User:Mike Aghili

Mechanical Engineer | Advanced Manufacturing Researcher | Product Development and Smart Systems Lead

I am a mechanical engineer and advanced manufacturing researcher working at the intersection of additive manufacturing, smart hardware, data-driven process optimization, and personalized product development. I earned my Ph.D. in Mechanical Engineering from Concordia University in 2026. My doctoral work investigated cost-scalable mass personalization through hybrid 3D printing and electroforming, with an emphasis on manufacturing complex, high-precision metal components at lower cost.

My industrial work spans research-grade monitoring systems, personalized medical devices, product development, and manufacturing process improvement. I currently lead mechanical and manufacturing development at Neurocage Systems, where I work with multidisciplinary engineering and research teams to develop AI-enabled smart monitoring platforms for preclinical neuroscience. My responsibilities cover mechanical architecture, sensor and electronics integration, rapid prototyping, CFD analysis, design for manufacturing and assembly, supplier coordination, and production scaling.

I am interested in collaborations involving advanced manufacturing, additive manufacturing, mass personalization, smart and connected hardware, medical devices, and AI/ML-enabled manufacturing systems.

Contact: mohamad.aghili@concordia.ca Google Scholar: Mike (SMA) Aghili LinkedIn: Mike Aghili

• Advanced manufacturing: additive manufacturing, hybrid 3D printing-electroforming, microfabrication, CNC machining, laser cutting, electroplating, and rapid prototyping
• Mechanical product development: concept development, CAD, engineering validation, enclosure and mechanism design, prototype-to-production development, and product lifecycle support
• Smart systems: sensors, data-acquisition hardware, embedded electronics integration, IoT systems, Raspberry Pi, Arduino, and real-time monitoring platforms
• Manufacturing engineering: design for manufacturing and assembly (DFM/DFA), process optimization, quality improvement, root-cause analysis, supplier coordination, and production scaling
• Simulation and data: CFD airflow and ventilation analysis, machine learning, predictive modeling, data analytics, dashboards, and manufacturing process monitoring
• Software and tools: SolidWorks, Fusion 360, CATIA, AutoCAD, 3DEXPERIENCE, Python, MATLAB, C/C++, VBA, Power BI, TensorFlow, Keras, Scikit-learn, and OpenCV
• Technical leadership: multidisciplinary team leadership, mentoring, project planning, technical documentation, requirements traceability, and cross-functional coordination

Montreal, Canada | November 2025-present

• Lead a multidisciplinary team of five engineers and research scientists developing AI-enabled smart monitoring systems for real-time animal behavioral research.
• Directed four generations of smart home-cage systems and new modules, including automated food and water delivery hardware, from concept through production.
• Designed and validated mechanical systems in SolidWorks and Fusion 360 while integrating sensors, data-acquisition equipment, and embedded electronics.
• Coordinated CNC machining, additive manufacturing, laser cutting, and overseas manufacturing to improve quality, cost, manufacturability, and scalability.
• Performed CFD airflow and ventilation studies for cages, rack systems, and environmental enclosures.
• Redesigned assemblies to reduce assembly time by approximately 10% and contributed to three patent applications.

McGill University-affiliated research centre | Montreal, Canada | November 2025-present

• Develop integrated smart-cage systems for real-time behavioral monitoring in preclinical neuroscience research.
• Design mechanical hardware, sensor integration, and data-acquisition architecture for automated research platforms.
• Support interdisciplinary work combining mechanical engineering, neuroscience, and AI-driven behavioral analysis.

Montreal, Canada | January 2018-March 2026

• Developed hybrid additive manufacturing and electroforming methods for personalized, high-precision metal components.
• Created predictive models for electroforming processes using manufacturing data and machine-learning methods.
• Integrated temperature, pH, and conductivity sensing with networked Raspberry Pi, Arduino, and Linux-based monitoring systems.
• Developed experimental datasets and visualization tools for electrolyte condition monitoring, process characterization, and manufacturing optimization.
• Designed and fabricated research components using CAD, fused-filament fabrication, electroforming, and surface characterization methods.
• Published and presented research on hybrid manufacturing, personalized metal components, microfabrication, and medical-device design.

Montreal, Canada | September 2021-March 2023

• Helped lead the design and additive manufacture of personalized medical devices.
• Supported product development under ISO 13485 and FDA 21 CFR Part 820 design-control requirements.
• Conducted biocompatible polymer selection, risk analysis (FMEA), and verification and validation activities.
• Managed system architecture and requirements traceability across multidisciplinary teams.
• Applied Agile and Lean methods, contributing to a reported 55% reduction in design iteration time.

Montreal, Canada | June-August 2022

• Developed Excel/VBA and Power BI tools for product-data tracking and operational reporting.
• Supported production troubleshooting, process documentation, and interdepartmental engineering coordination.
• Contributed to tracking and visualization improvements associated with a reported 30% increase in operational efficiency.

My doctoral research combined fused-filament fabrication with electroforming to manufacture high-precision personalized metal components. The work explored additively manufactured molds and mandrels, design strategies for free-form and multi-level geometries, manufacturing cost and scalability, and the relationship between electroforming parameters and final part quality.

I developed process-monitoring and predictive-modeling approaches using electrolyte properties, sensor measurements, and electroforming parameters. This work connects physical manufacturing experiments with data analytics, machine learning, IoT sensing, and real-time decision support.

My current work applies mechanical product development, sensing, embedded electronics, data acquisition, and environmental simulation to automated monitoring systems for neuroscience research. The goal is to move reliable research hardware from early prototypes to repeatable laboratory deployment and scalable production.

• Personalized free-form metal components: Developed design and manufacturing approaches that use 3D-printed molds and electroforming to produce multi-level and free-form metal geometries.
• High-precision personalized flexures: Investigated a low-cost hybrid 3D printing-electroforming method for customized flexure mechanisms.
• Smart electroforming monitoring: Integrated physical sensors, IoT hardware, process data, and predictive models for real-time manufacturing analysis.
• Patient-specific medical devices: Contributed to the design, simulation, material selection, and additive manufacture of personalized devices.
• Smart home-cage platforms: Led mechanical development of modular monitoring systems and automated food and water delivery modules for preclinical research.
• COVID-19 manufacturing response: Produced face shields for Centre hospitalier de l'Universite de Montreal (CHUM) and helped coordinate volunteer 3D-printing capacity for frontline staff.(The Gina Cody School of Engineering and Computer Science joins the fight against COVID-19)

• Aghili, Sayedmohammadali (2026). Enabling Cost-Scalable Mass Personalization through Hybrid Additive Manufacturing-Electroforming and Data-Driven Process Modeling. Ph.D. thesis, Department of Mechanical, Industrial and Aerospace Engineering, Concordia University. Supervisor: Prof. Rolf Wuthrich.

• Hamed, H.; Aghili, S.; Wuthrich, R.; Abou-Ziki, J.D. (2024). Electroforming of Personalized Multi-Level and Free-Form Metal Parts Utilizing Fused Deposition Modeling-Manufactured Molds. Micromachines, 15(6), 734.
• Aghili, S.; Zheng, Z.; Wuthrich, R. (2023). Low-cost manufacturing of high-precision personalized flexures by a hybrid 3D printing-electroforming technique. The International Journal of Advanced Manufacturing Technology, 128, 2333-2346.
• Zheng, Z.; Aghili, S.M.; Wuthrich, R. (2022). Towards electroforming of copper net-shape parts on fused deposition modeling (FDM) printed mandrels. The International Journal of Advanced Manufacturing Technology, 122, 1055-1067.
• Aghili, S. et al. (2023). Toward Patient-Specific Pessary to Manage Pelvic Organ Prolapse: Design and Simulation. 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
• Aghili, S. et al. (2020). Fabrication of Low-Cost Molds for Manufacturing of High-Precision Micro Parts by Electroforming. Electrochemical Society international meeting.
• Aghili, S. et al. (2020). Electroforming on Additively Manufactured Mandrels. Electrochemical Society international meeting.

For an updated publication list, see my Google Scholar profile.

• Ph.D., Mechanical Engineering, Concordia University, Montreal, Canada, 2026
  • GPA: 3.94/4.3
  • Research areas: additive manufacturing, hybrid manufacturing, mass personalization, machine learning, Industry 4.0, IoT, microfabrication, electroforming, and manufacturing data analytics
  • Thesis: Enabling Cost-Scalable Mass Personalization through Hybrid Manufacturing
• B.Eng., Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran, 2016

From 2018 to 2025, I worked as a teaching assistant, laboratory demonstrator, and marker at Concordia University. I supported engineering students in numerical methods, differential equations, 2D/3D CAD, and data-science applications. Across these roles, I contributed to the instruction and mentoring of more than 5,000 students.

• Second place, ASM International - Montreal Chapter event (2022)
• Concordia University Tuition Award of Excellence
• Concordia Merit Scholarship
• Annual doctoral research support at Concordia University
• Featured by Concordia University for volunteer 3D-printing work supporting CHUM during the COVID-19 response Link

I welcome research and industrial collaboration in:

• additive and hybrid manufacturing
• personalized and low-volume production
• data-driven manufacturing and process optimization
• smart sensing and connected hardware
• mechanical systems for biomedical and neuroscience research
• open manufacturing data and reproducible prototyping

• Google Scholar
• Ph.D. thesis - Concordia University Spectrum
• Mike Aghili