Production
https://prod.org.br/article/doi/10.1590/0103-6513.20210147
Production
Research Article

Challenges of manufacturing for energy efficiency: towards a systematic approach through applications of machine learning

Elaheh Gholamzadeh Nabati; Maria Teresa Alvela Nieto; Dennis Bode; Thimo Florian Schindler; André Decker; Klaus-Dieter Thoben

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Abstract

Paper aims: Due to increasing energy prices, manufacturers have to pay more attention to the energy efficiency of their production processes. This paper aims to support manufacturers in increasing processes’ energy efficiency by using production data and applying machine learning approaches.

Originality: Systematic guidelines or standards for minimising the energy consumption of manufacturing processes through machine learning approaches are still lacking. This gap is addressed in this paper.

Research method: The paper follows a qualitative research method to understand the manufacturing processes and their challenges in improving energy efficiency. The raw data for a 5-step approach were collected in research projects with manufacturing SMEs, and information about the processes through interviews and workshops with them. Then, an analysis of currently available machine learning frameworks and their selection and implementation is conducted.

Main findings: The main result is a 5-step approach for increasing the energy efficiency of manufacturing processes through machine learning. Essential applications and technical challenges for data mapping, integrating, modelling, implementing, and deploying machine learning algorithms in manufacturing processes for increasing energy efficiency are presented.

Implications for theory and practice: The findings can guide manufacturers, researchers, and data scientists to use machine learning in practice when they intend to increase the energy efficiency of manufacturing processes.

Keywords

Energy efficiency, Manufacturing processes, Machine learning

References

Ahmad, T., Zhu, H., Zhang, D., Tariq, R., Bassam, A., Ullah, F., Al Ghamdi, A. S., & Alshamrani, S. S. (2022). Energetics systems and artificial intelligence: applications of industry 4.0. Energy Reports, 8, 334-361. http://dx.doi.org/10.1016/j.egyr.2021.11.256.

Alvela Nieto, M. T., Nabati, E. G., Bode, D., Redecker, M.A., Decker, A., & Thoben, K.-D. (2019). Enabling energy efficiency in manufacturing environments through deep learning approaches: lessons learned. Cham: Springer. http://dx.doi.org/10.1007/978-3-030-29996-5_65.

Alvela Nieto, M. T., Nabati, E. G., & Thoben, K.-D. (2021). Energy transparency in compound feed production. In IFIP International Conference on Advances in Production Management Systems. Cham: Springer. http://dx.doi.org/10.1007/978-3-030-85914-5_53.

AutoML. (2022). Automated Machine Learning. Retrieved in 2022, March 29, from https://www.automl.org/

Azure, M. (2022). What is automated machine learning? Retrieved in 2022, March 29, from http://docs. microsoft.com/en-us/azure/machine-learning concept-automated-ml

Blesl, M., & Kessler, A. (2018). Energieeffizienz in der industrie. Berlin: Springer-Verlag GmbH. https://doi.org/10.1007/978-3-662-55999-4.

Botelho, S. S. C., Filho, N. D., Espindola, D., Amaral, M., Emmendorfer, L., Penna, R., Frazzon, E. M., Pereira, C. E., & Ventura, R. (2014). Including operator’s skill and environment conditions in IMS. In 2014 12th IEEE International Conference on Industrial Informatics (INDIN) (pp. 295-300). New York: IEEE. http://dx.doi.org/10.1109/INDIN.2014.6945527.

Bundesamt für Wirtschaft und Ausfuhrkontrolle – BAFA. (2020). Energieaudit. Retrieved in 2020, December 5, from https://www.bafa.de/DE/Energie/Energieeffizienz/Energieaudit/energieaudit_node.html

Daigneau, R. (2012). Service design patterns: fundamental design solutions for SOAP/WSDL and RESTful web services. Amsterdam: Addison-Wesley.

Darwish, M., Shehab, E., Al-Ashaab, A., & Haque, B. (2010). Value stream mapping and analysis of product development (engineering) process. In Proceedings of the 8th International Conference on Manufacturing Research (ICMR), (pp. 14-16). UK: University Durham.

Deutsches Institut für Normung – DIN. (2015). DIN EN ISO 9001:2008. Retrieved in 2022, April 13, from https://www.din.de/de/wdc-beuth:din21:235671251

DevOps. (2020). Where the world meets. Retrieved in 2020, August 10, from https://devops.com/

DuttaGupta, A. (2017). Energy efficiency using machine learning–targeting small and medium-sized manufactures. In Proceedings of the IIE Annual Conference (pp. 976-981). Norcross: Institute of Industrial and Systems Engineers.

Gleich, R., Bartels, P., & Breisig, V. (2012). Nachhaltigkeitscontrolling: konzepte, instrumente und fallbeispiele für die umsetzung. Germany: Haufe-Gruppe.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge: MIT Press. Retrieved in 2020, September 15, from http://www.deeplearningbook.org/

Hecker, D., Döbel, I., Rüping, S., & Schmitz, V. (2017). Künstliche Intelligenz und die Potenziale des maschinellen Lernens für die Industrie. Wirtschaftsinformatik & Management, 9(5), 26-35. http://dx.doi.org/10.1007/s35764-017-0110-6.

Herwig, H. (2016). Energie: richtig bewerten und sinnvoll nutzen: essentials (1st ed.). Wiesbaden: Springer-Verlag. http://dx.doi.org/10.1007/978-3-658-12920-0.

IBM. (2011). IBM-SPSS modeler crisp-dm guide. Retrieved in 2020, April 28, from https://www.ibm.com/docs/en/spss-modeler/18.1.1?topic=spss-modeler-crisp-dm-guide

International Energy Agency – IEA. (2017). Manufacturing energy consumption by subsector in selected IEA countries. Retrieved in 2020, September 15, from https://www.iea.org/data-and-statistics/charts/ manufacturing-energy-consumption-by-subsector-in-selected-iea-countries-2017

International Organization for Standardization – ISO. (2008). Software engineering: software product quality requirements and evaluation (SQuaRE): data quality model. Geneva: ISO. Retrieved in 2022, April 15, from https://www.iso.org/standard/35736.html

International Organization for Standardization – ISO. (2018). ISO 50001: energy management. Geneva: ISO. Retrieved in 2021, September 15, from https://www. iso.org/iso-50001-energy-management.html

International Organization for Standardization – ISO. (2019). Data quality: part 63: data quality management: process measurement. Geneva: ISO. Retrieved in 2022, April 15, from https://www.iso.org/standard/65344.html

Irrek, W., & Thomas, S. (2008). Defining energy efficiency. Retrieved in 2022, April 27, from https://wupperinst.org/uploads/tx_wupperinst/energy_efficiency_ definition.pdf

Kleppmann, W. (2016). Versuchsplanung: produkte und prozesse optimieren (Praxisreihe Qualitätswissen). München: Hanser.

Metaflow. (2021). A framework for real-life data science. Retrieved in 2021, July 11, from https: //metaflow.org/

Mills, E., Shamshoian, G., Blazek, M., Naughton, P., Seese, R. S., Tschudi, W., & Sartor, D. (2008). The business case for energy management in high-tech industries. Energy Efficiency, 1(1), 5-20. http://dx.doi.org/10.1007/s12053-007-9000-8.

MLOps. (2022). Machine Learning Operations. Retrieved in 2022, March 29, from https://ml-ops.org/

Monostori, L. (2003). AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing. . Engineering Applications of Artificial Intelligence, 16, 277-291. http://dx.doi.org/10.1016/S0952-1976(03)00078-2.

Nabati, E. G., Alvela Nieto, M. T., Decker, A., & Thoben, K.-D. (2020). Application of virtual reality technologies for achieving energy efficient manufacturing: literature analysis and findings. In IFIP International Conference on Advances in Production Management Systems. Cham: Springer. http://dx.doi.org/10.1007/978-3-030-57993-7_54.

Narciso, D. A., & Martins, F. (2020). Application of machine learning tools for energy efficiency in industry: a review. Energy Reports, 6, 1181-1199. http://dx.doi.org/10.1016/j.egyr.2020.04.035.

OPCFoundation. (2020). Overview. Retrieved in 2020, October 20, from https://reference.opcfoundation.org/v104/

Pyvovar, N. (2019). Data science project management methodologies. Retrieved in 2022, April 19 , from https://medium.datadriveninvestor.com/data-science-project-management-methodologies-f6913c6b29eb

Roy, J., & Ramanujan, A. (2001). Understanding Web services. IT Professional, 3(6), 69-73. http://dx.doi.org/10.1109/6294.977775.

Schaefer, J. L., Carvalho, P. S., Ruhoff, A., Thomas, J. D., & Siluk, J. C. M. (2021). Permeability evaluation of Industry 4.0 technologies in cloud-based energy management systems environments - Energy Cloud. Production, 31, e20210038. http://dx.doi.org/10.1590/0103-6513.20210038.

Scikit Learn. (2007). Novelty and outlier detection. Retrieved in 2020, October 5, from https://scikit-learn.org/ stable/modules/outlier_detection.html

Scrum Alliance. (2015). Overview: what is Scrum? Retrieved in 2020, July 11, from https://www.scrumalliance.org/about-scrum

Sen, D., Ozturk, M., & Vayvay, O. (2016). An overview of big data for growth in SMEs. Procedia: Social and Behavioral Sciences, 235, 159-167. http://dx.doi.org/10.1016/j.sbspro.2016.11.011.

Seow, Y., & Rahimifard, S. (2011). A framework for modelling energy consumption within manufacturing systems. CIRP Journal of Manufacturing Science and Technology, 4(3), 258-264. http://dx.doi.org/10.1016/j.cirpj.2011.03.007.

Song, B., Ao, Y., Xiang, L., & Lionel, K. (2018). Data-driven approach for discovery of energy saving potentials in manufacturing factory. Procedia CIRP, 69, 330-335. http://dx.doi.org/10.1016/j.procir.2017.11.143.

Swagger. (2020). API development for everyone. Retrieved in 2020, November 18, from https://swagger.io/

Tan, D., Suvarna, M., Tan, Y. S., Li, J., & Wang, X. (2021). A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing. Applied Energy, 291, 116808. http://dx.doi.org/10.1016/j.apenergy.2021.116808.

Thiede, S. (2012). Energy efficiency in manufacturing systems. In C. Herrmann & S. Kara (Eds.), Sustainable production, life cycle engineering and management. Berlin: Springer.

Thiede, S., Turetskyy, A., Loellhoeffel, T., Kwade, A., Kara, S., & Herrmann, C. (2020). Machine learning approach for systematic analysis of energy efficiency potentials in manufacturing processes: a case of battery production. CIRP Annals, 69(1), 21-24. http://dx.doi.org/10.1016/j.cirp.2020.04.090.

Tuev. (2020). Energy audit according to DIN EN 162471. Retrieved in 2020, December 5, from https://www.tuev-nord.de/en/company/energy/energy-efficiency/energy-efficiency-in-trade-and-industry/energy-audit-according-to-din-en-16247-1/

Uber. (2017). Meet Michelangelo: Uber’s Machine Learning Platform. Retrieved in 2021, December 12, from https://eng.uber.com/michelangelo-machine-learning-platform/

Verein Deutscher Ingenieure – VDI. (2019). VDI 4663 blatt 1: bewertung von energie- und stoffeffizienz - methodische anwendung des physikalischen optimums. Berlin: VDI.

Vilalta, R., & Drissi, Y. (2002). A perspective view and survey of metalearning. Artificial Intelligence Review, 18(2), 77-95. http://dx.doi.org/10.1023/A:1019956318069.

Yu, L., & Liu, H. (2004). Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research, 5, 1205-1224.

Zhong, Q., Tang, R., Lv, J., Jia, S., & Jin, M. (2016). Evaluation on models of calculating energy consumption in metal cutting processes: a case of external turning process. International Journal of Advanced Manufacturing Technology, 82(9-12), 2087-2099. http://dx.doi.org/10.1007/s00170-015-7477-4.
 


Submitted date:
12/29/2021

Accepted date:
06/03/2022

62c82b4ba9539544e3535494 production Articles
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