Node-Red) machine availability arrays. Using this system, the percentage of input part designs being assigned to optimal NM machines was calculated. It is defined as the ratio of number of assigned parts on specific machines to the total number of input parts. For the ANN smart agent the percentage assignment varied between 9
84 to 96% based on the prediction accuracy of the ANN algorithm. However, when the ANN prediction accuracy was computed along with the machine availability array for different time points, a much lower prediction accuracy was observed. This is due to the fact that even though the ANN algorithm had higher prediction accuracies, the real-time machine availability may not matchup with the ANN machine predictions. Figure 4 (chart) shows the variation in the percentage of input part designs being assigned to nano/micro machines for both ANN predictions and dynamic machine identification system. Thus, the dynamic machine identification system represents the real-time behavior of the cyber nano manufacturing network when integrated with the ANN based smart agent.
4.0
Conclusion This paper presents a framework for Cyber Nanomanufacturing (NM) that translates an
input nano/micro scale digital design to optimal process selection. An artificial neural network algorithm was used to evaluate the input features obtained through CAD feature extraction and user specifications. The smart agent system was implemented on 200 digital designs with an average accuracy of 92% to identify optimal nano/micro manufacturing processes. A cyberinterface simulator was coded in Node-Red IoT program to simulate the real-time availability of machines on the cyber network. A dashboard interface was developed which included machine availability gauge, time history tracking of machines and machine availability array. Finally, an application program interface (API) was programmed in Java to predict the dynamic machine allocation based on inputs from the ANN smart agent and cyber-interface simulator. 4.0
Acknowledgements
10
This material is based upon work supported by the National Science Foundation under Grant No. 1435649. References [1] [2]
[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]
Jeschke S, Brecher C, Meisen T, Özdemir D, Eschert T. Industrial Internet of Things and Cyber Manufacturing Systems TT -. Ind. Internet Things Cybermanufacturing Syst., Cham : Springer International Publishing : Springer; 2017, p. 3–19. Bareduan SA, Hasan SH, 11 2008 IEEE International Conference on Industrial Engineering and Engineering Management 2008 IEEE International Conference on Industrial Engineering and Engineering Management 2008 Dec. 8 - 2008 Dec. Bottleneck adjacent matching 4 (BAM4) heuristic for re-entrant flow shop with dominant machine TT -. 2008 IEEE Int. Conf. Ind. Eng. Eng. Manag., IEEE; 2008, p. 148–52. Wang L. Cyber Manufacturing: Research and Applications. 10th Int Symp Tools Methods Compet Eng Hungary, from 19 to 23 May, 2014 2014;1:39–49. Monostori L, Kádár B, Bauernhansl T, Kondoh S, Kumara S, Reinhart G, et al. Cyberphysical systems in manufacturing. CIRP Ann - Manuf Technol 2016;65:621–41. doi:10.1016/j.cirp.2016.06.005. Babiceanu RF, Seker R. Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook. Comput Ind 2016;81:128–37. doi:https://doi.org/10.1016/j.compind.2016.02.004. Wright P. Cyber-physical product manufacturing. Manuf Lett 2014;2:49–53. doi:https://doi.org/10.1016/j.mfglet.2013.10.001. Chryssolouris G, Stavropoulos P, Tsoukantas G, Salonitis K, Stournaras A. Nanomanufacturing processes: a critical review. Int J Mater Prod Technol 2004;21:331– 48. Liddle JA, Gallatin GM. Nanomanufacturing: a perspective. ACS Nano 2016;10:2995– 3014. Desai S, Craps M, Esho T. Direct writing of nanomaterials for flexible thin-film transistors (fTFTs). Int J Adv Manuf Technol 2013;64:537–43. Dimov S, Brousseau E, Minev R, Bigot S. Micro-and nano-manufacturing: Challenges and opportunities. Proc Inst Mech Eng Part C J Mech Eng Sci 2012;226:3–15. Desai S, Yang M, Xu Z, Sankar J. Direct write manufacturing of solid oxide fuel cells for green energy. J Environ Res Dev 2014;8:477. Li L, Hong M, Schmidt M, Zhong M, Malshe A, Huis B, et al. Laser nano-manufacturing– state of the art and challenges. CIRP Ann Technol 2011;60:735–55. Yang M, Xu Z, Desai S, Kumar D, Sankar J. Fabrication of Micro Single Chamber Solid Oxide Fuel Cell Using Photolithography and Pulsed Laser Deposition. J Fuel Cell Sci Technol 2015;12:21004. Miranda J, Mäkitalo N, Garcia-Alonso J, Berrocal J, Mikkonen T, Canal C, et al. From the Internet of Things to the Internet of People. IEEE Internet Comput 2015;19:40–7. Kleinfeld R, Steglich S, Radziwonowicz L, Doukas C. glue. things: a Mashup Platform for wiring the Internet of Things with the Internet of Services. Proc. 5th Int. Work. Web Things, ACM; 2014, p. 16–21.
11
S2213-8463(17)30094-9 https://doi.org/10.1016/j.mfglet.2017.12.013 MFGLET 116
To appear in:
Manufacturing Letters
Received Date: Revised Date: Accepted Date:
31 October 2017 18 December 2017 18 December 2017
Please cite this article as: H. Almakaeel, A. Albalawi, S. Desai, Artificial Neural Network based framework for Cyber Nano Manufacturing, Manufacturing Letters (2017), doi: https://doi.org/10.1016/j.mfglet.2017.12.013
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Artificial Neural Network based framework for Cyber Nano Manufacturing
Huraish Almakaeel Department of Industrial and Systems Engineering, North Carolina A&T State University, 419 McNair Hall, 1601, East Market St. Greensboro, NC, 27411. E-mail: [email protected] Ahmed Albalawi Department of Industrial and Systems Engineering, North Carolina A&T State University, 419 McNair Hall, 1601, East Market St. Greensboro, NC, 27411. E-mail: [email protected] Salil Desai1 Department of Industrial and Systems Engineering, North Carolina A&T State University, 422-B McNair Hall, 1601, East Market St Greensboro, NC, 27411. E-mail: [email protected] Phone/fax: (336) 285 3725/ (336) 334 7729
1
Corresponding Author
1
Abstract Nanomanufacturing plays an important role for high performance products in several applications. The challenge for fabricating products with nanomaterials is the inability to interconnect and interface with nano/micro manufacturing equipment. This paper presents a framework for cyber nanomanufacturing. Input part designs of nano/micro scale components are evaluated with an artificial neural network (ANN) based smart agent to predict optimal nanomanufacturing processes. An internet-of-things (IoT) based cyber-interface simulator is implemented to simulate real-time machine availability. Further, an application program interface (API) is developed to integrate the ANN smart agent and IoT simulator outcomes to predict dynamic machine allocations in real-time. Keywords:
artificial
neural
network,
cyber-physical
systems,
internet-of-things
nanomanufacturing, smart agent. 1.0
Introduction Cyber manufacturing refers to the utilization of internet technologies and software
architecture to conduct collaborative manufacturing activities in interdisciplinary areas across geographical regions [1–3]. Cyber-physical manufacturing systems have the potential to transform the manufacturing industry in a significant fashion by sharing manufacturing resources with customers over the Internet [4–6]. However, key manufacturing technologies such as Nano Manufacturing (NM) require precision control, distributed networking and cloud computing backbone to enhance their accessibility via service hubs [7,8]. Nanomanufacturing has encompassed major products and shows great promise for several applications [9–13]. The major challenge for achieving products fabricated with nano materials is the ability to interconnect and 2
interface with nano/micro manufacturing equipment. Cyberspace is an ideal way to connect disparate units of nano/micro scale facilities to meet the growing user needs. This paper provides a framework for seamless integration of a nano/micro scale platform over the cyber network with the development of three different sub-systems. Our team has developed a smart agent system which can dynamically allocate nano/micro resources to different input jobs over the cyber network.
2.0 Cyber Nanomanufacturing Framework The proposed Cyber Nano Manufacturing framework consists of three sub-systems which include: (1) Artificial Neural Network based Smart Agent, (2) Cyber Interface Simulator, and (3) Dynamic Machine Identification System. The first system utilizes artificial neural networks (ANN) algorithm to predict the optimal nano/micro manufacturing process based on a set of input design requirements. The second sub-system includes the development of a Cyber Interface Simulator which utilizes the internet of things (IoT) program for real-time allocation of nano/micro manufacturing resources. An application program interface (API) was developed to integrate inputs from the ANN algorithm and IoT interface to allocate nanomanufacturing resources over the cyber network.
3
Figure 1: Framework for cyber nanomanufacturing Figure 1 shows a schematic of the cyber nanomanufacturing (NM) framework. The input part characteristics were extracted to populate a datasheet. CAD inputs from the nano/micro scale digital design were processed for feature extraction. In addition, user inputs were obtained for each part design. A knowledge base was generated by compilation of nano/micro manufacturing literature, subject matter feedback and best practices from NM original equipment manufacturers (OEMs). These include process capability data sheets from original equipment manufacturers (OEMs) and expert opinion documented in technical journals and published media. An IoT device simulator was used to retrieve information on the nano manufacturing
4
process availability and capability. These inputs were preprocessed and compiled in a datasheet to be fed to the smart cyber agent based on artificial neural network algorithm (ANN). The ANN algorithm was used to classify each part design based on inputs from the knowledge base and the IoT device interface. 3.1 Artificial Neural Network (ANN) Based Expert System
Figure 2: Artificial Neural Network based Smart Agent System Typically, high-end nano and micro machines operate on a standalone basis limiting their application potential to a wider user base. The advent of cyber networking and distributed computing has permitted connectivity of both users and service providers with a variety of options to process their nano and micro scale designs. However, a key impediment is the ability to evaluate nano and micro scale designs for their assignment to different nano/micro manufacturing technologies. This is due to the fact that an input part design can be processed on several different techniques based on its topological features, geometric complexity, process throughput and other user specified constraints. In addition, the nano/micro machine
5
assignment/selection is also dependent on the process capability data for each equipment. Thus, the selection of appropriate nano/micro manufacturing process for each input part design is a complex multicriteria decision making problem. Further, selecting the appropriate machine assignment has high sensitivity to minor changes in part design, user specifications and machine capability. Our team has implemented an artificial neural network based smart agent system that translates input CAD designs, user specifications and knowledge base for selecting optimal nano/micro manufacturing processes. Artificial neural network is a self-adaptive flexible, powerful, and data-driven tool. It is an algorithm that can compute nonlinear and complex underlying characteristics with a high degree of accuracy. It has the advantage of being implemented in parallel architectures such as graphical processing units (GPUs), thereby shrinking the solution processing time for real-time implementation on cyber networks. Therefore, artificial neural networks can be successfully used to support nanoscale manufacturing. Figure 2 shows the input variables that are extracted from the input CAD design and user specifications for each part design. The output variables were the five candidate processes which include: Dip Pen Nanolithography (DPN), Nanoimprint Lithography (NIL), Photolithography (PHO), Pulse Laser Deposition (PLD), and Self Assembly (SA). These nano/micro manufacturing processes were selected based on their unique process capabilities to process a wide range of nano/micro designs. The Levenberg-Marquardt training algorithm was implemented in MATLAB source code to evaluate 200 part designs by iteratively updating their weight and bias values to minimize the mean square error. The ANN was trained for over 500 epochs to improve its performance to an average accuracy of 92% with a high correlation coefficient between the output and target values (R-value = 0.934). The ANN based smart agent system provided an unbiased and effective method of predicting optimal nano/micro machine
6
based on input part design. This system is flexible to include user-defined criteria and can be extended further to include a higher number of input variables, output processes and CAD designs. 3.2 Cyber Interface Simulator (IoT)
Figure 3: Cyber Interface Simulator for internet of things (IoT) In cyber-physical systems, the assignment of input part designs to different NM machines relies not only on the accuracy of the ANN based smart agent but also on the availability of appropriate process capability. Thus, to simulate NM machine availability over the cyber 7
network we have developed an Internet of Things (IoT) interface. The Node-Red [14,15] IoT simulator was chosen to create a Cyber Interface Simulator as shown in Figure 3. Different nodes were programmed to simulate input trigger event, machine availability generator and dashboard interface. An input trigger was provided to initiate time for an interval of 2 hours to check the availability of nano machines on the cyber network. A function generator was coded using JavaScript to generate the different machines available on the network based on a distribution function. The dashboard interface developed (Figure 3) displayed the number of machines available on the network with their time history plot. A database was populated with the machine availability array (Figure 3) for each time period. The cyber interface simulator was executed to record data sets for the machines available on the network for different time periods (e.g. 24 hrs., 48 hrs., etc.). It is important to note that the IoT interface developed was able to capture variations in both the number of machines available and the type of machine available over the network. The machine availability function generator can also be updated to simulate a variety of distribution functions that represent real-time resource availability on the cyber network.
8
3.3
Dynamic Nano Machine Identification System
Figure 4: Dynamic Nano Machine Identification System The dynamic nano machine identification system was developed to integrate the output from both the ANN based smart agent and the cyber interface simulator as show in Figure 4. As described in section 3.1, the ANN based smart agent predicted the assignment of the optimal nano/micro manufacturing process for an input part design. However, the actual assignment of an input part to a specific machine is also dependent on the resource availability over the cyber network which was obtained from the IoT simulator. The dynamic machine identification system evaluates the compatibility of two systems to predict real-time (dynamic) nano/micro machine assignment. An application program interface (API) was coded in Java programming language to compute optimal assignment between ANN and IoT (Node-Red) machine availability arrays. Using this system, the percentage of input part designs being assigned to optimal NM machines was calculated. It is defined as the ratio of number of assigned parts on specific machines to the total number of input parts. For the ANN smart agent the percentage assignment varied between 9
84 to 96% based on the prediction accuracy of the ANN algorithm. However, when the ANN prediction accuracy was computed along with the machine availability array for different time points, a much lower prediction accuracy was observed. This is due to the fact that even though the ANN algorithm had higher prediction accuracies, the real-time machine availability may not matchup with the ANN machine predictions. Figure 4 (chart) shows the variation in the percentage of input part designs being assigned to nano/micro machines for both ANN predictions and dynamic machine identification system. Thus, the dynamic machine identification system represents the real-time behavior of the cyber nano manufacturing network when integrated with the ANN based smart agent.
4.0
Conclusion This paper presents a framework for Cyber Nanomanufacturing (NM) that translates an
input nano/micro scale digital design to optimal process selection. An artificial neural network algorithm was used to evaluate the input features obtained through CAD feature extraction and user specifications. The smart agent system was implemented on 200 digital designs with an average accuracy of 92% to identify optimal nano/micro manufacturing processes. A cyberinterface simulator was coded in Node-Red IoT program to simulate the real-time availability of machines on the cyber network. A dashboard interface was developed which included machine availability gauge, time history tracking of machines and machine availability array. Finally, an application program interface (API) was programmed in Java to predict the dynamic machine allocation based on inputs from the ANN smart agent and cyber-interface simulator. 4.0
Acknowledgements
10
This material is based upon work supported by the National Science Foundation under Grant No. 1435649. References [1] [2]
[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]
Jeschke S, Brecher C, Meisen T, Özdemir D, Eschert T. Industrial Internet of Things and Cyber Manufacturing Systems TT -. Ind. Internet Things Cybermanufacturing Syst., Cham : Springer International Publishing : Springer; 2017, p. 3–19. Bareduan SA, Hasan SH, 11 2008 IEEE International Conference on Industrial Engineering and Engineering Management 2008 IEEE International Conference on Industrial Engineering and Engineering Management 2008 Dec. 8 - 2008 Dec. Bottleneck adjacent matching 4 (BAM4) heuristic for re-entrant flow shop with dominant machine TT -. 2008 IEEE Int. Conf. Ind. Eng. Eng. Manag., IEEE; 2008, p. 148–52. Wang L. Cyber Manufacturing: Research and Applications. 10th Int Symp Tools Methods Compet Eng Hungary, from 19 to 23 May, 2014 2014;1:39–49. Monostori L, Kádár B, Bauernhansl T, Kondoh S, Kumara S, Reinhart G, et al. Cyberphysical systems in manufacturing. CIRP Ann - Manuf Technol 2016;65:621–41. doi:10.1016/j.cirp.2016.06.005. Babiceanu RF, Seker R. Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook. Comput Ind 2016;81:128–37. doi:https://doi.org/10.1016/j.compind.2016.02.004. Wright P. Cyber-physical product manufacturing. Manuf Lett 2014;2:49–53. doi:https://doi.org/10.1016/j.mfglet.2013.10.001. Chryssolouris G, Stavropoulos P, Tsoukantas G, Salonitis K, Stournaras A. Nanomanufacturing processes: a critical review. Int J Mater Prod Technol 2004;21:331– 48. Liddle JA, Gallatin GM. Nanomanufacturing: a perspective. ACS Nano 2016;10:2995– 3014. Desai S, Craps M, Esho T. Direct writing of nanomaterials for flexible thin-film transistors (fTFTs). Int J Adv Manuf Technol 2013;64:537–43. Dimov S, Brousseau E, Minev R, Bigot S. Micro-and nano-manufacturing: Challenges and opportunities. Proc Inst Mech Eng Part C J Mech Eng Sci 2012;226:3–15. Desai S, Yang M, Xu Z, Sankar J. Direct write manufacturing of solid oxide fuel cells for green energy. J Environ Res Dev 2014;8:477. Li L, Hong M, Schmidt M, Zhong M, Malshe A, Huis B, et al. Laser nano-manufacturing– state of the art and challenges. CIRP Ann Technol 2011;60:735–55. Yang M, Xu Z, Desai S, Kumar D, Sankar J. Fabrication of Micro Single Chamber Solid Oxide Fuel Cell Using Photolithography and Pulsed Laser Deposition. J Fuel Cell Sci Technol 2015;12:21004. Miranda J, Mäkitalo N, Garcia-Alonso J, Berrocal J, Mikkonen T, Canal C, et al. From the Internet of Things to the Internet of People. IEEE Internet Comput 2015;19:40–7. Kleinfeld R, Steglich S, Radziwonowicz L, Doukas C. glue. things: a Mashup Platform for wiring the Internet of Things with the Internet of Services. Proc. 5th Int. Work. Web Things, ACM; 2014, p. 16–21.
11