In this study, the total algorithm of the strength-based design of the system for mass production has been developed. The proposed algorithm, which includes numerical, analytical, and experimental studies, was implemented through a case study on the strength-based structural design and fatigue analysis of a tractor-mounted sunflower stalk cutting machine (SSCM). The proposed algorithm consists of a systematic engineering approach, material selection and testing, design of the mass criteria suitability, structural stress analysis, computer-aided engineering (CAE), prototype production, experimental validation studies, fatigue calculation based on an FE model and experimental studies (CAE-based fatigue analysis), and an optimization process aimed at minimum weight. Approximately 85% of the system was designed using standard commercially available cross-section beams and elements using the proposed algorithm. The prototype was produced, and an HBM data acquisition system was used to collect the strain gage output. The prototype produced was successful in terms of functionality. Two- and three-dimensional mixed models were used in the structural analysis solution. The structural stress analysis and experimental results with a strain gage were 94.48% compatible in this study. It was determined using nCode DesignLife software that fatigue damage did not occur in the system using the finite element analysis (FEA) and experimental data. The SSCM design adopted a multi-objective genetic algorithm (MOGA) methodology for optimization with ANSYS. With the optimization solved from 422 iterations, a maximum stress value of 57.65 MPa was determined, and a 97.72 kg material was saved compared to the prototype. This study provides a useful methodology for experimental and advanced CAE techniques, especially for further study on complex stress, strain, and fatigue analysis of new systematic designs desired to have an optimum weight to strength ratio.
Geometric Optimization of ‘U’-Drill Cutting Edge by Finite Element Analysis (FEA) and its Experimental Validation
