An integrated energy efficiency evaluation method for forging workshop based on IoT and data-driven. (October 2022)

• Record Type: Journal Article
• Authors: Zhang, Hongwei; Li, Li; Li, Lingling; Cai, Wei; Liu, Jiefei; Sutherland, John W.
• Is Part Of: Journal of manufacturing systems. Volume 65(2022)
• Page Start: 510
• Page End: 527
• Publication Date: 2022-10
• Subjects: Energy efficiency evaluation -- Forging workshop -- IoT -- Data-driven; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2022.10.010
• Languages: English
• ISSNs: 0278-6125

Abstract

Abstract: Forging is an industry with high emissions and high energy consumption. Due to the constant increase in the international energy price and the negative impact on the world's environment, researchers have begun to focus on transforming forging into information and sustainable development. However, there remains a substantial gap in the development of the energy management process and energy-efficiency evaluation for complicated forging processes. Therefore, this paper defines a multi-level energy flow model and energy indicators with forging energy characteristics. Then, an energy management system for energy efficiency evaluation is developed based on IoT and data-driven. The energy consumption data are conceived for collection, transmission, storage, processing, mining, and application. Moreover, an integrated energy efficiency evaluation method is detailed with the system energy consumption data. Finally, a case study is carried out to verify the proposal's feasibility and credibility. Through the proposed evaluation method, a forging enterprise can make energy efficiency-optimization decisions. Highlights: Analyzing energy flows and defining energy efficiency indicators for the forge. Developing a system for energy efficiency based on IoT and data-driven. Exploring an integrated energy efficiency evaluation method from multi-layer. Illustrating the reliability and practical performance of the proposed method.