Robotic Automation in Rubber Processing: Improving Safety and Productivity
Abstract
This study examines the effect of robotic automation on productivity and safety in the rubber processing sector. The primary goals were to evaluate the advantages of robotic integration, do a cost-benefit analysis, and investigate the implications of the adoption policy. A thorough literature research, case study analysis, and industry expert interviews were all part of the process. Important discoveries show that robotic automation considerably improves workplace safety by lowering the risks associated with manual labor and chemical exposure. Continuous operation, precision production, and enhanced quality control contributed to productivity gains. The cost-benefit analysis shows significant long-term labor cost savings and improved productivity. The policy implications underscore the significance of labor training, legislative assistance, and technological integration in enabling extensive adoption. The revolutionary potential of robotic automation in rubber processing is highlighted by this study, which provides policymakers and industry stakeholders with ideas on how to use technology to promote competitiveness and sustainable growth.
References
Anumandla, S. K. R., Yarlagadda, V. K., Vennapusa, S. C. R., & Kothapalli, K. R. V. (2020). Unveiling the Influence of Artificial Intelligence on Resource Management and Sustainable Development: A Comprehensive Investigation. Technology & Management Review, 5, 45-65. https://upright.pub/index.php/tmr/article/view/145
Ascari, L., Bertocchi, U., Corradi, P., Laschi, C., Dario, P. (2009). Bio-inspired Grasp Control in a Robotic Hand with Massive Sensorial Input. Biological Cybernetics, 100(2), 109-28. https://doi.org/10.1007/s00422-008-0279-0 DOI: https://doi.org/10.1007/s00422-008-0279-0
Beckerle, P., Kõiva, R., Kirchner, E. A., Bekrater-Bodmann, R., Dosen, S. (2018). Feel-Good Robotics: Requirements on Touch for Embodiment in Assistive Robotics. Frontiers in Neurorobotics. https://doi.org/10.3389/fnbot.2018.00084 DOI: https://doi.org/10.3389/fnbot.2018.00084
Dakhli, Z., Lafhaj, Z. (2017). Robotic Mechanical Design for Brick-laying Automation. Cogent Engineering, 4(1). https://doi.org/10.1080/23311916.2017.1361600 DOI: https://doi.org/10.1080/23311916.2017.1361600
Indri, M., Trapani, S., Lazzero, I. (2017). Development of a Virtual Collision Sensor for Industrial Robots. Sensors, 17(5), 1148. https://doi.org/10.3390/s17051148 DOI: https://doi.org/10.3390/s17051148
Khair, M. A., Tejani, J. G., Sandu, A. K., & Shajahan, M. A. (2020). Trade Policies and Entrepreneurial Initiatives: A Nexus for India’s Global Market Integration. American Journal of Trade and Policy, 7(3), 107–114. https://doi.org/10.18034/ajtp.v7i3.706 DOI: https://doi.org/10.18034/ajtp.v7i3.706
Kothapalli, K. R. V., Tejani, J. G., Rajani Pydipalli, R. (2021). Artificial Intelligence for Microbial Rubber Modification: Bridging IT and Biotechnology. Journal of Fareast International University, 4(1), 7-16.
Mullangi, K., Anumandla, S. K. R., Maddula, S. S., Vennapusa, S. C. R., & Mohammed, M. A. (2018). Accelerated Testing Methods for Ensuring Secure and Efficient Payment Processing Systems. ABC Research Alert, 6(3), 202–213. https://doi.org/10.18034/ra.v6i3.662 DOI: https://doi.org/10.18034/ra.v6i3.662
Muradore, R., Fiorini, P., Akgun, G., Barkana, D. E., (2015). Development of a Cognitive Robotic System for Simple Surgical Tasks. Bonfe, M. International Journal of Advanced Robotic Systems, 12(4). https://doi.org/10.5772/60137 DOI: https://doi.org/10.5772/60137
Nacy, S. M., Tawfik, M. A., Baqer, I. A. (2017). A Novel Approach to Control the Robotic Hand Grasping Process by Using an Artificial Neural Network Algorithm. Journal of Intelligent Systems, 26(2), 215-231. https://doi.org/10.1515/jisys-2015-0115 DOI: https://doi.org/10.1515/jisys-2015-0115
Natakam, V. M., Nizamuddin, M., Tejani, J. G., Yarlagadda, V. K., Sachani, D. K., & Karanam, R. K. (2022). Impact of Global Trade Dynamics on the United States Rubber Industry. American Journal of Trade and Policy, 9(3), 131–140. https://doi.org/10.18034/ajtp.v9i3.716 DOI: https://doi.org/10.18034/ajtp.v9i3.716
Pydipalli, R. (2020). AI-Driven Metabolic Engineering for Microbial Rubber Conversion: IT-enabled Strategies. Asian Journal of Applied Science and Engineering, 9(1), 209–220. https://doi.org/10.18034/ajase.v9i1.89 DOI: https://doi.org/10.18034/ajase.v9i1.89
Pydipalli, R. (2021). Bioinformatics Tools and IT Infrastructure for High-Throughput Genomic Data Analysis. Digitalization & Sustainability Review, 1(1), 103-115. https://upright.pub/index.php/dsr/article/view/146
Pydipalli, R., & Tejani, J. G. (2019). A Comparative Study of Rubber Polymerization Methods: Vulcanization vs. Thermoplastic Processing. Technology & Management Review, 4, 36-48. https://upright.pub/index.php/tmr/article/view/132
Roberts, C., Pydipalli, R., Tejani, J. G., & Nizamuddin, M. (2021). Green Chemistry Approaches to Vulcanization: Reducing Environmental Impact in Rubber Manufacturing. Asia Pacific Journal of Energy and Environment, 8(2), 67-76. https://doi.org/10.18034/apjee.v8i2.750 DOI: https://doi.org/10.18034/apjee.v8i2.750
Rodriguez, M., Tejani, J. G., Pydipalli, R., & Patel, B. (2018). Bioinformatics Algorithms for Molecular Docking: IT and Chemistry Synergy. Asia Pacific Journal of Energy and Environment, 5(2), 113-122. https://doi.org/10.18034/apjee.v5i2.742 DOI: https://doi.org/10.18034/apjee.v5i2.742
Sachani, D. K., Anumandla, S. K. R., Maddula, S. S. (2022). Human Touch in Retail: Analyzing Customer Loyalty in the Era of Self-Checkout Technology. Silicon Valley Tech Review, 1(1), 1-13.
Sachani, D. K., Dhameliya, N., Mullangi, K., Anumandla, S. K. R., & Vennapusa, S. C. R. (2021). Enhancing Food Service Sales through AI and Automation in Convenience Store Kitchens. Global Disclosure of Economics and Business, 10(2), 105-116. https://doi.org/10.18034/gdeb.v10i2.754 DOI: https://doi.org/10.18034/gdeb.v10i2.754
Sandu, A. K., Pydipalli, R., Tejani, J. G., Maddula, S. S., & Rodriguez, M. (2022). Cloud-Based Genomic Data Analysis: IT-enabled Solutions for Biotechnology Advancements. Engineering International, 10(2), 103–116. https://doi.org/10.18034/ei.v10i2.712 DOI: https://doi.org/10.18034/ei.v10i2.712
Stöckli, F., Modica, F., Shea, K. (2016). Designing Passive Dynamic Walking Robots for Additive Manufacture. Rapid Prototyping Journal, 22(5), 842-847. https://doi.org/10.1108/RPJ-11-2015-0170 DOI: https://doi.org/10.1108/RPJ-11-2015-0170
Tejani, J. G. (2017). Thermoplastic Elastomers: Emerging Trends and Applications in Rubber Manufacturing. Global Disclosure of Economics and Business, 6(2), 133-144. https://doi.org/10.18034/gdeb.v6i2.737 DOI: https://doi.org/10.18034/gdeb.v6i2.737
Tejani, J. G. (2019). Innovative Approaches to Recycling Rubber Waste in the United States. ABC Research Alert, 7(3), 181–192. https://doi.org/10.18034/ra.v7i3.660 DOI: https://doi.org/10.18034/ra.v7i3.660
Tejani, J. G. (2020). Advancements in Sustainable Rubber Production: Bio-Based Alternatives and Recycling Technologies. ABC Journal of Advanced Research, 9(2), 141-152. https://doi.org/10.18034/abcjar.v9i2.749 DOI: https://doi.org/10.18034/abcjar.v9i2.749
Tejani, J. G. (2023). The Influence of Crosslinking Agents on the Properties of Thermoplastic Elastomers. Silicon Valley Tech Review, 2(1), 1-12.
Tejani, J. G., Khair, M. A., & Koehler, S. (2021). Emerging Trends in Rubber Additives for Enhanced Performance and Sustainability. Digitalization & Sustainability Review, 1(1), 57-70. https://upright.pub/index.php/dsr/article/view/130
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