Projects

Description:

As part of MSE1068: Additive Manufacturing of Advanced Engineering Materials at the University of Toronto, I completed both a group design project and an individual research review exploring the versatility of additive manufacturing (AM) in functional and microscale applications.

1. The group project, titled Black Organizer Box (B.O.B), focused on developing a modular, edge-mounted desk organizer for space-limited student workspaces. It emphasized Design for Additive Manufacturing (DfAM) principles, modularity, and sustainability.

2. The individual report examined Two-Photon Polymerization (2PP) as a cutting-edge AM technique capable of sub-100 nm resolution for fabricating biomedical, optical, and microrobotic components. Together, these projects connected the macroscale design flexibility of AM with its microscale precision capabilities, showcasing the breadth of modern manufacturing innovation.

Methodology:

1. Design Project (B.O.B):

   • The design introduced a base box with four standardized edge-rails allowing interchangeable snap-in modules such as a phone stand, cup holder, mini fan, and fidget toy. Each accessory used the same trapezoidal male–female rail interface for universal fit and ease of customization.

   • All components were modeled in SolidWorks and printed using FDM on Prusa printers, with a focus on reducing print time and material waste (total print cost ≈ $10–15).

   • DfAM principles guided the lattice-infill optimization, single-material construction, and repairability-oriented design, minimizing environmental impact and improving recyclability.

2. Research Project (2PP):

   • Conducted a deep-dive review of the 2PP microfabrication process, including voxel-based writing, resin chemistry, and laser–matter interactions. Evaluated 2PP’s use of femtosecond NIR lasers and photoinitiators for nanoscale polymerization.

   • Examined feedstock materials (e.g., PEGDA, Ormocers, CNT/Fe₃O₄ nanocomposites) and critical process parameters such as laser power, scan speed, and hatching distance.

   • Compared 2PP against conventional techniques (UV lithography, E-beam lithography), emphasizing its ability to produce true 3D freeform structures with high fidelity and biocompatibility.

Results:

1. B.O.B Organizer:

   • Reduced cost by >85% compared to commercial organizers (total ≈ $10–15).

   • Enabled same-day customization and replacement of modules.

   • Reduced material waste via thin-shell and lattice-infill strategies, lowering filament use and energy consumption.

   • Demonstrated sustainability through repairable, reconfigurable architecture and single-material recyclability.

2. 2PP Study:

   • Identified resolution capability of <100 nm and Young’s modulus up to 185 MPa for optimized laser parameters.

   • Documented successful fabrication of biocompatible microneedles, scaffolds, and hydrogel actuators with >90% fracture toughness recovery after thermal cycling.

   • Proposed multi-beam parallelization and hybrid manufacturing integration to address throughput and cost limitations.

   • These combined projects demonstrated the scalability of additive manufacturing—from desktop functional systems to nanoscale biomedical devices—bridging practical design and frontier research in digital manufacturing innovation.

Design Report
Report

Description:

As part of MIE540: Product Design at the University of Toronto, our team developed and optimized a motorized dual-flywheel ping pong launcher capable of accurately targeting distances between 10 and 20 feet. Initially conceptualized as a spring-powered design, the system evolved into a fully motorized mechanism after benchmarking and functional trials revealed limitations in repeatability and precision. The final product integrates Arduino-controlled motors, parametric CAD modeling, and design of experiments (DOE) for data-driven optimization. This project demonstrated end-to-end product development—from ideation and prototyping to validation and tolerance analysis—while maintaining focus on manufacturability and cost-effectiveness.

Methodology:

The design process was structured around iterative development and experimental validation:

• Concept Development: Used Pugh and Weighted Decision Matrices to evaluate multiple launcher mechanisms (spring, pneumatic, and flywheel). The dual-flywheel concept was selected for its controllable range, scalability, and simplicity.

• CAD and Prototyping: Designed modular components (base, motor mounts, flywheels, and feeding guide) in SolidWorks and 3D-printed them for rapid prototyping.

• Experimental Design (DOE): Conducted a full factorial 2³ design using wheel gap, contact material, and motor speed as control factors, with three replicates per condition (24 trials total). The experiments aimed to minimize deviation from the bucket center (accuracy metric).

• Data Analysis: Used Minitab to perform factorial ANOVA and main effects analysis. Wheel surface material was identified as the most influential factor, followed by flywheel gap. Optimized configuration: 3.594 cm gap, rubber contact, 5500 RPM.

• Verification & Latitude Studies: Conducted verification runs to validate DOE predictions and tested performance latitude for the flywheel gap (3.3–3.6 cm safe range).

• Tolerance Analysis: Used Root Sum Square (RSS) and Worst-on-Worst (WOW) methods to ensure dimensional robustness of key interfaces (flywheel, shaft, and housing).

Results:

• Achieved average deviation of 72.1 cm from target at 10–20 ft, improving accuracy by >40% over the best DOE baseline.

• Defined optimal operational parameters for repeatable, accurate shots.

• Established safe operating limits for motor RPM (4500–10,000) and wheel gap (3.3–3.6 cm).

• Demonstrated cost-effective manufacturability with a unit production cost of $6.00 and target sale price of $26.36, yielding a projected NPV of $48M over four years.

• Updated FMEA, P-Diagram, and PFD to reflect electromechanical risks (motor heating, misalignment, PWM noise) and their mitigations.

The final launcher is an accurate, modular, and manufacturable system that blends mechanical design, electronics integration, and statistical optimization—showcasing end-to-end engineering design competence.

Report

Description:

As part of MIE519: Advanced Manufacturing Technologies at the University of Toronto, I collaborated with a team to design a self-healing automotive brake disc that extends service life and enhances safety using advanced material engineering. Conventional cast-iron discs often fail due to thermal fatigue and wear, leading to cracks and reduced braking performance. Our objective was to develop a hybrid metal–polymer system that autonomously heals microcracks generated under braking cycles, reducing maintenance needs and improving long-term reliability.

Methodology:

We proposed a two-step hybrid manufacturing approach integrating Powder Metallurgy (PM) and Polymer Infiltration (PI):

1. Powder Metallurgy (Press & Sinter): 

   • Aluminum powder reinforced with 20 vol% SiC, along with 0.5% zinc stearate (lubricant) and 1% magnesium (wetting agent), was compacted under 400–600 MPa and sintered at 600–650 °C in an inert atmosphere to form a porous AlSiC metal matrix.

   • The controlled porosity (10–25%) was optimized to enable uniform polymer infiltration without compromising strength. 

2. Polymer Infiltration:

   • A self-healing polymer based on furfuryl amine (furan), DGEBA epoxy resin, and bismaleimide (BMI) was synthesized via a thermally reversible Diels–Alder reaction. T

   • he prepolymer was vacuum-infiltrated into the AlSiC matrix, ensuring deep pore penetration. Upon heating to 150–200 °C, crosslinking occurred; cooling reformed the bonds, thus enabling reversible healing of thermal microcracks. 

   • Adhesion promoters such as DMS-A32 siloxane were considered to improve metal–polymer interface strength.

Results:

The proposed AlSiC–polymer hybrid disc demonstrated potential for:

• 16–26% reduction in wear rate and 34% lower friction coefficient compared to conventional AlSiC discs.

• Reversible self-healing performance with up to 90% fracture toughness recovery over at least five thermal cycles.

• Effective healing activation at 150–200 °C — aligning with normal brake disc operating temperatures for commercial vehicles.

While material cost and high-temperature durability remain challenges, the project validated the technical feasibility of integrating self-healing functionality into metallic systems, paving the way for next-generation smart components in automotive manufacturing.

Report

Description:

As part of MIE506: MEMS Design and Microfabrication, I completed three simulation-based projects exploring the physics, design, and performance of micro-electromechanical systems (MEMS) using ANSYS Workbench. The projects covered thermoelectric power generation, electrothermal actuation, and electrostatic comb-drive motion, building a practical understanding of multiphysics coupling between electrical, thermal, and mechanical domains at the microscale. The course emphasized both analytical modeling and finite-element simulation, validating theoretical predictions through numerical experiments.

Methodology:

Each project combined analytical derivations with ANSYS Multiphysics simulations, incorporating temperature fields, electrical potentials, and structural deformation:

• Project 1: Thermoelectric Generator (TEG) Simulation: Designed and simulated a microscale thermoelectric generator consisting of p–n legs under a thermal gradient (ΔT = 430 K). Both Seebeck voltage and Peltier heat transfer were computed analytically and compared to FEM simulations. Material optimization was also performed using Tin Selenide (SnSe) to enhance power output. The SnSe-based design achieved a 342% increase in current and 332% increase in power compared to the baseline polysilicon model. 

• Project 2: Electrothermal V-Beam Actuator: Modeled a polysilicon V-beam actuator in ANSYS under voltages ranging from 0–5 V to study Joule-heating-induced deflection. Both analytical displacement (based on beam theory) and simulated results were compared, revealing an ~80% deviation attributable to the simplifying assumptions in the analytical model versus nonlinear coupled-field physics in ANSYS. The actuator showed increasing displacement with voltage but also rising temperature and stress, with beam melting predicted beyond 2.5 V (≈2690 °C). 

• Project 3: Electrostatic Comb-Drive Actuator: Developed a planar comb-drive MEMS actuator and simulated displacement under voltages from 30 V to 90 V using the PiezoAndMEMS extension in ANSYS. Analytical and numerical results were compared, highlighting realistic nonlinearities and fringing field effects. Key parameters such as mesh size (2 µm) and quadratic element order ensured convergence. The comb drive achieved micrometer-scale displacements (0.32–0.44 µm), demonstrating high-precision control with minimal steady-state power consumption. 

Results:

• TEG: Validated Seebeck voltage of 160.8 mV and current output of 24 A with only 2.4% error between analytical and simulated results.

• Electrothermal Actuator: Achieved displacement–voltage response consistent with nonlinear thermal expansion effects; identified critical voltage threshold before structural failure.

• Comb Drive: Demonstrated quadratic voltage–displacement dependence (x ∝ V²) and analyzed trade-offs between analytical simplicity and simulation fidelity.

These projects deepened my expertise in multiphysics modeling, MEMS design principles, and finite-element validation, bridging micro-scale theory with engineering practice. The cumulative work provided a strong foundation for designing and optimizing microactuators, sensors, and energy-harvesting devices across advanced manufacturing and microsystems engineering applications.

Description:

As part of MIE504: Applied Computational Fluid Dynamics at the University of Toronto, I completed a series of ANSYS Fluent–based simulations exploring laminar internal flows, external cylinder flow with heat transfer, and particle transport in complex geometries. These projects were designed to develop a strong foundation in CFD model setup, mesh validation, post-processing, and physics interpretation, bridging theoretical principles with industrially relevant applications such as flow optimization, heat management, and particulate transport.

Methodology:

Each project followed a systematic CFD workflow — including geometry creation, mesh independence testing, boundary condition definition, solver setup, and result validation.

• Project 1: Laminar Internal Flow (Fully Developed Flow Verification): Simulated steady laminar flow in a rectangular duct using a velocity inlet and pressure outlet to determine the entrance length and validate fully developed conditions. Conducted a grid refinement study (750–5950 elements) to confirm convergence with a criterion value below 0.01, selecting the 3000-cell mesh for efficiency. Compared analytical and simulated maximum velocities with <0.4% deviation. 

• Project 2: External Flow and Conjugate Heat Transfer (Cylinder Flow & Battery Cooling): Modeled external laminar flow over an aluminum cylinder (Re ≈ 124) and validated the drag coefficient (Cd ≈ 1.41) and Nusselt number (Nu ≈ 6.07) within 5–7% of theoretical predictions. Extended analysis to battery module cooling using air and mineral oil, determining the minimum required inlet velocity (0.6 m/s) to maintain battery surface temperature below 30 °C, with comparative analysis showing oil achieving 8–10 °C lower peak temperatures. 

• Project 3: Particle-Laden Flow and Transient Simulation: Investigated Discrete Phase Modeling (DPM) to analyze particle-wall interactions under varying mass flow rates (0.0031–0.0093 kg/s). Compared steady-state and transient simulations, observing that higher flow rates increased particle trapping by up to 9×, while time-dependent inlet variations produced smoother, more realistic deposition trends. Wall shear stress peaked at 17.6 Pa in the contraction zone, confirming local velocity gradients as the primary driver. 

Results:

Across all simulations, the findings validated theoretical predictions while deepening understanding of mesh quality metrics (aspect ratio < 4, skewness < 0.8), convergence criteria, and fluid–thermal coupling:

• Verified fully developed laminar flow within 0.3 m of duct length (error ≈ 10% vs. analytical).

• Achieved drag coefficient Cd = 1.41 (4.6% deviation from literature) and Nusselt number Nu = 6.07 (7.1% deviation).

• Demonstrated effective cooling optimization through inlet velocity control and fluid selection.

• Highlighted particle transport sensitivity to flow rate and transient conditions, with transient modeling yielding improved physical realism.

These projects strengthened my expertise in ANSYS Fluent, CFD post-processing, and engineering interpretation of simulation data, aligning closely with industrial fluid–thermal system design and optimization tasks.

Description:

As part of a team project for MIE870: Project Management, I collaborated with three peers to analyze the Edinburgh Trams Project — a large-scale public infrastructure initiative that faced significant cost overruns and schedule delays. The goal was to evaluate the project’s management practices, identify critical lessons aligned with PMBOK principles, and propose actionable improvements. Our work focused on understanding how stakeholder conflicts, contract management, and scope definition shaped project outcomes and what best practices could be derived for future megaprojects.

Methodology:

Using a structured project schedule and work breakdown structure (WBS), our team divided responsibilities across five main phases — Project Selection, Research and Analysis, Report Development, Presentation, and Final Reflection. I contributed to the Research and Analysis and Lessons Learned sections, synthesizing literature on risk management and stakeholder engagement. Tools such as MS Project and Gantt charts were used for planning and tracking progress, while PMBOK guidelines provided the evaluation framework. The deliverables included a detailed report, presentation, and reflective summary demonstrating team collaboration and iterative feedback cycles.

Results:

Our analysis highlighted key lessons such as the necessity of early stakeholder alignment, the risks of inadequate contract scoping, and the importance of adaptive scheduling and change management. The project improved my ability to translate theoretical PM principles into practical strategies, enhancing my competence in risk analysis, task scheduling, and teamwork coordination. Through this work, I gained a deeper appreciation of how project management tools and structured methodologies can mitigate real-world engineering challenges.

Report

Description:

As part of the graduate course ME1745: Surface Engineering, this project proposed the development of a smart coating capable of dynamically tuning its wettability in response to both light and temperature stimuli. Unlike traditional static superhydrophobic surfaces, these dual-responsive systems aim to enable reversible and spatially controlled wetting transitions. The motivation was to explore how combining azobenzene (photo-responsive) and pNIPAm (thermo-responsive) polymers could yield multifunctional surfaces with potential applications in anti-icing, oil–water separation, and self-cleaning technologies relevant to Canadian industries and climate challenges.

Methodology:

The study proposed fabricating heterogeneous coatings through spin-coating, photolithography, and soft lithography techniques. A pNIPAm layer served as the temperature-sensitive base, while azobenzene was patterned above it to provide localized light sensitivity. The resulting micro/nanostructures (grooves or pillars) were designed to amplify surface energy changes. Characterization methods included SEM, AFM, XPS, and FTIR to confirm surface morphology and chemistry, while contact angle goniometry under controlled thermal and optical conditions measured wettability transitions. Durability and scalability were further explored through cyclic tests and roll-to-roll manufacturing feasibility studies.

Results:

The proposed system is expected to exhibit reversible wettability shifts exceeding 100°, driven by the interplay between azobenzene’s rapid photoisomerization and pNIPAm’s thermally induced phase transition. Optimized microtopographies (20–40 µm spacing) are predicted to enhance responsiveness, while cyclic testing aims to demonstrate over 85% retention of performance after 100 stimulus cycles. Anticipated applications include reducing ice adhesion by up to 70%, achieving >90% efficiency in oil–water separation, and enabling self-cleaning efficiencies above 95%. Beyond these, the project establishes a scalable design framework for integrating smart coatings into sustainability-focused technologies across energy, transportation, and environmental sectors.

Report

Description:

This project explored the impact of microneedle shape on skin penetration and drug delivery efficiency. The primary objective was to design and fabricate microneedles with varying shapes to optimize mechanical strength and enhance drug absorption.

Methodology:

We designed microneedles with four distinct shapes and developed three key approaches to evaluate their mechanical performance and drug delivery efficiency. Methodologies were established to test skin penetration, including mechanical strength tests, efficiency measurements, and drug absorption rates. A step-by-step protocol involving ten testing stages was documented and followed to ensure the reliability and consistency of the microneedles.

Results:

The optimized microneedle designs demonstrated improved skin penetration and enhanced drug delivery. The established protocols provided a systematic framework for testing mechanical strength and drug absorption, ensuring the needles' compliance with performance standards.

Report

Description:

The project involved the development of a harness-based system utilizing IMUs and Arduino UNO to track scapular motion for rehabilitation purposes. The goal was to accurately measure the relative angles of the scapula to improve rehabilitation exercises for patients suffering from scapular dysfunctions.

Methodology:

We designed a harness with embedded IMUs to capture X, Y, and Z angles of scapular motion. The Arduino UNO served as the processing unit for the sensor data, and we extracted key metrics such as the range of motion and variability. To enhance monitoring accuracy, we trained the YOLOv8 computer vision model for object detection, achieving an mAP50 of 0.95 and mAP50-95 of 0.62. This integration of computer vision allowed us to track both rotational and translational scapular motion, surpassing traditional tracking methods.

Results:

The system successfully tracked scapular motion with high accuracy, providing valuable kinematic insights. The integration of computer vision significantly enhanced the precision of motion tracking, offering a comprehensive solution for scapular rehabilitation.

Report

Description:

This project focused on the design and analysis of a collet mechanism using MSC Adams. The objective was to create a mechanism capable of performing under various conditions, ensuring reliability and efficiency. We aimed to model the system with high accuracy, simulating both static and dynamic forces.

Methodology:

We began by designing a detailed collet mechanism in MSC Adams, incorporating static force analysis. The required torque at different cam positions (0º, 90º, 180º, 270º) was calculated to be 11 Nm. Dynamic motion analysis was then performed under driving forces of 5 Nm and 10 Nm. MATLAB was employed for cam profile generation, and kinematic simulations were conducted, showing a close match between theoretical and simulated displacement profiles with a maximum deviation of less than 1%. Sensitivity analysis was performed to assess the system’s robustness, revealing negligible impact from a 5% and 10% change in cam size. Joint clearances were analyzed at speeds of 2.5 RPM and 15 RPM to ensure smooth operation.

Results:

The analysis demonstrated that the collet mechanism was highly robust, with component variations and joint clearances showing minimal impact on performance. The simulated displacement profiles closely matched the theoretical values, ensuring the design's accuracy and reliability.

Part 1
Part 2

Description:

Our team embarked on an innovative project focused on harnessing energy from gym equipment, specifically targeting the Lat Pull Down machine. The key idea was to convert the linear motion generated by the machine into rotational motion, subsequently transforming it into electrical energy using a compound gear rack system and dynamo.

Methodology:

To achieve this, we designed an external attachment using Solidworks and conducted thorough static and dynamic analyses in Ansys to ensure the reliability of our gear tooth design. The chosen setup involved a compound gear system, rack gear mechanism, and a dynamo, all carefully integrated into the existing machine. We meticulously selected standard component sizes for ease of procurement, utilizing various nuts, bolts, and bearings. Additionally, we manufactured certain components, including the compound gear, rack, and the acrylic base for the dynamo system.

Results:

Our energy harvesting system successfully generated approximately 4.5 volts per dynamo. This output proved sufficient for charging smartphones and other electronic devices, showcasing the practicality and effectiveness of our solution.

Presentation

Report

Description:

In this project, our focus was on the material and process selection for sewing machine needles. Identifying potential failure mechanisms, such as buckling, impact load, and fracture strength, guided our approach. The objective was to minimize costs while enhancing slenderness, leading us to a comprehensive evaluation of material indices and performance equations.

Methodology:

After careful consideration, cast iron emerged as the optimal material for sewing machine needles based on its superior material index values. The next step involved meticulous process selection, considering factors like material properties, mass, section thickness, and surface roughness. Utilizing various process selection charts, we determined the appropriate shaping, machining, and finishing processes.

Results:

The step-by-step manufacturing process was outlined, detailing each stage from raw material to the final product. By integrating the selected material and processes, we achieved a sewing machine needle that not only met performance requirements but also minimized costs through thoughtful material and process selection.

Presentation

Description:

Our project focused on the intricate task of optimizing the design of a cantilever beam within a MEMS accelerometer. The goal was to enhance the beam's performance by exploring various dimensions, shapes, and patterns to achieve maximum deflection at the free end. 

Methodology:

• Base Model Selection:

  1. Initiated the project with a base model of a cantilever beam with a rectangular cross-section.

• Pattern Selection:

  1. Chose a circular slot pattern to introduce variations in the beam design.

• 3D CAD Modeling:

  1. Utilized Catia software to create solid models with different hole patterns, including:

     • A pattern of holes in a straight line.

     • A pattern with holes in two straight lines.

     • A pattern with a non-uniform distribution of holes.

  2. Varied dimensions (length, height, and breadth) across 6 combinations, resulting in 22 distinct 3D CAD models.

• Simulation in COMSOL:

  1. Conducted simulations on COMSOL for each model, mimicking the conditions of a proof mass of 0.5 micrograms experiencing a deceleration of 8 meters per second squared.

  2. Identified the combination and hole pattern that yielded the maximum beam deflection at the free end.

• Optimization of Hole Pattern Position:

  1. Selected the optimal combination of dimensions and pattern.

  2. Varied the position of the chosen pattern to determine the configuration that provided the maximum deflection.

• Geometry Variation:

  1. Explored the modification of the complete beam geometry by introducing a tapered shape with varying width.

Results:

Through this comprehensive approach, we successfully identified the optimal design parameters for the cantilever beam, maximizing its deflection. This optimized design is crucial for enhancing the performance of MEMS accelerometers, contributing to their accuracy and efficiency in measuring accelerations. The project underscores our commitment to precision engineering and innovative design in the field of micro-electromechanical systems.

Presentation

Introduction:

Optimizing the gear ratio in electric vehicles (EVs) is a critical factor influencing energy efficiency, performance, and battery consumption. Through simulations, we can evaluate the impact of different gear ratios on various parameters such as energy consumption, efficiency, torque, and top speed. Achieving the ideal gear ratio involves balancing objectives like energy consumption, efficiency, torque, and top speed, which, in turn, leads to better energy efficiency, improved driving performance, and extended range for EVs.

Modelling:

In the initial stages, we simplified the complex task of modeling a car for our analysis. We represented the car as an object with known mass and moment of inertia, focusing on a single wheel's dynamics rolling on a road surface. This abstraction allowed us to extrapolate our findings to a four-wheel-drive system without altering the optimum gear ratio.

Methodology:

Our methodology involved formulating mathematical expressions for efficiency and energy consumption. We constrained RPM and torque values based on motor efficiency contours, subsequently creating an expression for energy as a function of weight (W) and gear ratio (G). By analyzing a 3D plot visualizing energy variation, we determined an acceptable range for gear ratios. We introduced constraints for efficiency (>90%) and torque, leading to linear relations between gear ratio and weight for specific velocity values.

Objective:

• Maximize efficiency.

• Minimize energy consumption.

• Maximize top speed.

Selecting the Gear Ratio:

Analyzing the cumulative graph derived from various relations, we established criteria for selecting the optimal gear ratio. The chosen ratio should be above Gmin_Energy, within the range of Gmin_Torque and Gmax_Torque, and lie on the Gmin_Torque line to ensure efficient top-speed performance. We selected the gear ratio corresponding to 8000 RPM, achieving a top speed of 36.1 kmph with an efficiency of over 90% and minimal energy consumption. For practical versatility, we proposed a 3-speed gearbox allowing smoother operation and efficient performance across varying speed requirements.

Results:

We propose a 3-speed gearbox configuration:

• Gear 1: 45.9398 - Top speed 36.1 kmph

• Gear 2: 27.6571 - Top speed 60 kmph

• Gear 3: 18.4307 - Top speed 90 kmph

This optimized gearbox configuration ensures energy-efficient and high-performance operation, catering to different speed requirements in electric trucks.

Poster

Report

Description:

The project revolves around the numerical simulation of temperature distribution during orthogonal cutting, specifically addressing the challenges and limitations associated with practical temperature measurements. A comprehensive literature review was conducted to understand the current industry solutions to this problem. The focus of the analysis was on predicting the temperature profiles of the chip, tool, and workpiece through numerical simulations.

Need to Address the Problem:

Understanding the effects of temperature in cutting operations is critical, impacting various aspects such as cutting tool health, workpiece behavior, and final surface quality. The adverse effects include lowering the strength, hardness, and wear resistance of cutting tools, compromising dimensional accuracy in workpieces, and causing thermal damage to machined surfaces. Tool wear, influenced by mechanisms like flank wear, crater wear, and chipping of the cutting edge, adds another layer of complexity to the analysis.

Introduction and Assumptions:

The selected paper for comparison, titled "Modeling of Temperature Distribution in Orthogonal Cutting with Dual-Zone Contact at Rake Face," serves as a reference point. Assumptions crucial to the analysis include the machining operation being a plain strain operation (2D), the cutting tool being rigid and perfectly sharp, sliding friction existing between the tool-chip interface, and material properties remaining independent of temperature. The governing equations involve elasticity (Hook's law), plasticity (Johnson-Cook equation), and thermal considerations (2D heat equation).

Methodology:

The methodology involved identifying mathematical relations, selecting material properties and parameters, and outlining a step-by-step process for simulating the operation using ABAQUS software. The chosen mathematical model was based on the assumptions mentioned earlier. The simulation process included the application of constitutive equations for elasticity, the Johnson-Cook equation for plasticity, and the 2D heat equation for thermal considerations. The numerical method served as a practical alternative to overcome the challenges associated with traditional temperature measurements.

Results:

The simulation results demonstrated the maximum temperature at the tool-chip interface to be 1308 °C, aligning closely with the literature value of 1297 °C, indicating an accuracy of around 98%. The consistency between the simulation results and the mathematical model extracted from the literature highlights the reliability and effectiveness of the numerical approach. This accomplishment validates the utility of numerical simulations in predicting temperature distribution during orthogonal cutting operations, providing valuable insights for further advancements in the field.

Presentation

Description:

This project focuses on leveraging image processing techniques, specifically using a laser scanner to detect and categorize defects in multiple specimens. One specimen served as an ideal reference without defects, while the others exhibited various imperfections. Two distinct methods were employed for defect detection and characterization.

Methodology:

• Method Using CloudCompare:

  1. Utilized a laser scanner to capture point cloud data for both the ideal specimen and damaged samples.

  2. Employed CloudCompare software to simulate the scanned objects from the point cloud data.

  3. The software facilitated a comparison between the ideal and damaged specimens, enabling the characterization of differences visualized in the form of defects.

  4. This method provided a contour representation of defects when present, allowing for a qualitative assessment.

• Algorithmic Approach with Python:

  1. Obtained point cloud data in CSV format from the laser scanner.

  2. Implemented a custom Python code utilizing various algorithms for defect detection.

  3. Normal estimation was performed, following the mathematical procedures outlined in a reference paper, generating normal vectors at each point on the surface.

  4. Segmentation divided the points into two regions: defective and defectless. Points exceeding a predefined threshold angle were marked as defective.

  5. The final output was intended to provide a 2D image visualizing the segmented regions and defect locations.

  6. Despite errors in block 2, blocks 1 and 3 exhibited satisfactory performance.

Results:

• Block 1: Successfully estimated point normal vectors.

• Block 2: Segmentation block experienced errors, impacting the final output.

• Block 3: Effectively differentiated regions and executed image processing.

• The CloudCompare software demonstrated its effectiveness, providing a contour representation when defects were present.

Conclusion:

While the project encountered challenges in the algorithmic approach's segmentation block, the combination of the CloudCompare software and successfully executed blocks demonstrated promise in defect detection and characterization. Further refinement in the algorithm and code is identified as a potential avenue for improvement, ensuring a more robust and accurate defect detection process. The results obtained from CloudCompare offer valuable insights into defect contours, enhancing the overall efficacy of the project in identifying and understanding specimen irregularities.

Presentation

Report

Description:

The project investigates stress concentration in an Aluminum plate (6063) with dimensions 20 mm * 115 mm and a thickness of 3 mm. Holes with various fillet radii are incorporated. The MTS Uniaxial tensile machine is used for a tensile test, and the DIC method captures displacement and strain fields. Nominal stress at approximately maximum load is determined, and the stress concentration factor (Kt) is estimated for each hole. The study aims to empirically explore stress concentration for different fillet radii, emphasizing the impact of abnormalities and sharp corners on stress concentration.

Methodology:

• Materials and Machines:

Aluminum sheet, Wire EDM, DIC, Black and white spray paint, MTS Tensile test machine, Drilling machine, Hand Saw, Filer.

• Material Properties:

Al 6063, Dimensions 115 mm * 20 mm * 3 mm, Fillet Radii 0 mm, 1.5 mm, 3 mm, 4.5 mm.

• Procedure:

  1. Cutting aluminum sheet into specimens.

  2. Employing Wire EDM to create holes with distinct fillet radii.

  3. Painting specimens using black and white spray paint.

  4. Conducting uniaxial tensile tests using a DIC setup.

  5. Analyzing images with NCorr software and MATLAB.

  6. Calculating the stress concentration factor (Kt) using experimental data.

Results:

• Kt1 = 1.80126, Kt2 = 1.78015, Kt3 = 1.77371, Kt4 = 1.76466.

• Observations: Kt1 > Kt2 > Kt3 > Kt4, indicating that the specimen with the smallest fillet radius has the highest stress concentration factor.

• Stress concentration values exhibit minimal variation over changes in fillet radius.

This comprehensive project offers insights into stress concentration factors influenced by fillet radii, contributing to the understanding of structural integrity in engineering design.

Report