Lifetime Assessment and Optimization of a Welded A-Type Frame in a Mining Truck Considering Uncertainties of Material Properties and Structural Geometry and Load

Abstract

In order to improve the fatigue performance of a welded A-type frame in a heavy off-road mining truck, a novel method was presented to implement lifetime and weight collaborative optimization while considering uncertainties in geometry dimension, material properties, and bearing load. The mechanical and cyclic material parameters were obtained from experimental work to characterize the base metal and the weldment. The finite element model of a welded A-type frame was constructed to analyze stress distribution and predict fatigue life, the force time histories of which were acquired from multi-body dynamics simulation. The simulated failure position and fatigue life had a good agreement with the actual results. Then, both structural lifetime and weight were considered as optimization objectives. The thickness of main steel plates and elastic and cyclic material parameters were chosen as uncertain design variables as well as main loads at connection locations. The fifty sample points in the light of Latin hypercube sampling method and its responses calculated by finite element analysis were supposed to build the approximation model based on the Kriging approximation method. After its fitting precision was guaranteed, the non-dominated sorting genetic algorithm II (NSGA-II) was utilized to find the optimal solution. Finally, the fatigue life of a welded A-type frame was increased to 2.40×105 cycles and its mass was lessened by 8.2%. The optimized results implied that good fatigue performance of this welded A-type frame needs better welding quality, lower running speed for downhill and turning road surface, and thicker front plates.

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

“…A welded A-type chassis's FL can be enhanced, and its mass can be reduced. By utilizing the fuzzy fatigue reliability optimization based on evolutionary algorithms, its reliability ranged from 69.47% to 95.12% [108]. Also, it was inferred that a better welding process can help improve the fatigue performance of welded A-type frame in a heavy off-road mining truck [112].…”

“…Using the 3D-extended finite element toolkit for Abaqus (XFA3D) and machine learning methods in a digital twin setting, Ren et al [107] predicted a structure's FL and discovered that the adoption of a digital twin strategy for the complete life management of aging structures requires the incorporation of as-manufactured attributes and uncertainties. Mi et al [108], while examining the lifecycle of a welded A-type frame in a mining vehicle, considered the material attribute unpredictabilities along with load and structural geometry through experimental data, FEA, and NSGA-II.…”

“…Bridges [90,92,93], energy [94]. Steel [96,97,102,106,108,109,112,114,115,118,120,121,123,126,[129][130][131][132], aluminum [107,110], titanium [108], stainless steel [114,119], CFRP + steel [121],…”

A New Strategy for Railway Bogie Frame Designing Combining Structural–Topological Optimization and Sensitivity Analysis

“…Similar approaches, applied on different types of bogie frame, have been proposed by other researchers [21][22][23]. Lifetime assessment and optimization have been applied on a welded a-type frame for a mining truck [24]. Experimental fatigue evaluation has been carried out on a metro bogie frame [25,26].…”