Research Article
Optimization of the Parameters of a Helical Rotary Harrow Based on the Method of Mathematical Planning of Experiments
Muhamedov Djobirxon,
Abduvaxobov Dilshod,
Ismatullayev Kaxramon,
Muxammadjonov Komiljon*,
To‘xtasinov Rustambek
Issue:
Volume 14, Issue 2, April 2026
Pages:
28-35
Received:
14 May 2026
Accepted:
25 May 2026
Published:
26 June 2026
DOI:
10.11648/j.ijmea.20261402.11
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Abstract: This article investigates the optimization of the constructive and technological parameters of a helical rotary harrow using the method of mathematical planning of multifactor experiments. The study aims to improve the quality of soil cultivation while reducing energy consumption and increasing the operational efficiency of the unit. In the research, the influence of the rotational speed of the helical toothed section, tooth diameter, tooth length, helix rise angle, and aggregate travel speed on the quality and energy performance indicators of the rotary harrow was analyzed. The experiments were carried out based on the Plan B5 experimental design method. The lifting height of the bottom soil layer, soil crumbling degree, power consumption of the rotary harrow, and specific draft resistance were selected as evaluation criteria. Experimental data were processed using the PLANEX software package. The homogeneity of variance was evaluated using Cochran’s criterion, the significance of regression coefficients was determined by Student’s criterion, and the adequacy of the developed models was verified using Fisher’s criterion. As a result of the study, regression equations describing the relationships between the input factors and evaluation criteria were obtained, and the optimal constructive and technological parameters of the helical rotary harrow were determined. The obtained results demonstrate that the proposed parameter optimization improves soil cultivation quality, decreases energy consumption, and enhances the operational efficiency of the machine unit.
Abstract: This article investigates the optimization of the constructive and technological parameters of a helical rotary harrow using the method of mathematical planning of multifactor experiments. The study aims to improve the quality of soil cultivation while reducing energy consumption and increasing the operational efficiency of the unit. In the researc...
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Research Article
Artificial Intelligence in Mechanical Engineering Education: Analysis and Implications
Michael Reynolds*
Issue:
Volume 14, Issue 2, April 2026
Pages:
36-42
Received:
3 June 2026
Accepted:
13 June 2026
Published:
30 June 2026
DOI:
10.11648/j.ijmea.20261402.12
Downloads:
Views:
Abstract: Artificial Intelligence (AI) is currently transforming education and the student experience. Students are frequently using AI as an assistive aid in completing homework and writing assignments. Within engineering, students are using AI both as an analysis tool and to generate design concepts. This paper demonstrates how well three common AI algorithms solve both fundamental and advanced engineering problems. The selected engineering problems represent a range of topics commonly encountered in undergraduate curricula. The results show that AI can solve most fundamental engineering problems, but it has significant limitations with professional engineering work. This work demonstrates several cases where AI can generate incorrect solutions and explains the rationale behind such errors. At the time of this paper, AI still has significant limitations that make complete reliance on generated solutions both unwise and unethical. The value added of an engineer comes from areas where AI tools are too often incorrect or incomplete, such as modeling real physical systems, designing and interpreting experiments, and designing and building physical systems to achieve multiple objectives. This paper urges engineering educators to be knowledgeable about AI and encourage students to use it in assistive means and not as a replacement for fundamental knowledge. Future work will continue to refine AI’s role in education as well as test its ability to solve advanced problems.
Abstract: Artificial Intelligence (AI) is currently transforming education and the student experience. Students are frequently using AI as an assistive aid in completing homework and writing assignments. Within engineering, students are using AI both as an analysis tool and to generate design concepts. This paper demonstrates how well three common AI algorit...
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