The optimization of energy consumption in product manufacture has analyzed
Keywords:
Robotic arm, Analytical Hierarchy Process (AHP), Energy consumption, SustainableAbstract
A scheme is given for optimizing the movement of the robotic arm with the help of AHP so that the minimum energy consumption criteria can be achieved. As compared to Direct Kinematics, Inverse Kinematics will evolve two solutions out of which the best-fit solution will be selected with the help of AHP and is kept in search space for future use. The importance of sustainable manufacturing has been widely discussed. The optimization of energy consumption in product manufacture has been deeply analyzed, mainly focusing on the energy directly absorbed by the manufacturing process. On the contrary, this paper focuses on the analysis and optimization of the energy consumption related to the robot arm, probably the most mathematically complex robot anyone could ever build, and we will present an optimized solution for the movement of a three-arm manipulator using the Analytical Hierarchy Process (AHP).
Downloads
References
Azadeh, A., Amalnick, M. S., Ghaderi, S. F., & Asadzadeh, S. M. (2007). An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors. Energy policy, 35(7), 3792-3806.
Bahrami, H., & Taki, M. (2011). Optimization of energy consumption for wheat production in Iran using data envelopment analysis (DEA) technique. African Journal of Agricultural Research, 6(27), 5978-5986.
Bunse, K., Vodicka, M., Schönsleben, P., Brülhart, M., & Ernst, F. O. (2011). Integrating energy efficiency performance in production management–gap analysis between industrial needs and scientific literature. Journal of Cleaner Production, 19(6-7), 667-679.
Cai, W., Liu, C., Lai, K. H., Li, L., Cunha, J., & Hu, L. (2019). Energy performance certification in mechanical manufacturing industry: A review and analysis. Energy Conversion and Management, 186, 415-432.
Camposeco-Negrete, C. (2013). Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA. Journal of Cleaner Production, 53, 195-203.
Camposeco-Negrete, C. (2015). Optimization of cutting parameters using Response Surface Method for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminum. Journal of cleaner production, 91, 109-117.
Cerdas, F., Juraschek, M., Thiede, S., & Herrmann, C. (2017). Life cycle assessment of 3D printed products in a distributed manufacturing system. Journal of Industrial Ecology, 21(S1), S80-S93.
Chi, Y., Guo, Z., Zheng, Y., & Zhang, X. (2014). Scenarios analysis of the energies’ consumption and carbon emissions in China based on a dynamic CGE Model. Sustainability, 6(2), 487-512.
Dietmair, A., & Verl, A. (2008, November). Energy consumption modeling and optimization for production machines. In 2008 IEEE International Conference on Sustainable Energy Technologies (pp. 574-579). IEEE.
Feng, Y., Hong, Z., Zhang, Z., Zhang, Z., & Tan, J. (2017). Interval analysis and DEMATEL-based reliability apportionment for energy consumption optimization with energy Internet. IEEE Access, 5, 4769-4778.
Franco, A., Lanzetta, M., & Romoli, L. (2010). Experimental analysis of selective laser sintering of polyamide powders: an energy perspective. Journal of Cleaner Production, 18(16-17), 17221730.
Gebisa, A. W., & Lemu, H. G. (2017). Design for manufacturing to design for Additive Manufacturing: Analysis of implications for design optimality and product sustainability. Procedia Manufacturing, 13, 724-731.
Hristu-Varsakelis, D., Karagianni, S., Pempetzoglou, M., & Sfetsos, A. (2010). Optimizing production with energy and GHG emission constraints in Greece: An input–output analysis. Energy Policy, 38(3), 1566-1577.
Huberman, N., & Pearlmutter, D. (2008). A life-cycle energy analysis of building materials in the Negev desert. Energy and Buildings, 40(5), 837-848.
Ingarao, G., Vanhove, H., Kellens, K., & Duflou, J. R. (2014). A comprehensive analysis of electric energy consumption of single point incremental forming processes. Journal of Cleaner Production, 67, 173-186.
Lindemann, C., Jahnke, U., Moi, M., & Koch, R. (2012, August). Analyzing product lifecycle costs for a better understanding of cost drivers in additive manufacturing. In 23th Annual International Solid Freeform Fabrication Symposium–An Additive Manufacturing Conference. Austin Texas USA 6th-8th August.
Mose, C., & Weinert, N. (2015). Process chain evaluation for an overall optimization of energy efficiency in manufacturing—The welding case. Robotics and Computer-Integrated Manufacturing, 34, 4451.
Peng, T. (2016). Analysis of energy utilization in 3d printing processes. Procedia Cirp, 40, 62-67.
Priarone, P. C. (2016). Quality-conscious optimization of energy consumption in a grinding process applying sustainability indicators. The International Journal of Advanced Manufacturing Technology, 86(5-8), 2107-2117.
Sarkar, B., Omair, M., & Choi, S. B. (2018). A multi-objective optimization of energy, economic, and carbon emission in a production model under sustainable supply chain management. Applied Sciences, 8(10), 1744.
Shao, G., Kibira, D., & Lyons, K. (2010, January). A virtual machining model for sustainability analysis. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 44113, pp. 875-883).
Shen, X., Chen, L., Xia, S., Xie, Z., & Qin, X. (2018). Burdening proportion and new energy-saving technologies analysis and optimization for iron and steel production system. Journal of Cleaner Production, 172, 2153-2166.
Tian, Y., Xiong, S., & Ma, X. (2017). Analysis of the potential impacts on China’s industrial structure in energy consumption. Sustainability, 9(12), 2284.
Zhang, Y., Ren, S., Liu, Y., & Si, S. (2017). A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. Journal of Cleaner Production, 142, 626-641.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Tennessee Research International of Social Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles published in the Tennessee Research International of Social Sciences (TRISS) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant TRISS right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.
Articles published in TRISS can be copied, communicated and shared in their published form for non-commercial purposes provided full attribution is given to the author and the journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.