Predicting the Structural Strength of Heavy-Duty Lifting Hooks with PhysicsAI

Background

       Heavy-duty hooks (Figure 1) bear high loads during lifting operations, and their structural strength is directly related to jobsite safety. In traditional finite element analysis, the process is time-consuming, and frequent design changes further prolong development time.
       To solve these issues, Zhen Feng Enterprise adopted Altair PhysicsAI, leveraging historical design data and turning it into a predictive tool to accelerate analysis of new designs, thereby improving efficiency and accuracy.
 
 

Fig. 1 Heavy-duty hook (Source: Zhen Feng Enterprise Co., Ltd.)

Results

Accurate Prediction

       The PhysicsAI model can accurately predict the structural strength of the hook, with very small deviation from traditional CAE analysis results — the error is within approximately 3%. (See Figure 2)

 

Fig. 2 CAE validation vs. PhysicsAI prediction (Source: Zhen Feng Enterprise Co., Ltd.)

Improved Efficiency

       For structural strength analysis of new hook designs, traditional CAE calculations required several hours, whereas PhysicsAI predictions take only a few seconds, dramatically shortening the design cycle compared with conventional analysis.

 

Technical Highlights

Parametric Design

       Using HyperMesh to perform parametric design on the hook enables rapid generation of multiple design variants. 
 

Automated Analysis

       Design of Experiments (DOE) analysis is performed in HyperStudy.
 

PhysicsAI Training and Application

       Historical design and analysis data are used to train the AI model, enabling fast and accurate prediction of structural strength.

 

Conclusion

       By leveraging Altair PhysicsAI technology, Zhen Feng Enterprise transformed design data into a predictive tool, significantly improving design efficiency and reducing modification time. The AI model enabled a highly efficient integration of design and virtual validation, demonstrating the potential of AI in engineering design and helping the company build an innovative, data-driven design process.

 

Source: 2024 RTC Technical Highlights