John Vallance | Mechanical Engineering Portfolio

Featured Project

Through-Spindle Coolant Nozzle

Independently identified and resolved a $1,500+ recurring failure mode in CNC tool holder coolant delivery. Designed, manufactured, and validated a modular replacement through 20 prototype iterations.

★ Flagship Project

Modular Through-Spindle Coolant Nozzle Redesign

Machined on Mazak Quick Turn MY 250 — Fits Mazak Integrex i-200 Tool Holders

The OEM coolant nozzle suffers from thread seizure, corrosion, and a non-serviceable monolithic design. This project replaces it with a dual-material modular assembly — SS 316 threaded body for corrosion resistance, 15-5 PH coolant tube for strength — reducing maintenance cost by 97% and achieving $58,000+ projected fleet savings over 5 years.

97%

Cost Reduction

$58K+

Fleet Savings

Prototypes

157×

Safety Factor

86%

FMEA Risk Reduction

Problem Identification

Redesigned nozzle installed in Mazak tool holder — modular tube protrudes through the threaded body, sealed with O-rings and retained by internal spiral clip ring.

Root Cause Analysis

The OEM design couples a consumable component (nozzle, $150) to a high-value component (tool holder, $1,500) through an irreversible Loctite joint. Three failure modes were identified:

Thread seizure — Loctite bond exceeds thread yield, destroying the tool holder during replacement
Corrosion — 4130 steel (PREN = 1.6) degraded by alkaline coolant, contaminating the coolant loop
Non-serviceable design — single-piece construction requires full replacement for any wear

$1,500

Per-Event Risk

720

Max RPN (OEM)

Material Selection & Dual-Material Architecture

Nozzle components and assembly showing modular architecture

Complete modular nozzle assembly — SS 316 threaded body, 15-5 PH coolant tube, O-rings, washer, and internal retaining ring.

First-Principles Material Selection

Material selection was driven by quantitative Pitting Resistance Equivalent Number (PREN) calculations, not convention:

Material	PREN	Role
AISI 4130 (OEM)	1.6	Inadequate
SS 316	26.9	Threaded Body
15-5 PH H1025	15.4	Coolant Tube

Dual-material architecture optimizes each component for its primary loading condition — corrosion resistance for the permanently-installed body, yield strength for the replaceable tube.

PINN-predicted velocity field inside the nozzle tube

Physics-Informed Neural Network (PINN) predicted velocity field — Navier-Stokes equations embedded directly into the PyTorch neural network loss function.

Fluid Dynamics & Structural Verification

Internal flow analyzed with Bernoulli's equation and Darcy-Weisbach friction analysis:

Reynolds number: 32,962 (turbulent)
Nozzle pressure drop: only 5.46 PSI (2.56% of supply) — not flow-restricting
Tube wall hoop stress: 6.35 MPa vs 1,000 MPa yield → 157× safety factor
O-ring squeeze: 22% (within Parker ORD 5700 spec of 15–30%)

157×

Hoop Stress SF

115×

Thread Shear SF

Manufacturing

Mazatrol CNC program for the threaded body

Mazatrol CNC program "THREADNOZZLE" — conversational programming for the threaded body at 2,000 RPM with 425 SFM finishing pass.

CNC Manufacturing Process

All components machined on the Mazak Quick Turn MY 250 CNC turning center using Mazatrol conversational programming (Mazatrol certified — the nozzle itself is designed for the Integrex i-200's tool holders):

OD roughing / finishing at 425 SFM, 0.002 IPR diamond insert
O-ring grooves with programmed groove profile
External thread M20 × 2.0, 6g tolerance
~20 minute cycle time per threaded body

Mazatrol program running on Mazak Quick Turn MY 250

Nozzle machined on the Mazak Quick Turn MY 250 CNC turning center — Mazatrol conversational programming, threading operation shown at 2,000 RPM.

Quality Control & Metrology

Dimensional inspection performed with calibrated instruments:

Cpk ≥ 1.25 on pitch diameter
Cpk ≥ 1.42 on bore ID
Cpk ≥ 1.98 on OD measurements
TIR < 0.002" on concentricity (Mahr dial indicator)
All sealing surfaces verified ≤ 32 Ra μin

Dial indicator runout measurement on Mahr stand — verifying tube-to-thread concentricity within TIR < 0.002".

20 Prototype Iterations, 5 Major Revisions

Each failure mode was treated as a first-principles learning opportunity:

v1–v3

Coolant Leakage Past O-Rings

O-ring compression below 15% Parker minimum → Redesigned gland geometry to achieve 22% squeeze.

v4–v6

O-Ring Extrusion Under Pressure

Gland fill exceeded 85%, causing extrusion into clearance gap at 213 PSI → Reduced gland depth, verified fill at 71.9%.

v7–v9

Thread Burr Micro-Leak Paths

Threading insert chip buildup created burr → Added deburring pass, adjusted finish feed rate.

v10–v12

Abnormal Vibration

Off-center bore from insufficient boring bar rigidity → Improved rigidity, added mandatory runout verification.

v13–v20

Design Validated

All failure modes eliminated. Zero leakage at 213 PSI. Tool-free swap confirmed. Inspection protocol established.

Advanced Analysis — PINN Validation

PINN predictions vs analytical calculations comparison

Physics-Informed Neural Network (PyTorch) predictions vs. analytical solutions — validating centerline velocity, pressure drop, and flow regime across 15,000 training epochs on ~9,000 collocation points. The PINN embeds Navier-Stokes equations directly into the loss function, learning flow fields without a computational mesh.

PINN pressure field prediction across the nozzle domain.

Why PINNs Matter

PINNs combine deep learning with governing physics — the network learns field predictions that simultaneously satisfy conservation of mass and momentum.

This demonstrates interdisciplinary competency in both machine learning and fluid mechanics, applied to a real engineering problem. PINNs represent the frontier of computational mechanics for fluid systems.

15K

Training Epochs

Collocation Points

Results & Impact

Completed threaded body with tube insert — M20 × 2.0 external thread, O-ring gland, and coolant tube.

Quantified Business Impact

Per-unit material cost: $12.70 vs $150 OEM (92% reduction)
Tool holder damage risk: eliminated entirely (was ~30% per event)
Nozzle swap: < 90 seconds tool-free vs 15+ min risky removal
5-year fleet savings (10 tool holders): $58,258
FMEA total RPN: 1,832 → 255 (86% reduction)
Critical RPNs above 200: 4 → 0

Machined nozzle components — threaded body, coolant tube, O-ring seals

Live tooling turret and Kitagawa chuck on Mazak Quick Turn MY 250

Live tooling turret with Kitagawa chuck on Mazak Quick Turn MY 250

CAD / SolidWorks CNC Machining FMEA Fluid Dynamics PINN / Machine Learning GD&T Materials Science Metrology

Project Portfolio

Engineering Projects

Five additional projects spanning control systems, biomedical sensing, structural analysis, precision manufacturing, and mechanical reverse engineering.

A two-wheeled robot that stabilizes an inherently unstable inverted pendulum using real-time closed-loop feedback control at 200 Hz. Integrates mechanical design, embedded electronics, sensor fusion, and PID control theory — the same fundamental problem as keeping a rocket vertically oriented during powered descent.

200 Hz

Control Loop

±2°

Steady-State Error

>5 min

Sustained Balance

1.89×

Torque Margin

Robot & Circuit Design

Technical design illustration of the two-wheeled self-balancing robot

Technical illustration of the two-wheeled self-balancing robot concept — showing the mechanical layout with NEMA 17 stepper motors, chassis frame, and battery placement. The physical build uses a laser-cut birch plywood frame with press-fit assembly.

System Architecture

The control challenge is the classical inverted pendulum — with a positive real pole at s = +21.2 rad/s, the uncontrolled robot falls in under 40 ms.

Controller: Discrete PID with anti-windup clamping, zero-crossing integral reset, and deadband attenuation
Tuned gains: Kp=4, Ki=0.08, Kd=3
Sensor fusion: Complementary filter (α=0.98) combining accel + gyro
Recovery: Disturbance recovery within ~1.5 seconds

Robot design visualization showing component layout

Design visualization — center of mass at Z̄ = 29.7 mm above axle, moment of inertia 2.54 × 10⁻³ kg·m², motor torque margin of 1.89×.

Mechanical Design & Dynamics

Center of mass analyzed with 13-component mass distribution table. Battery pack positioned high (39.2% of MOI) to increase pendulum length and reduce natural frequency — more time for the controller to react.

Key finding: The stepper motors provide 0.6 N·m combined dynamic torque vs 0.317 N·m required at maximum tilt — a comfortable 1.89× margin.

Control Performance

PID tuning trials with quantified performance metrics

15+ documented PID tuning trials — systematic approach combining Ziegler-Nichols-inspired oscillation testing with real-time serial gain adjustment.

Iterative PID Tuning

Systematic tuning methodology with real-time serial command interface — gain adjustments without reflashing firmware. Stability boundary characterized: ±25° tilt recovery, ±80°/s angular rate limit.

~45° phase margin and ~8 dB gain margin estimated from step response characteristics.

Step response of the tuned PID system — ~15% overshoot, ~1.2s settling time (2%), consistent with ζ ≈ 0.5.

Step Response Validation

Disturbance rejection validated: robot recovers from gentle push perturbations within ~1.5 seconds. Sustained balancing for >5 minutes without intervention.

The complementary filter crossover (0.65 Hz) sits below the robot's natural frequency (~4 Hz), ensuring the gyro captures all relevant dynamics while the accelerometer provides the long-term reference.

Firmware Implementation

PID controller implementation — sensor reading and filtering

Sensor initialization and MPU-6050 I2C setup

Complementary filter — gyro + accelerometer fusion

calculatePID() — anti-windup, deadband, output clamping

Motor control — A4988 step pulse generation

Serial tuning interface — live gain adjustment

Main loop — 200 Hz deterministic timing

Anti-windup strategy — integral clamping + zero-crossing reset

Setup — IMU calibration and initial angle read

Circuit & Hardware

Circuit diagram — dual power rail design isolates noisy motor supply (12V Li-Ion) from logic supply (9V) to prevent I2C corruption during current transients.

Electrical System Design

Dual power rail architecture separates logic from motor supply — critical for noise isolation:

Logic: 9V → Arduino → 5V regulated (Arduino, MPU-6050, A4988 logic)
Motor: 12V Li-Ion 3S → A4988 VMOT → NEMA 17 coils (2–3A)
A4988 current limit set to 1.0A via VREF (reduces heat, extends battery)
Battery life: ~1.4 hrs continuous balancing, ~3–4 hrs average use

PID Control Sensor Fusion Embedded C++ Arduino Control Theory Real-Time Systems

An optical heart rate monitoring system using photoplethysmography (PPG) — detecting volumetric blood changes via infrared and red LED reflectance. Validated against a commercial reference device within ±1.6 BPM at rest and ±1.4 BPM post-exercise, with full calculus-based waveform analysis.

±1.6

BPM Accuracy (Rest)

±1.4

BPM Accuracy (Exercise)

28.5 dB

SNR (Resting IR)

100 Hz

Sample Rate

Hardware & Soldering

Assembled PPG heart rate monitor circuit

Assembled PPG circuit — MAX30102 sensor breakout soldered with header pins, connected to Arduino Uno via I2C (SDA/SCL), powered via USB.

Embedded Sensor Integration

Built a complete data acquisition system: MAX30102 optical sensor with dual LEDs (IR 880nm + Red 660nm), 18-bit internal ADC, I2C fast mode at 400 kHz streaming to host computer at 115200 baud.

Dual-channel Red + IR simultaneous acquisition
100 Hz sample rate, 10ms period
Moving average filter (window=5) for noise rejection
Derivative-based peak detection algorithm

Soldering header pins to MAX30102 sensor

Soldering header pins to the MAX30102 breakout board — proper tinning technique for reliable through-hole joints.

Prototype Assembly

Soldered header connections and verified electrical continuity before powering. Proper strain relief on jumper wires ensures reliable connection during finger placement adjustments.

The solderless breadboard platform allows rapid iteration on sensor positioning and shielding against ambient light interference.

Signal Acquisition & Processing

Resting PPG waveform with peak detection

Resting PPG waveform (IR channel) — clear systolic peaks with sharp upstrokes (~0.15s rise time), visible diastolic notch, and respiratory amplitude modulation.

Resting State Analysis

Mean HR: 72.4 BPM with 14.3 BPM standard deviation (CV = 19.8%) — healthy heart rate variability reflects the dynamic sympathetic/parasympathetic balance.

SNR of 28.5 dB on IR channel exceeds the 10 dB minimum for reliable peak detection by 18.5 dB.

72.4

Mean BPM

19.8%

HRV (healthy)

Post-exercise PPG waveform — visibly faster oscillation, near-zero HRV (CV = 0.96% under sympathetic dominance), reduced AC amplitude from vasoconstriction.

Post-Exercise Analysis

Mean HR: 124.6 BPM with only 1.2 BPM standard deviation — sympathetic dominance produces a metronomically regular heartbeat.

Resting-to-exercise differential: Δ52.2 BPM (p < 0.01, statistically significant). Heart rate recovery follows the exponential decay model HR(t) = HR_rest + ΔHR · e^-t/τ.

Calculus-Based Analysis

First derivative of resting PPG signal — zero crossings identify peak locations for beat-to-beat interval computation.

Derivative-Based Peak Detection

Peak detection uses first-derivative zero-crossings with amplitude thresholding. Second derivatives characterize waveform morphology to classify systolic peaks vs. diastolic notches.

Trapezoidal numerical integration computes total cardiac work over measurement windows — the area under the PPG curve as a proxy for cardiac output demand.

Resting vs exercise heart rate comparison

Resting vs. post-exercise heart rate comparison — statistically significant shift (p < 0.01) validating the sensor under two distinct physiological states.

Comparative Validation

System validated against a commercial reference device at ±1.6 BPM resting and ±1.4 BPM post-exercise — achieving consumer wearable accuracy at ~$15 total prototype cost.

The project demonstrates that the fundamental signal processing pipeline (acquire → filter → detect → compute → validate) is identical regardless of whether the form factor is a wristwatch or a breadboard.

Circuit Diagram & Data

Full circuit schematic — MAX30102 to Arduino I2C wiring with decoupling capacitor

Raw resting heart rate data on serial plotter

Raw serial plotter output — resting heart rate PPG signal

Post-exercise serial plotter output — elevated frequency

MAX30102 sensor and photoresistor comparison

MAX30102 optical sensor close-up — dual LED + photodetector array

Resting dataset — 600 samples at 100 Hz

Post-exercise dataset — elevated BPM clearly visible

Exercise derivative analysis — peak detection on post-exercise waveform

Signal Processing PPG / Biosensing I2C / Embedded Data Analysis Calculus / Statistics Biomedical

Analyzes the classical stress concentration problem — a circular hole in a tensile plate — through three independent methods: analytical Kirsch solution, finite element analysis (Fusion 360), and experimental strain gauge measurement. Confirms the theoretical 3× stress concentration factor and quantifies discrepancies between methods.

3×

Kirsch SCF

FEA

Fusion 360

με

Strain Gauge

3-Method

Validation

Analytical Solution

Kirsch stress distribution around circular hole

Kirsch analytical stress distribution around a circular hole in a thin plate under uniaxial tension — maximum stress concentration factor of 3× at the hole's edge (θ = 90°).

Kirsch Analytical Solution

Stress concentration at the hole occurs because load-carrying "path" is disrupted — the material must redistribute stress around the discontinuity. The Kirsch solution predicts σ_max = 3σ_nominal at the hole edge perpendicular to loading.

Polar coordinate stress functions derived from Airy stress function
σ_r, σ_θ, τ_rθ computed in closed form
SCF decays to far-field stress within ~2 hole diameters

Polar representation of the stress field — showing the angular dependence of σ_θ at the hole boundary.

Assumptions & Limitations

Kirsch's solution assumes a large, flat plate with a small hole — the 3× factor may not hold exactly if the hole is large relative to the plate, or if the plate boundary is too close.

Real-world assumptions: perfectly uniform tension at edges, no misalignment, geometrically perfect hole, and homogeneous material properties.

FEA Simulation (Fusion 360)

FEA von Mises stress contour — hot spots at the hole edge confirming the Kirsch prediction. Mesh refined near the hole for accuracy.

Finite Element Analysis

Modeled the plate in Fusion 360 with polycarbonate properties (E ≈ 2.4 GPa, ν ≈ 0.37). Applied fixed boundary condition at one end and tensile load at the opposite.

Refined mesh near hole for high stress gradient resolution
Solved for elastic stress/strain field
Maximum stress at hole edge compared to theoretical 3× factor

Principal stress vectors — showing tension redistribution around the hole.

Principal Stress Field

Principal stress vectors illustrate how the load path is redirected around the geometric discontinuity. At the hole's edge tangent to loading, only hoop stress (σ_θ) exists — the radial component vanishes, concentrating the full load into the tangential direction.

Exaggerated deformation from FEA — the plate elongates and hole becomes oval, confirming the load redistribution pattern.

Deformation Pattern

The deformation shape — plate elongation and hole ovalization — visually confirms the stress redistribution. Material directly in line with the load direction sees the highest stress amplification.

This is why engineering design avoids sharp internal corners: fillets redistribute stress, reducing concentration factors from theoretical infinity to manageable values.

Experimental Validation

Strain gauge time series during tensile loading — stable readings throughout test.

Strain Gauge Measurement

A bonded foil strain gauge measured surface strain near the hole. Data acquired via Arduino with real-time serial streaming.

Discrepancies between theoretical and experimental values analyzed: material variation from handbook values, gauge calibration tolerance, bonding quality, alignment, and boundary condition idealizations.

Stress concentration factor decay from hole edge

SCF decay curve — showing how stress concentration factor diminishes with distance from the hole edge, converging to the far-field nominal stress.

SCF Decay Profile

The stress concentration factor decays from its peak (3.0×) at the hole edge toward 1.0× (far-field) within approximately 2 hole diameters — the "Saint-Venant's principle" in action.

This decay profile guides mesh refinement requirements in FEA and strain gauge placement in experimental work.

Cross-Validation

FEA vs analytical vs experimental comparison

Three-method cross-validation — FEA vs analytical vs experimental results comparison.

Three-Method Validation

Comparing analytical (Kirsch), computational (FEA), and experimental (strain gauge) results quantifies the accuracy of each approach and reveals real-world error sources.

Understanding why these methods disagree — and by how much — is more valuable than any single number.

Hand-derived Kirsch stress equations in polar coordinates

Numerical evaluation with plate dimensions and loading

Force integration — verifying static equilibrium via stress integration

Python analysis script output — stress profile computed along measurement line

Stress Analysis FEA / Fusion 360 Strain Gauges Experimental Validation Kirsch Solution Elasticity

Production machining experience at American Precision Machining producing tight-tolerance components for industrial, hydraulic, and instrumentation applications. Operated multi-axis CNC equipment, programmed using conversational and G-code methods, and verified finished dimensions with calibrated metrology — routinely holding tolerances as tight as ±0.0005".

100%

QC Pass Rate

±0.0005"

Tightest Tolerance

Ra 12.4

Best Finish (μin)

5-axis

Simultaneous

Machined Parts

Precision stainless steel components — machined to ±0.0005" tolerances with surface finishes to Ra 12.4 μin.

Four-Part Manufacturing Portfolio

Part A: Multi-threaded brass fitting body — three thread pitches, hex flange via live tooling, cross-drilled holes, ±0.0005" thread tolerances.

Part B: Stainless steel flanged hub — 6-hole bolt circle, round-to-hex transition, internal threading, perpendicularity ≤ 0.001".

Part C (Most Complex): Stainless steel hydraulic manifold block — internal cross-drilled passages, 6-face machining, multi-axis operations, O-ring glands. ☆☆☆☆☆ complexity.

Part D: Stainless steel open-window cage — thin-wall machining (0.125" wall), deflection management, concentricity to TIR ≤ 0.001".

Additional machined components showing surface finish quality and geometric complexity.

Multi-Axis Machining Capability

Three CNC platforms operated for production:

Mazak Integrex i-200: 5-axis simultaneous milling/turning, B-axis tilting spindle, C-axis polar milling
Mazak Turning & Milling Lathe: Live tooling for secondary operations without re-fixturing
Hurco VMC: 3-axis vertical milling with WinMax conversational programming

Programming: MAZATROL conversational, G-code (ISO 6983), GibbsCAM for complex 5-axis work.

Detail view — thread form quality and surface finish on a machined component.

Metrology & Inspection

10+ metrology instruments used across 40 dimensions on 4 parts:

CMM for GD&T, true position, concentricity (18 features)
Mahr MarSurf XC 2 profilometer for Ra/Rz/Rq surface finish (20 measurements)
Inside/outside/pitch/depth micrometers, digital calipers
Thread plug and ring gages (Go/No-Go)
Pin gages for precision bore verification
Optical comparator for thread form inspection

Every dimension in spec: 40/40 conformance, 100%

Closeup of machined surface showing finish quality

Surface finish closeup — face-milled stainless steel achieving Ra 12.4 μin (0.31 μm).

Surface Finish Science

Surface finish directly impacts sealing, wear, fatigue life, and corrosion resistance. Achieved Ra 12.4–36.4 μin across all parts — well within aerospace hydraulic component requirements.

Theoretical finish predicted from cutting parameters (f²/8r formula), then verified with profilometry — demonstrating understanding of the relationship between machining parameters and surface quality.

Process Capability & Tolerance Analysis

Process capability analysis — Cp/Cpk computed for critical dimensions across all four parts. Most features achieve Cpk > 1.5, with the tightest at Cpk ≥ 2.0.

Statistical Process Control

Cpk values computed from measurement data:

Feature	Cp	Cpk
Thread pitch diameter	≥ 1.25	≥ 1.25
OD dimensions	≥ 1.98	≥ 1.98
Bore ID	≥ 1.42	≥ 1.42

All Cpk values exceed the 1.33 industry standard for capable processes, with most above 1.5 (Six Sigma threshold).

Tolerance stackup analysis — worst-case and RSS (root-sum-square) methods applied to assembly dimension chains.

Tolerance Stack Analysis

Worst-case tolerance consumption analyzed for assembly-critical dimension chains. RSS method applied to multi-feature tolerance stacks to determine statistical assembly clearance.

Understanding tolerance allocation is critical for manufacturing cost optimization — tighter than necessary tolerances add cost without functional benefit.

Surface roughness comparison across surfaces

Surface roughness comparison across all measured surfaces

Inspection deviation chart — measured vs nominal for all 40 features

Theoretical vs. measured surface finish validation

CNC operations capabilities dashboard

Tolerance consumption across assembly feature chain

Measurement uncertainty budget — instrument, environmental, and operator contributions

Surface roughness parameter relationships

Ra/Rz/Rq parameter relationships across surface types

CNC Machining 5-Axis Metrology / CMM GD&T / ASME Y14.5 SPC / Cpk MAZATROL G-Code GibbsCAM

Complete reverse engineering of a commercial mortise lock — from systematic disassembly and precision measurement through parametric SolidWorks reconstruction and functional FDM 3D-printed prototype. A mechanically sophisticated system with ~10 interacting components in a compact volume.

Components

SLDPRT Files

Print Iterations

Functional

Prototype

Assembly & Mechanism

Mortise lock SolidWorks assembly — 10 components with full mate relationships enabling kinematic simulation of handle rotation and deadbolt throw.

Reverse Engineering Methodology

6-phase structured workflow: Disassembly → Measurement → CAD Modeling → Assembly → 3D Printing → Functional Validation.

Systematic disassembly with component cataloging
Digital caliper measurements (0.01mm resolution)
Fully parametric SolidWorks models for all 10 components
Assembly mates preserving actual kinematics (concentric, coincident, limit mates)
FDM 3D printing with tolerance compensation (holes enlarged 0.2–0.4mm)

Engineering Analysis

Latch return spring force analysis — Hooke's Law derivation from spring geometry, with Wahl factor correction for shear stress.

Spring & Force Analysis

Derived the spring constant from geometry using the helical compression formula k = Gd&sup4;/(8D³N_a) = 3.96 N/mm. Computed preload force (10.10 N) and maximum working force (60.39 N).

Handle torque for latch retraction: 1.28 N·m — consistent with smooth, comfortable door handle operation.

Cam displacement analysis — deadbolt throw (15.88mm) derived from eccentric cam geometry s(θ) = e(1 − cos θ).

Cam Kinematics — Deadbolt Actuation

The deadbolt cam converts ~90° of key rotation into 15.88mm of linear deadbolt travel. The displacement relationship s(θ) = e(1 − cos θ) reveals that mechanical advantage peaks at start (θ → 0) and diminishes as the cam approaches full extension.

Force amplification: MA = 1/sin θ — a 50 N key force generates 70.7 N at the bolt at θ = 45°.

Material comparison — steel vs PLA bolt shear strength

Material substitution analysis — steel bolt (30 kN shear) vs. PLA bolt (6.2 kN shear), PLA provides ~20% of original security but validates mechanism function.

Material Substitution Analysis

Quantified the performance trade-off when substituting PLA for steel:

Steel deadbolt shear resistance: 30.0 kN (6,740 lbf)
PLA deadbolt shear resistance: 6.2 kN (1,387 lbf)
PLA latch bolt bevel angle increased from 15° to 25° to compensate for higher PLA-on-steel friction (μ = 0.35 vs 0.15)

PLA is a functional prototype material — sufficient for mechanism validation but requiring ~20% security margin reduction.

Handle force requirement curve — lever ratio reduces spring force from 60.39 N to 23.31 N at the handle, confirming comfortable operation.

Handle Ergonomics

The lever mechanism ratio (r₁/r₂) reduces the spring force experienced at the handle. Maximum handle force of 23.31 N — well below the 50 N threshold for comfortable prolonged operation.

Handle torque: T = F × r_handle = 1.28 N·m — smooth, effortless latch retraction.

Dimensional deviation — CAD vs physical measurement

Dimensional accuracy analysis — CAD model vs. physical part measurements across all components.

CAD Accuracy Validation

Periodic comparison of CAD geometry against physical parts ensured model accuracy. Design intent preservation: nominal dimensions inferred from measurement clusters, tolerances estimated from measurement scatter.

This process required inferring the designer's intent — not just copying geometry, but understanding why each feature is the size and shape it is.

Reverse Engineering SolidWorks CAD Mechanism Design FDM 3D Printing Cam Kinematics Material Substitution

Precision engineering from theory to reality.