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Machine learning approach for systematic analysis of energy efficiency potentials in manufacturing processes: A case of battery production
CIRP Annals ( IF 4.1 ) Pub Date : 2020-01-01 , DOI: 10.1016/j.cirp.2020.04.090
Sebastian Thiede , Artem Turetskyy , Thomas Loellhoeffel , Arno Kwade , Sami Kara , Christoph Herrmann

Energy efficiency in manufacturing plays a crucial role in decreasing manufacturing costs and reducing environmental footprint. This is particularly important for producing battery cells with novel processes due to their cost-sensitivity and high potential impact on the environment. Therefore, design and operation of these processes are critical and require a high level of process and machine specific understanding. A methodology based on machine learning is presented, which has the capability of identifying improvement potentials using machine and process specific influencing factors. A battery production case is used to demonstrate the accuracy, transferability and validity of the methodology.

中文翻译:

用于系统分析制造过程中能效潜力的机器学习方法:以电池生产为例

制造业的能源效率在降低制造成本和减少环境足迹方面起着至关重要的作用。由于成本敏感性和对环境的潜在影响很大,这对于采用新型工艺生产电池尤为重要。因此,这些过程的设计和操作至关重要,需要对过程和机器有高度的理解。提出了一种基于机器学习的方法,该方法能够使用机器和过程特定的影响因素来识别改进潜力。电池生产案例用于证明该方法的准确性、可转移性和有效性。
更新日期:2020-01-01
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