The Use of Big Data for Sustainable Development in Motor Production Line Issues

Abstract

<jats:p>This study explores big data gathered from motor production lines to gain a better understanding of production line issues. Motor products from Solen Electric Company’s motor production lines were used to predict failure points based on big data analytics, where 3606 datapoints from the company’s testing equipment were statistically analyzed. The current study focused on secondary data and expert interview results to further define the relevant statistical dimensions. Only 14 of the original 88 detection parameters were required for monitoring the production line. The relationships between these parameters and the relevant motor components were established to indicate how an abnormal reading may be interpreted to quickly resolve an issue. Thus, a theoretical model for the monitoring of the motor production line was proposed. Further implications and practical suggestions are also offered to improve the production lines. This study explores big data analysis and smart manufacturing and demonstrates the promise of these technologies in improving production line efficiency and reducing waste to promote sustainable production goals. Big data thus constitute the core technology for advancing production lines into Industry 4.0 and promoting industry sustainability.</jats:p>

Industry 4.0 Accelerating Sustainable Manufacturing in the COVID-19 Era: Assessing the Readiness and Responsiveness of Italian Regions

“…The acceleration of I4.0 trends will also unlock major opportunities for sustainable manufacturing. Lin et al [30] emphasize that in the future of I4.0 applications, Big Data will drive sustainable development, especially in relation to the Sustainable Development Goals introduced by the United Nations (UN) in 2015. Braccini and Margherita [31] confirm, through a single case study of a manufacturing company, that I4.0 applications can support the economic, social, and environmental dimensions of sustainability.…”

Knowledge Management for Sustainable Development in the Era of Continuously Accelerating Technological Revolutions: A Framework and Models

“…Driven by cost advantages of commodity hardware and open-source software, increased investment in data systems, as a percent of IT spending on one hand, while on the other hand opening opportunities for innovative new business models and new insights that drive competitive advantages, which resulted in data stored (and collected) everywhere and in different formats, which continues to grow exponentially (for example Internet of Things, smart factories, etc., [115]). What followed was a major development in data analytics tools and new generations of Artificial Intelligence models and tools that enhanced organizational knowledge management capabilities (see example [116]) and can support sustainable development (see examples at [117,118]).…”

Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model

“…The machining process of blanks includes turning, milling, planing, grinding, etc., as shown in Figure 1. In the machining process, the input flow includes the blank, cutting fluid, electric energy driving the machine tool, tools, and fixtures, and the output flow consists of the loss of chips, cutting fluid, and cutting tools [25][26][27][28][29][30][31][32]. The carbon emissions caused by the output stream are mainly carbon emissions of materials in the machining process and carbon emissions from energy consumption, which can be expressed by Equation (1);…”

“…If we only consider environmental factors in the manufacturing process, this will inevitably increase the processing cost, so we should consider the environment, cost, and other factors in the process design. The machining cost of machine tools mainly includes the cutting tool replacement cost, processing cost, and other auxiliary costs [24,27,28].…”

“…The inertia weight and the flight time of particles can be adjusted adaptively according to the global optimal value information. The adjustment formula has been defined in Equations (27) and (28). (28) where t w is the inertia weight of the particle of generation t , t The optimization process of the APSO algorithm is shown in Figure 4.…”