Erin Chen, Engineer at R&D Division, Moldex3D
Most enterprises are pursuing the integration of cloud platforms, big data, and artificial intelligence (AI). In the traditional enterprise structure, different teams, divisions, and individuals may create different databases, but the same data could have different revisions when it is put in different places, leading to inconsistency of a company’s internal data. Moreover, since the inconsistent data is difficult to be shared, problems can occur when maintaining a continuous product development experience and resolving known development issues. The divergent data can be a big obstacle in the path toward AI. Thus, it is a critical issue to store and manage big data with nicely organized and visualized effective information.
Moldex3D iSLM is a cloud-based platform developed for data management and application. Before developing a new mold, users can create a mold project through iSLM Solution Management, and record all of the data from Design for Manufacturing (DFM) (Fig. 1), Computing Aided Engineering (Fig. 2, 3), and Mold Tryout. As such, after the mold tryout, iSLM will deliver the tryout data back to the Solution to compare with the CAE data (Fig. 4).
Fig. 1 Users can set different DFM (Design for Manufacturing) records in iSLM for specific issues
Fig. 2 Project data analysis enables users to compare the detailed data of mesh, material, and process to observe the differences among different parameter settings.
Fig. 3 Check the CAE analysis results in the 3D Viewer
Fig. 4 The comparison between the CAE and onsite Mold Tryout data
The Knowledge Management (KM) is the searching system in iSLM for users to quickly filter the historical mold designs through the key classifications (Industry, Product and Part), mold materials, mold thickness, and mold volume (Fig. 5). Users can then refer to a target mold, and obtain its information of CAE analysis and mold tryout with the KM comparison functions. The developers can refer to past experience during product development so that the time cost can be reduced. Furthermore, CAE analysts can attain the mold tryout setting experience through the comparison for more consistent CAE parameter settings with the real world.
Fig. 5 Filter out the desired mold designs through the classifications and materials
Fig. 6 The mold design comparison
Through the iSLM cloud platform, enterprises can more efficiently manage all of the relevant data for the mold development process and integrate the data of DFM, CAE, and mold tryout. As the result, the quickly accumulated data can then assist to achieve cyber-physical integration. Moreover, the visualized data can make a better workflow and thus more efficient teamwork. In the future, the big data accumulated in iSLM can be utilized more effectively through Machine Learning and Deep Learning.