Developments in Mechanical Engineering of Immediate Relevance to Industrial Engineering

Advances in Mechanical Engineering
0.848 Impact Factor
Table of Contents
Volume 11 Issue 3, March 2019
https://journals.sagepub.com/toc/ade/current



Multi-objective optimization design of spur gear based on NSGA-II and decision making
Qizhi YaoFirst Published March 13, 2019 Research Article 
Advances in Mechanical Engineering
Volume 11 Issue 3, March 2019
https://doi.org/10.1177/1687814018824936
https://journals.sagepub.com/doi/full/10.1177/1687814018824936

Open access article:
Due to most of power transmission systems requiring light weight, efficient, and low-cost elements, Tamboli et al. (11) optimized a heavy-duty gear reducer with helical gear pair based on the minimum volume. Rao (12) used teaching learning based optimization (TLBO) and elitist teaching learning based optimization (ETLBO) algorithms to optimize a spur gear train for weight reduction under the contains of bending strength, surface durability, torsional strength, and center distance.

11. Tamboli, K, Patel, S, George, PM. Optimal design of a heavy duty helical gear pair using particle swarm optimization technique. Proc Tech 2014; 14: 513–519.

12. Rao, RV . Design optimization of a spur gear train using TLBO and ETLBO algorithms. In: Rao, RV (ed.) Teaching learning based optimization algorithm: and its engineering applications. Cham: Springer, 2016, pp.91–101.

Wei and Lin14 performed a multi-objective optimization design for a helical gear using finite element method and Taguchi method.

14. Wei, F, Lin, H. Multi-objective optimization of process parameters for the helical gear precision forging by using Taguchi method. J Mech Sci Technol 2011; 25: 1519–1526.

Huang et al.18 worked on the optimization of three-stage spur gear reduction units in order to minimize volume and maximize surface fatigue life.

Huang, HZ, Tian, ZG, Zuo, MJ. Multiobjective optimization of three-stage spur gear reduction units using interactive physical programming. J Mech Sci Technol 2005; 19: 1080–1086.


The effects of robot welding and manual welding on the low- and high-cycle fatigue lives of SM50A carbon steel weld zones
Changwan Han, Changhwan Yang, Hanjong Kim, ...
First Published March 13, 2019 Research Article
Advances in Mechanical Engineering
Volume 11 Issue 3, March 2019
https://doi.org/10.1177/1687814019828266
https://journals.sagepub.com/doi/full/10.1177/1687814019828266

Open access

The major advantage of RW is the productivity enhancement of uniform quality products, because robots can always guarantee the same operating conditions for welding.

Conclusion
The  robot welding (RW)  and manual welding (MW) effects on the fatigue of SM50A carbon steel weld zones were analyzed in this study. The RW weld zone showed better fatigue life at 800 MPa, but slightly poorer fatigue life than the MW at 227 MPa. However, no significant difference in the overall S-N curves between the MW and RW except these two stress levels may suggest that the RW method is more desirable due to its advantage in maintaining consistency of welding process parameters than the MW. Further systematic studies to derive the correlations between welding parameters (welding currents, voltages, and speeds) and weld zone microstructures, as well as their effects on the fatigue strength of weld zones, can contribute to the design of the optimized welding process parameters in the RW method.