Multi-objective optimization design of Titanium alloy frame for electric commercial vehicles based on MOGA algorithm

With advancements in oil and gas fracturing technology, the performance of sand-mixing trucks critically influences construction efficiency and operational quality. Traditional diesel-powered sand-mixing trucks face challenges such as high emissions, elevated noise levels, significant fuel consumption, and frequent maintenance requirements, limiting their deployment in environmentally sensitive areas. Consequently, research on electric sand-mixing trucks is essential for advancing green operations and digitalized oilfield development. This study investigates a titanium alloy frame for an electric sand-mixing truck. A finite element model was established to analyze its mechanical performance under four typical operating conditions, exploring the application potential of titanium alloy in the frame structure. Modal analysis was performed to extract the first six natural frequencies and mode shapes, followed by harmonic response analysis using the modal superposition method. The maximum stress, deformation, frame mass, and first-order natural frequency under torsional loading were selected as objective functions. Key dimensional variables were identified through sensitivity analysis, a response surface model was constructed, and optimization was conducted using the Multi-Objective Genetic Algorithm (MOGA).Results indicate that under full-load bending, emergency braking, and steering conditions, the overall frame stress remains below 100 MPa, with localized stress concentrations ranging from 120 to 140 MPa. The torsional condition exhibits a peak stress of 599.94 MPa, identified as the primary cause of local crack initiation. Post-optimization, the maximum equivalent stress decreased by 60.81%, maximum displacement reduced by 1.82%, mass decreased by 3.5%, and the first-order natural frequency increased by 93.6%. These improvements significantly enhance structural strength and stiffness while avoiding resonance risks within the road excitation frequency range. The study offers guidance for the design and application of electric sand-mixing truck frames.