Chile produces nearly 30% of the world’s copper. But by 2025, declining ore grades and water scarcity will make traditional extraction unprofitable. Abaqus.cl’s primary objective is to deploy predictive digital twins for every major tailings dam in the Antofagasta region. By coupling Abaqus FEA with real-time IoT sensors, their goal is to predict liquefaction and structural fatigue 72 hours before a failure occurs. The metric for success by Q4 2025: Zero unplanned downtime for their five largest mining clients.
Abaqus continues to prioritize its role as an independent advisor in Santiago, Chile. Key objectives for 2025 include:
As the world shifts to electric vehicles, the "Lithium Triangle" (Chile, Argentina, Bolivia) becomes strategic. Abaqus.cl’s most ambitious 2025 objective is to open a specialized Electro-Mechanical Simulation Lab in Antofagasta. Here, their engineers will not just run analyses for battery manufacturers; they will co-develop patentable cooling systems for lithium-ion cells subjected to desert heat. By December 2025, the objective is to have filed three joint patents with local mining tech startups, moving from a service provider to an R&D partner.
As we approach 2025, abaqus.cl stands at a pivotal juncture. The engineering simulation landscape in Latin America is shifting from traditional on-premise modeling to integrated, cloud-based digital ecosystems. Our 2025 objectives are designed to transition the company from a premier software provider to a holistic engineering solutions partner. By leveraging the power of the Abaqus Unified FEA suite, we aim to empower Chilean industries—mining, aerospace, and construction—to achieve unprecedented efficiency and sustainability.
Based on publicly available information and industry reports, Abaqus CL's objectives for 2025 can be summarized as follows:
Implementing advanced data analytics to better align client portfolios with evolving global economic shifts. 3. Expansion of Educational and Support Resources
Enhance the efficiency of risk analysis and Monte Carlo sampling by leveraging near-term NISQ (Noisy Intermediate-Scale Quantum) technologies.