Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries

Data-driven cleaner production strategy for energy-intensive manufacturing industries: Case studies from Southern and Northern China

Edge-cloud cooperation driven self-adaptive exception control method for the smart factory

Cloud Manufacturing (CMfg) has gained significant attention owing to its capability in reshaping the cooperation paradigm among multiple geographically dispersed enterprises, which is conducive to handle a complex production task flexibly through the industrial internet platform. Cloud Service Assembly (CSA) is concerned with integrating a series of services together for serving a complex manufacturing task, which, as one of bottlenecks for CMfg, plays a critical role in efficient utilization of resources. Evolutionary Algorithms (EAs) have been widely used in resolving CSA in the past. However, they are always executed from scratch for tackling a single task in each run, whereas handling a batch of tasks collectively via leveraging inter-task knowledge transfer has been scarcely studied. Notably, CMfg is often faced with situation of multiple tasks arriving dynamically. In light of this, we propose a Multi-task Transfer EA (MTEA), where several service collaboration tasks are optimized jointly to speed up the search efficiency by exploiting knowledge extraction among tasks. Specifically, data models derived from evolving populations are learned to capture valuable knowledge for transfer so as to boost problem-solving efficacy, a parameter online learning strategy is utilized to tune the intensity of knowledge transfer across tasks. Extensive experiments are conducted on a series of CSA instances, results prove the feasibility and competence of MTEA against state-of-the-art peers. • A transfer learning assisted search approach is proposed to optimize cloud service assembly tasks in Industrial Internet platform. • Overlap knowledge of tackling distinct tasks are extracted to leverage the search efficacy and solution quality. • Several strategies are embedded into the proposal to exploit the underlying relationships between tasks. • Comprehensive empirical studies reveal that the proposal is effective and promising in handling multiple tasks. 

Towards multi-task transfer optimization of cloud service collaboration in industrial internet platform

Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries

With the wide application of advanced industrial Internet of Things (IIoT) and cyber physical system (CPS) technologies, the manufacturing resources assignment method is transformed from manual and passive mode to intelligent and active mode. However, due to the lack of real-time analysis and accurate prediction of production performance, the production adjustment demands are often released after production exceptions happen, and production decisions are often made based on historical production information, which may lead to the problem of production interruption or performance reduction. To address this issue, a proactive manufacturing resources assignment (PMRA) method based on production performance prediction for the smart factory is proposed. First, the advanced IIoT and CPS technologies are applied to create a cloud-edge cooperation environment for a smart factory, where the resources are made smart with distributed control capacity, and cloud center and edge resources can collaborate dynamically. Second, a real-time colored Petri net enabled key production performance indicators analysis and prediction method are proposed to extract real-time production information and predict future production status accurately. Then, the PMRA method is presented to assign the resources before production exceptions happen. Finally, a case study from a typical manufacturer for computer numerical control machine tools in North China is used to validate the proposed method and results show that the proposed PMRA method can largely reduce the total tardiness and the total energy consumption.

Jin Wang

Papers

A Proactive Manufacturing Resources Assignment Method Based on Production Performance Prediction for the Smart Factory