Digitally-enhanced lubricant evaluation scheme for hot stamping applications

Abstract

Digitally-enhanced technologies are set to transform every aspect of manufacturing. Networks of sensors that compute at the edge (streamlining information flow from devices and providing real-time local data analysis), and emerging Cloud Finite Element Analysis technologies yield data at unprecedented scales, both in terms of volume and precision, providing information on complex processes and systems that had previously been impractical. Cloud Finite Element Analysis technologies enable proactive data collection in a supply chain of, for example the metal forming industry, throughout the life cycle of a product or process, which presents revolutionary opportunities for the development and evaluation of digitally-enhanced lubricants, which requires a coherent research agenda involving the merging of tribological knowledge, manufacturing and data science. In the present study, data obtained from a vast number of experimentally verified finite element simulation results is used for a metal forming process to develop a digitally-enhanced lubricant evaluation approach, by precisely representing the tribological boundary conditions at the workpiece/tooling interface, i.e., complex loading conditions of contact pressures, sliding speeds and temperatures. The presented approach combines the implementation of digital characteristics of the target forming process, data-guided lubricant testing and mechanism-based accurate theoretical modelling, enabling the development of data-centric lubricant limit diagrams and intuitive and quantitative evaluation of the lubricant performance.

Industry 4.0 in Metal Forming Industry Towards Automotive Applications: A Review

“…The global flow of steels [3] and aluminium [4] were mapped based on big data, which provides intuitive and comprehensive image on the entire material supply chain cycle from the raw materials to end-use goods. Big data was collected from the manufacturing processes enabling the development of Digital Characteristics (DCs) to map the manufacturing processes from the perspective of metadata [89], where our understanding remains incomplete. The DCs incorporate essential information spanning the design, manufacturing and application stages of the manufactured products [89].…”

“…Big data was collected from the manufacturing processes enabling the development of Digital Characteristics (DCs) to map the manufacturing processes from the perspective of metadata [89], where our understanding remains incomplete. The DCs incorporate essential information spanning the design, manufacturing and application stages of the manufactured products [89]. Figure 5 shows the DCs developed based on the datasets from the tube hydroforming and hot stamping process by characterising the thermo-mechanical evolutions during forming, including the normalised major and minor strains, and normalised major and minor stresses.…”

“…The post-FEA modules contains the 'Tailor' module for enabling the prediction of post-form strength, the 'Tool-maker' module for enabling the prediction of cold die quenching efficiency, and the 'Formability' module for enabling the prediction of formability. The effectiveness of these functional modules was successfully validated in peer-reviewed studies towards automotive applications [74,75,[94][95][96][97][98][99]. The cloud FEA can be further characterised into 3 layers, namely the resource layer, cloud platform layer and application layer.…”

Intelligent Online Sensing of Defects Evolution in Metallic Materials during Plastic Deformation

“…Developing an intelligent sensing system capable of real-time online monitoring of defect evolution during plastic deformation is of paramount importance for quality control and the realization of intelligent forming. [1][2][3][4] Currently, online sensing of metal forming primarily relies on ultrasonic, eddy current and acoustic emission (AE) detection technology. Ultrasonic detection offers advantages such as the ability to inspect large-sized components, high sensitivity, speed, and cost-effectiveness, enabling both quantitative and qualitative defect analysis.…”

“…Developing an intelligent sensing system capable of real‐time online monitoring of defect evolution during plastic deformation is of paramount importance for quality control and the realization of intelligent forming. [ 1–4 ]…”