Doctor of Philosophy (PhD) in Mechanical Engineering

Shandong University of Technology, Shandong, China

Thesis Details

My Ph.D. thesis focused on developing AI-based models and multi-objective optimization techniques for enhancing the performance and quality of sapphire nanosecond laser machining. My work involved exploring the interaction between laser parameters and material properties to optimize machining. I utilized artificial intelligence algorithms to model complex manufacturing processes. I developed predictive models that can simulate and optimize laser machining parameters by leveraging techniques such as artificial neural networks and support vector regression. This approach helps achieve desired outcomes like enhanced precision, improved surface finish, and reduced processing times. Moreover, my research included applying multi-objective optimization methods to balance various conflicting objectives in laser machining. Another study was dedicated to the functionalization of material surfaces using laser techniques. This includes creating micro and nano-scale textures to modify surface properties such as wettability, friction, and optical characteristics. The goal is to develop surfaces with tailored functionalities for specific industrial applications, such as enhancing the performance of optical devices and improving tribological properties.

Thesis Topic

Research in nanosecond laser machining of sapphire with the aid of modeling and optimization methods

Awarded Outstanding Ph.D. Student in the School of Mechanical Engineering

Awarded Full Fund China Scholarship Council (CSC)

Sep. 2019 - Jun. 2024

Supervisor

Prof. Hongyu Zheng

Research Outputs

• A.N. Bakhtiyari, K. Wang, Z. Wang, L. Wang, Y. Wu, H. Zheng, (2025) “Hybrid Intelligent Algorithm for Modeling and Optimization of Laser Microgrooving of Sapphire”, The 9th International Conference on Manufacturing Technologies (ICMT 2025), 13-17 Jan 2025, Atlanta, USA. (Accepted for presentation in ICMT 2025 and publication in Lecture Notes in Mechanical Engineering.

• A.N. Bakhtiyari, H. Zheng, (2024) “A Hybrid Approach to Simulating and Optimizing Laser-Induced Plasma-Assisted Ablation (LIPAA) of Sapphire Using Machine Learning and Numerical Modeling Methods”, International Symposium on Advanced Intelligent Manufacturing Technology, 1-4 Dec., Zibo, Shandong, China.

• A. N. Bakhtiyari, M. Omidi, A. Yadav, Y. Wu, H. Zheng, (2024), “AI-based modeling and multi-objective optimization of ultraviolet nanosecond laser-machined sapphire”, Applied Physics A, Volume 130,101. DOI: 10.1007/s00339-023-07259-9 (SJR Q2)

• A. N. Bakhtiyari, A. Yadav, Y. Wu, H. Zheng, (2023), “Enhancing wettability of sapphire (Al2O3) substrate through laser surface texturing”, Materials Letters, Volume 355, 135506. DOI: 10.1016/j.matlet.2023.135506 (SJR Q2)

• A. N. Bakhtiyari, K. Wang, C. Wang, Y. Wu, H. Zheng, (2023), “Effect of using reflective target materials on enhancing UV nanosecond laser machining of sapphire”, Optik, Volume 290, 171333. DOI: 10.1016/j.ijleo.2023.171333 (SJR Q2)

• A. N. Bakhtiyari, Y. Wu, L. Wang, Z. Wang, H.Y. Zheng, (2023), “Laser machining sapphire via Si-sapphire interface absorption and process optimization using an integrated approach of the Taguchi method with grey relational analysis”, Journal of Materials Research and Technology, Volume 24, Pages 663-674. DOI: 10.1016/j.jmrt.2023.02.218 (SJR Q1)

• A. N. Bakhtiyari, Y. Wu, D. Qi, H.Y. Zheng, (2023), “Modeling temporal and spatial evolutions of laser-induced plasma characteristics by using machine learning algorithms”, Optik, Volume 272, 170297. DOI: 10.1016/j.ijleo.2022.170297 (SJR Q2)

• A. N. Bakhtiyari, Z. Wang, H.Y. Zheng, (2021), “Feasibility of artificial neural network on modeling laser-induced colors on stainless steel”, Journal of Manufacturing Processes, Volume 65, 471-477. DOI: 10.1016/j.jmapro.2021.03.044 (SJR Q1)

• A. N. Bakhtiyari, Z. Wang, L. Wang, H.Y. Zheng, (2021), “A review on applications of artificial intelligence in modeling and optimization of laser beam machining”, Optics & Laser Technology, Volume 135, 106721. DOI: 10.1016/j.optlastec.2020.106721 (SJR Q1)

• A. N. Bakhtiyari, Z. Wang, H.Y. Zheng, (2021), “Application of artificial neural network in improving color consistency and repeatability of laser color marking”, The 8th International Conference on Surface Engineering (8th ICSE 2021), 3-5 Dec 2021, Weihai, China.

Master of Science (MSc) in Mechanical Engineering – Manufacturing and Production

Semnan University, Semnan, Iran

Thesis Details

My M.Sc. thesis focused on investigating the mechanical properties and microstructure of aluminum alloys (Al5083 and Al6061) processed through equal channel angular rolling (ECAR). The research aimed to understand the relationship between microstructural changes and mechanical properties induced by ECAR. By employing artificial neural networks (ANN) and nonlinear regression, I developed predictive models to estimate the mechanical properties of the processed alloys. This approach allowed for the accurate prediction of properties such as tensile strength, hardness, and ductility based on microstructural features. The study provided insights into optimizing the ECAR process parameters to enhance the mechanical performance of aluminum alloys, contributing to their potential applications in various industries.

Thesis Topic

Investigation of mechanical properties using artificial neural network and micro-structure of equal channel angular rolled Al5083 and Al6061 samples.

Sep. 2013 - Jun. 2016

Supervisor

Prof. Masoud Mahmoodi

Research Outputs

• M. Mahmoodi, A. N. Bakhtiyari, (2019), “Microstructure and its relationship to mechanical properties in equal channel angular rolled Al6061 alloy sheets”, Iranian Journal of Materials Forming, Volume 6, Issue 1, Pages 16-23. DOI: 10.22099/ijmf.2019.31820.1118 (ISC Journal)

• M. Mahmoodi, A. N. Bakhtiyari, G. Dini, (2017), “Correlation between microstructure and mechanical properties of Al5083 alloy sheets processed by equal channel angular rolling”, Journal of Materials Engineering and Performance, Volume 26, Pages 6022-6027. DOI: 10.1007/s11665-017-3021-z (SJR Q2)

• M. Mahmoodi, A. N. Bakhtiyari, (2016), “Applicability of artificial neural network and nonlinear regression to predict mechanical properties of equal channel angular rolled Al5083 sheets”, Latin American Journal of Solids and Structures, Volume 13, Pages 1515-1525. DOI: 10.1590/1679-78252154 (SJR Q2)

• M. Mahmoodi, A. N. Bakhtiyari, (2015), “Prediction of mechanical properties of equal channel angular rolled Al6061 alloy sheet using artificial neural networks and nonlinear regression”, Journal of Modeling in Engineering, Volume 15, Pages 197-207. DOI: 10.22075/jme.2017.2690 (ISC Journal)

• A. Asadi, A. N. Bakhtiyari, I. M. Alarifi, (2021), “Predictability evaluation of support vector regression methods for thermophysical properties, heat transfer performance, and pumping power estimation of MWCNT/ZnO-engine oil hybrid nanofluid”, Engineering with computers, Volume 37. Pages 3813-3823. DOI: 10.1007/s00366-020-01038-3 (SJR Q1)

• 1- I. M. Alarifi, H. M. Nguyen, A. N. Bakhtiyari, A. Asadi, (2019), “Feasibility of ANFIS-PSO and ANFIS-GA models in predicting thermophysical properties of Al2O3-MWCNT/Oil hybrid nanofluid”, Materials, Volume 12, Issue 21. DOI: 10.3390/ma12213628 (SJR Q2)

Bachelor of Science (BSc) in Mechanical Engineering – Manufacturing and Production

IAU, Najafabad Branch, Isfahan, Iran

Thesis Details

My B.Sc. thesis involved the design and manufacture of an ultrasonic digitizer, a device used for converting analog ultrasonic signals into digital form for various applications such as medical imaging, non-destructive testing, and industrial automation. The project required a comprehensive understanding of ultrasonic transducers, signal processing, and digital electronics. I focused on optimizing the design to achieve high accuracy and reliability while minimizing noise and distortion. The successful development of the ultrasonic digitizer demonstrated my ability to integrate theoretical knowledge with practical engineering skills, paving the way for advanced research and innovation in ultrasonic technology.

Thesis Topic

Designing and manufacturing ultrasonic digitizer

Awarded outstanding B.Sc. Student in the School of Mechanical Engineering

Sep. 2008 - Sep. 2012

Supervisor

Dr. Mohammad Amini