An organized compilation of ideas for oneself to communicate strategically and implement practical ideas. Formed and written by Onri Jay Benally.
This tree of terminologies have been helpful to me over the years in the English-speaking world, and it also contains some of the best adverbs I have found to be effective in speech and literature. Enjoy.
Communication Strategy Tree - by Onri
├─ Reality Alignment
│ ├─ Literally – exact portrayal
│ ├─ Faithfully – integrity-anchored depiction
│ ├─ Metaphorically – figurative framing
│ └─ In its direct or true form – unmediated, canonical phrasing
│
├─ Degree of Certainty
│ ├─ Perhaps – tentative probability
│ ├─ Arguably – defensible, yet contestable, speaker-oriented epistemic stance (hedged assertion)
│ ├─ Relatively – context-bound qualification
│ ├─ Reasonably – logically justified, moderately confident
│ ├─ Plausibly – appears possible or credible given current knowledge
│ └─ Reliably – with consistent, repeatable outcomes under similar conditions
│
├─ Extent/ Completeness
│ ├─ Virtually – almost wholly (≈ 99 %)
│ ├─ Practically – nearly, within pragmatic limits (≈ 95 %)
│ └─ Effectively – operationally equivalent in outcome (≈ 95–99 %), results-focused
│
├─ Modal/ Counterfactual Stance
│ ├─ Hypothetically – speculative scenario testing
│ ├─ Theoretically – arithmetically supported, by logic or data-pattern model
│ ├─ Suppose – assumed premise posited for exploration
│ ├─ If it were any different – counterfactual premise introduction
│ └─ On the flip side – alternative-outcome contrast
│
├─ Methodological Lens
│ ├─ Critically – careful, evidence-weighing evaluation of assumptions, sources, causal claims, and alternatives
│ ├─ Analytically – evidence-weighted derivation (systematic)
│ ├─ Coherently – internally consistent, globally structured, logically connected delivery
│ ├─ Systematically – step-wise, protocol-driven approach
│ ├─ Strategically – purpose-aligned planning
│ ├─ At the implementation level – code-/hardware-layer perspective
│ ├─ Organically – emerging naturally from context without forced structure
│ └─ In other words – explicit rephrasing signal that restates the same idea with different wording, often simpler or more concrete, to tighten understanding and highlight equivalence
│
├─ Conditionality & Contingency
│ ├─ Conditionally – valid only under stated premise(s)
│ └─ By default – baseline assumption prior to override
│
├─ Ideal-Real Spectrum
│ ├─ Ideally – perfect-world benchmark
│ ├─ Realistically thus far – evidence-based checkpoint
│ └─ Currently deployed – empirically instantiated case
│
├─ Temporal & Agency Orientation
│ ├─ Chronologically – earliest → latest sequencing, order-preserving narration
│ ├─ From here – forward-linking waypoint marking that subsequent steps, implications, or actions proceed from the current result or discussion state
│ └─ Proactively – forward-acting, anticipatory intervention
│
├─ Attention & Salience
│ ├─ Notably – signals noteworthy element
│ ├─ Interestingly – highlights engaging detail
│ ├─ Of interest – flags relevant point
│ ├─ For context – situational background primer; activates shared background and constraints, organizes given→new delivery, and aligns with conversational relevance
│ └─ For reference – anchor or lookup pointer; supplies baseline value, canonical source, exemplar, or specification for comparison without advancing a claim
│
└─ Norms & Baselines
└─ Conventionally – by established practice or standard usage
Functional Analogy Categories to Help Explain Virtually Anything to a General Audience - by Onri
└─ Root
├─ Analogy Families
│ ├─ LEGO analogy
│ ├─ Gravity-based/ potential energy hill analogy
│ ├─ Basketball analogy
│ ├─ Football/ soccer analogy
│ ├─ Mechanical analogy
│ └─ Car-related analogies
├─ Abstraction & Convergence
│ ├─ Heat map
│ └─ “Spherical cow”
├─ Perception & Analysis
│ ├─ Can be felt or seen
│ └─ Analyzed with dots in a finite dimension
│ └─ Often two-dimensional (2D)
│ └─ Exception: three-dimensional (3D) chess
└─ Communication Reminder
└─ “Say it with your chest.”
Idea Formation Pathways - by Onri
Scaffolding
│ – Pre-formation of rudimentary tables & smaller trees
│ – Define meta-constraints & “success metrics”
│ │ ├─ Hardware-agnostic framing (portability across platforms, interfaces, and toolchains)
│ │ ├─ Resource-lean framing (compute/time/tooling/budget minimized by default)
│ │ ├─ Low-maintenance or maintenance-free targets (drift tolerance, minimal recalibration)
│ │ └─ Agnostic/ plug-and-play design targets (minimal integration friction, standardized I/O)
│ – Normalization pass (so that comparisons are meaningful, not merely numerical)
│ │ ├─ Units + dimensional consistency (SI coherence, temperature/pressure conventions)
│ │ ├─ Coordinate frames + reference baselines (what “zero” and “nominal” mean)
│ │ └─ Dataset normalization (for comparability across sources)
│ │ ├─ z-score standardization
│ │ ├─ min-max scaling
│ │ └─ log/ power transforms (when distributions are heavy-tailed)
│ – Best materials availability search (feasibility anchored to what can actually be procured)
│ │ ├─ Vendor + lead-time scan (forms, thicknesses, purities, MOQ, geographic sourcing)
│ │ ├─ Datasheet/property scan (thermal budget, conductivity, loss tangent, corrosion)
│ │ └─ Process/tool access scan (etches, deposition options, metrology availability)
│ – Interface + stack realism (when buildability matters, not just conceptual correctness)
│ ├─ Identification of adhesion layers (what makes layer A actually stick to layer B)
│ ├─ Identification of diffusion barriers (what prevents intermixing over time/temperature)
│ └─ Identification of other necessary barriers
│ ├─ oxidation barriers/ passivation
│ ├─ moisture barriers
│ ├─ electromigration/ ion-migration blockers (as relevant)
│ └─ thermal/ chemical compatibility constraints (as relevant)
│
├─ Formation of hierarchy/ mind-map/ pathway diagram(s)
│ ├─ Normalize the vocabulary + variable names
│ └─ Decision: which simulation to run?
│ ├─ Steady-state simulation
│ │ ├─ Resource-lean model selection (lowest-fidelity model that still answers the question)
│ │ ├─ Normalization embedded in inputs/outputs (units, scaling, boundary conventions)
│ │ ├─ Interpolation (within-domain gap-filling for sweeps, response surfaces, lookups)
│ │ └─ (optional) feed results into scatter-plot refinement
│ └─ Transient simulation (animated)
│ ├─ Normalization of time discretization (sampling, timestep stability conventions)
│ ├─ Interpolation (temporal/spatial resampling for analysis and visualization)
│ └─ (optional) feed results into scatter-plot refinement
│
├─ Formation of heatmap(s)/ action-consequence diagram(s)
│ └─ (Supplement with a gravity-hill diagram or a mechanical analogy)
│ ├─ Normalize axes, thresholds, and colormaps (so plots compare across runs/sources)
│ ├─ Interpolation to grid sparse samples (explicitly label uncertainty/ sparsity)
│ └─ Decision: which simulation to run if applicable?
│ ├─ Steady-state simulation
│ │ ├─ Interpolation for contouring/heatmap construction (domain-bounded)
│ │ └─ (optional) feed results into scatter-plot refinement
│ └─ Transient simulation (animated)
│ ├─ Interpolation for smooth animation/ phase-portrait extraction
│ └─ (optional) feed results into scatter-plot refinement
│
└─ Draft scatter-plot (based on the initial tables)
├─ Normalization/ standardization of plotted variables (so axes are interpretable)
├─ Overlay feasibility constraints (so “best” does not mean “impossible”)
│ ├─ Hardware-agnostic constraints (interface envelopes, power/thermal limits, I/O limits)
│ ├─ Resource-lean constraints (runtime limits, measurement limits, tooling limits)
│ └─ Low-maintenance constraints (calibration frequency, drift budgets, failure modes)
└─ Refine scatter-plot (incorporate data produced by any simulation or literature/availability search)
├─ Interpolation (in-domain completion; explicitly bounded by evidence)
├─ Strategic extrapolation (out-of-domain hypotheses, but with guardrails)
│ ├─ Assumption-explicit forecasting (what must remain true for the extrapolation to hold)
│ ├─ Sensitivity analysis (which parameters dominate the extrapolated claim)
│ ├─ Uncertainty tagging (error bars, confidence regions, scenario bands)
│ └─ “Repair move” if extrapolation dominates:
│ ├─ expand the dataset (new measurements, new sources)
│ ├─ increase model fidelity (only where it changes the decision)
│ └─ narrow the claim (re-scope to the domain where evidence exists)
├─ Materials + stack refinement loop (feasibility converges alongside performance)
│ ├─ Re-run best materials availability search (substitutions, alternates, constraints)
│ └─ Re-validate adhesion layers/ diffusion barriers/ other barriers vs process window
├─ Plug-and-play readiness check (integration cost treated as a first-class metric)
│ ├─ Interface standardization (mechanical/electrical/software, as applicable)
│ ├─ Minimal-step bring-up + self-test hooks (where feasible)
│ └─ Documentation artifacts (checklists, parameter defaults, “known-good” configs)
└─ Comprehensive scatter-plot (final result)
├─ Exportable decision artifacts (tables, heatmaps, constraints checklist, BOM notes)
└─ Shortlisted candidates (hardware-agnostic, resource-lean, low-maintenance, plug-and-play)
Towards a Resource Lean Hardware Engineering Strategy - by Onri
Resource-lean Hardware Engineering Strategy
│
├─ Intuition-driven Early Design & Leap-frogging
│ ├─ Do order-of-magnitude checks (size, voltage, frequency, mass)
│ ├─ Rank design levers (material, geometry, feed-through) by one-change impact
│ ├─ Sketch performance barriers (max field, thermal limit) before any simulation
│ ├─ Quick validation: generate a coarse ParaView heat-map from a low-res GDSTK model
│ └─ Update priors; if no equilibrium, inject damping/feedback → closed-loop potential V₍closed-loop₎
│
├─ Open-source Design & Simulation Stack
│ ├─ Geometry definition & parametric CAD → GDSTK (Python API)
│ ├─ 3-D modelling & mesh generation → Blender + Blender-Python scripts
│ ├─ Curve-fitting & Approximation Techniques
│ │ ├─ Polynomial & least-squares fitting (NumPy polyfit, SciPy curve_fit)
│ │ ├─ Rational/ Padé approximants (SciPy, custom Python utilities)
│ │ ├─ Spline & B-spline/ NURBS fitting (SciPy interpolate, NURBS-Python)
│ │ ├─ Robust/ RANSAC regression (scikit-learn) for outlier-tolerant fits
│ │ ├─ Regularized & Bayesian fitting (PyMC, TensorFlow Probability) for
│ │ │ uncertainty-aware models
│ │ ├─ Machine-learning surrogates (Gaussian Processes, lightweight neural nets)
│ │ └─ Hybrid tetrational-Bezier approximation
│ │ • Combines iterated tetration with classic Bezier control points
│ │ • Generates high-order, smooth profiles with very few
│ │ control points → low memory & compute cost
│ ├─ Electromagnetic solver → openEMS (FDTD) consumes GDSTK geometry
│ ├─ Field visualization → ParaView (remote/Colab-linked)
│ ├─ Cloud compute & notebooks → Google Colab (run GDSTK, openEMS, post-process)
│ ├─ Collaborative version control → GitHub (repo, Issues, CI with GitHub Actions)
│ ├─ Optional electronics layout → KiCad (open-source PCB) & FreeCAD (mechanical)
│ ├─ Qiskit Metal – quantum-hardware layout & EM design
│ │ ├─ Open-source Python package (part of the Qiskit ecosystem) for
│ │ │ superconducting qubit, resonator, and package geometry creation.
│ │ ├─ Generates 3-D models that can be exported directly to GDSTK or
│ │ │ to a Blender scene for further meshing.
│ │ ├─ Built-in parametric constraints (trace width, substrate thickness,
│ │ │ coupling gaps) let you explore design space with the same intuition-driven
│ │ │ “one-change’’ ranking used elsewhere.
│ │ ├─ Runs in Google Colab via a pre-built Docker image (GPU optional) – the
│ │ │ notebook pulls Qiskit Metal, builds the layout, and hands the mesh to
│ │ │ openEMS for full-wave simulation.
│ │ ├─ Coupled to Qiskit’s circuit simulators (Aer, Aer-GPU) so you can
│ │ │ co-simulate the quantum circuit (e.g., gate fidelity) together with
│ │ │ the electromagnetic environment (crosstalk, Purcell loss).
│ │ └─ All geometry files, material tables, and simulation results are
│ │ version-controlled in the same GitHub repo as the rest of the project.
│ └─ Advanced low-cost HPC simulation – AWS Palace
│ ├─ Spot-instance EC2 fleet + AWS ParallelCluster for massive FDTD/ Meep sweeps
│ ├─ Pre-baked AMIs that contain openEMS, Meep, GDSTK, Python env. → one-click launch
│ ├─ Job orchestration via AWS Batch/ Step Functions; auto-scale to demand
│ ├─ Input/Output stored on S3 (versioned, cheap, lifecycle-controlled)
│ ├─ Interactive notebook front-end on Amazon SageMaker Studio (or Studio Lab)
│ │ → launch ParaView remote-rendering sessions directly from the notebook
│ ├─ Cost-control: Spot-price alerts, max-price caps, auto-shutdown of idle clusters
│ └─ CI integration – GitHub Actions can trigger a Palace run on every PR,
│ archive results as artefacts, and post a ParaView snapshot as a comment.
│
├─ Open-source Multiphysics & Micromagnetics (Google Colab)
│ ├─ Micromagnetics (GPU-accelerated)
│ │ ├─ MuMax3 – runs natively on Colab GPU runtimes via a Docker image;
│ │ │ Jupyter notebooks drive geometry import (GDSTK → OMF), material tables,
│ │ │ time-step scripts, and automatic post-processing with ParaView.
│ │ ├─ OOMMF – classic CPU-only code; compiled for Linux-x86 and invoked from Colab
│ │ │ using a persistent “/content/oommf” directory; results visualized with
│ │ │ `oommf::boxsi` or exported to VTK for ParaView.
│ │ └─ Fidimag – Python-based micromagnetic framework built on FEniCS; useful for
│ │ research-grade custom energy terms and easy integration with
│ │ SciPy optimisation loops.
│ ├─ Optics/ Photonics (continuum & wave)
│ │ ├─ MEEP (MIT-licensed FDTD) – already packed in the Colab-friendly image;
│ │ │ supports sub-pixel smoothing, dispersive media, and near-field scans.
│ │ ├─ pyMieScatt – analytical Mie-scattering calculations for nanoparticles.
│ │ └─ OpenFDTD/ FDTD-Calc – lightweight wrappers for rapid 2-D/3-D simulations.
│ ├─ Large-scale Magnetics & Induction
│ │ ├─ MagPar – finite-element micromagnetics for bulk magnetic components;
│ │ │ can be compiled on Colab’s Ubuntu environment.
│ │ ├─ Nmag – FEM micromagnetics that couples to PETSc; useful for
│ │ │ multi-physics (magneto-mechanical) studies.
│ │ └─ gprMax – open-source electromagnetic wave propagation (including RF/MW);
│ │ runs on CPU/GPU and can be scripted from Python notebooks.
│ ├─ RF/ Microwave Circuit & Antenna
│ │ ├─ openEMS (already in the stack) - extend with circuit-extraction (S-parameter)
│ │ │ post-processing scripts written in Python.
│ │ ├─ Qucs-S – circuit simulation (SPICE-like) for RF; command-line usage from Colab.
│ │ └─ PyAEDT (open-source wrapper) - can drive an offline install of ANSYS
│ │ Electronics Desktop when a licensed installation is available;
│ │ otherwise fall-back to openEMS.
│ ├─ Mechanical & Multi-physics (continuum)
│ │ ├─ Calculix – FEM for static/dynamic structural analysis;
│ │ │ driven by Python front-end (PyCalculix) and visualized in ParaView.
│ │ ├─ Elmer FEM – supports coupled magneto-thermal-mechanical problems;
│ │ │ scripts run on Colab via a small pre-built Docker image.
│ │ └─ OpenFOAM – CFD (including magnetohydrodynamics) – can be compiled
│ │ and executed on Colab’s CPU nodes; data streamed to ParaView.
│ ├─ Molecular-Dynamics (MD)
│ │ ├─ LAMMPS – general-purpose MD; GPU-accelerated package (`GPU` or `KOKKOS`);
│ │ │ input decks generated from GDSTK geometry → atomistic lattice.
│ │ ├─ GROMACS – biomolecular MD (useful for soft-matter or polymeric
│ │ │ adhesives); runs on Colab GPU with the `gmx_mpi` binary.
│ │ └─ ASE (Atomic Simulation Environment) – Python glue that spawns
│ │ LAMMPS, GPAW, or Quantum ESPRESSO runs and collects results.
│ ├─ Density-Functional Theory (DFT) & Quantum-Scale
│ │ ├─ Quantum ESPRESSO – plane-wave DFT; compiled for Linux x86 on Colab;
│ │ │ used for material-property extraction (permittivity, permeability,
│ │ │ magnetocrystalline anisotropy) that feed into higher-level models.
│ │ ├─ SIESTA – localized-basis DFT, lighter memory footprint for large cells.
│ │ └─ DFTB+ – density-functional tight-binding; fast for preliminary band-structure
│ │ calculations in a resource-lean context.
│ ├─ Hybrid/ Multi-scale Frameworks
│ │ ├─ Multiscale Modeling Toolbox (MMTB) – Python orchestrator that couples
│ │ │ continuum FEM (Calculix/Elmer) ↔ MD (LAMMPS) ↔ DFT (Quantum ESPRESSO).
│ │ ├─ PyMD-Hybrid – template scripts for handing off boundary regions
│ │ │ between micromagnetic (MuMax3) and atomistic (LAMMPS) domains.
│ │ └─ AiiDA – workflow manager that tracks provenance across all the
│ │ above codes, automatically storing inputs/outputs on Git-LFS.
│ └─ Colab-friendly Workflow
│ ├─ Every tool is wrapped in a Jupyter notebook cell that:
│ │ • pulls a Docker image or pre-compiled binary from the repository,
│ │ • mounts the project’s `/content/` directory (shared with Git-Hub),
│ │ • runs the simulation, and
│ │ • writes VTK/CSV results directly to an S3 bucket (or keeps them in the
│ │ notebook runtime for quick ParaView view).
│ └─ Notebook templates (saved in the repo) include:
│ • “Micromagnetics - MuMax3 Demo.ipynb’’,
│ • “Optical-FDTD - MEEP Demo.ipynb’’,
│ • “MD-Cascade - LAMMPS → Quantum ESPRESSO.ipynb’’,
│ • “Hybrid FEM-MD-DFT.ipynb’’,
│ • “Quantum-Hardware - Qiskit Metal Demo.ipynb’’.
│
├─ Resource-lean Fabrication Materials & Processes
│ ├─ Cardboard, corrugated paper, double-sided tape (cheap, biodegradable)
│ ├─ Modular origami-inspired foldable patterns designed in Blender
│ ├─ Water-based adhesives/ water-less dry-fit joints
│ ├─ Low-cost sheet plastics (PET, acetate) for moisture barriers
│ ├─ Hobby-grade 3-D printing (PLA/ABS) for functional hinges & enclosures
│ ├─ Laser-cutting from open-source SVG/DXF (Inkscape) → inexpensive batch cuts
│ ├─ Low-cost LASER-cut sheet metal & sheet-stock (2.5-D → folded-3-D substitute for expensive CNC-machined parts)
│ │ ├─ Why it replaces many CNC (computer numerical control) 3-D parts
│ │ │ ├─ Re-parameterize “bulk” 3-D into “developable” 2-D profiles + bends + fasteners (sheet-metal origami/ kirigami)
│ │ │ ├─ Use folds, hems, beads, and flanges to raise stiffness via section-modulus leverage (geometry beats mass)
│ │ │ ├─ Replace pockets/fillets with bend radii + relief cuts; keep load paths in-plane, then “lift” with bends
│ │ │ └─ Build self-aligning assemblies: tab-and-slot, captive features, and bend-up datums (jigless registration)
│ │ ├─ Sheet metals commonly LASER-cut (low-cost when thickness stays in standard shop windows)
│ │ │ ├─ Mild steel/ low-carbon steel (≈0.5–6 mm typical) – cheapest structural sheet for brackets and frames
│ │ │ ├─ Stainless steel (≈0.3–6 mm) – corrosion resistance; strong thin panels; clean edges with good settings
│ │ │ ├─ Aluminum (≈0.5–6 mm; fiber LASER preferred) – lightweight + thermal spreaders; watch dross and reflectivity
│ │ │ ├─ Spring steel (≈0.1–1 mm) – clips, compliant springs, EMI (electromagnetic interference) fingers
│ │ │ ├─ Brass/bronze (thin) – aesthetic panels, low-spark hardware, RF shielding
│ │ │ └─ Copper (thin; reflective + very conductive) – bus bars and shields; fiber LASER, or waterjet if reflectivity dominates
│ │ ├─ Non-metal sheet materials eligible for LASER cutting (often cheaper and faster than machining)
│ │ │ ├─ Acrylic (polymethyl methacrylate, PMMA) – clean CO₂-LASER edges; optical windows; light pipes; covers
│ │ │ ├─ PET/ PETG (polyethylene terephthalate/ glycol-modified) – guards, flexures; thin compliant frames
│ │ │ ├─ Polyimide (Kapton) film – insulation gaskets, flexible-circuit substrates, thermal/electrical barriers (thin)
│ │ │ ├─ FR-4 (flame-retardant glass-epoxy laminate) – adapter plates and stiffeners; needs fume control + conservative settings
│ │ │ ├─ Plywood/MDF/bamboo veneer – enclosures and jigs; seal edges for moisture stability
│ │ │ └─ Cardboard/paper – iteration-speed king; fold patterns
│ │ ├─ Materials to avoid on a typical shop LASER (and what to do instead so the design still “works”)
│ │ │ ├─ PVC (polyvinyl chloride) – corrosive chlorine chemistry; do not LASER-cut → switch to PET/PETG/acrylic, or waterjet
│ │ │ ├─ Acetal (polyoxymethylene, POM/Delrin) – formaldehyde fumes → route/CNC mill, or substitute PETG/nylon sheet
│ │ │ ├─ Carbon-fiber/epoxy sheet – hazardous dust/fumes → waterjet + sealed edge finishing, or swap to G-10/FR-4 where suitable
│ │ │ └─ Thick polycarbonate – tends to char/yellow → CNC route/waterjet, or “change identity” to acrylic if optical clarity is needed
│ │ ├─ Design-for-Manufacturability (DFM) rules that keep LASER-cut parts genuinely “low cost”
│ │ │ ├─ Kerf compensation: offset geometry by kerf width; always include a kerf coupon (same material, same thickness) per order
│ │ │ ├─ Bend reliefs: prevent corner tearing; add dogbones/slots at bend terminations; avoid trapped radii
│ │ │ ├─ Bend allowance math: K-factor tables; keep inside bend radius ≥ material thickness when possible for repeatability
│ │ │ ├─ Feature sizing: avoid holes < 1× thickness; avoid hole-to-edge < 1× thickness; avoid ultra-thin webs
│ │ │ ├─ Cost drivers: pierce count, micro-features, and tight tolerances → consolidate holes, use shared cut-lines, and relax where safe
│ │ │ └─ Heat-affected zone (HAZ): keep critical springs away from cut edges; specify deburr/tumble, and plan for edge rounding
│ │ ├─ Assembly patterns that “recreate 3-D” cheaply (and stay serviceable)
│ │ │ ├─ Tabs + slots + bend-up flanges → self-fixturing chassis (no custom fixtures needed)
│ │ │ ├─ Rivets, threaded inserts, and self-clinching fasteners → repeatable joints + reworkability
│ │ │ ├─ Spot weld/ braze/ solder (thin metals) when fasteners are too bulky or when conductivity is required
│ │ │ ├─ Captive nuts + access windows → tool-friendly field service and fast teardown
│ │ │ └─ Hybrid builds: LASER-cut “skeleton plates” + 3-D-printed nodes
│ │ ├─ Where sheet LASER cutting is a performance-per-dollar win over CNC
│ │ │ ├─ Enclosures, brackets, sensor mounts, battery trays, panelized fixtures, and alignment combs
│ │ │ ├─ Thermal spreaders/heat shields: aluminum/copper plates with vent patterns and fold-up stand-offs
│ │ │ ├─ EMI shields: folded cans, spring fingers, ground tabs (spring steel or stainless)
│ │ │ └─ Fluidics/optics: baffles, apertures, slit masks, and modular frames for tape-based or PET-based microfluidics
│ │ └─ File/format pipeline
│ │ ├─ Parametric patterns in Blender/GDSTK → DXF/SVG export (same “single source of truth” geometry idea)
│ │ ├─ Encode bend lines as etches + include part IDs; add kerf + bend coupons on every sheet
│ │ └─ Version-control cut files + bend notes + assembly drawings in GitHub alongside simulation + firmware
│ ├─ DIY metal-rod-based electrical discharge machining (EDM) using re-purposed 3-D printer motion parts (low-cost “spark erosion” for hard-to-machine metals)
│ │ ├─ High-school intuition (what it is, in plain terms)
│ │ │ ├─ A metal rod (the electrode) approaches the metal part, without quite touching it
│ │ │ ├─ Short electrical sparks jump the tiny gap through a liquid (the dielectric), and each spark removes a microscopic crater
│ │ │ ├─ The liquid cools the spot and flushes debris; the machine “feeds” the rod to keep sparking stable rather than shorting
│ │ │ └─ Net effect: you “burn” a hole/slot/cavity into conductive material, especially when cutters would chatter or dull
│ │ ├─ Graduate-level view (physics + control, still resource-lean)
│ │ │ ├─ Material removal is pulsed thermo-plasma erosion: per-pulse energy Eₚ ≈ ∫ V(t)·I(t) dt drives melt/vapor + ejection
│ │ │ ├─ Closed-loop gap control (servo): regulate spark-gap via measured gap voltage/current, preventing sustained arcs and hard shorts
│ │ │ ├─ Stability levers: duty cycle (on/off time), current limit, flushing pressure, debris conductivity, and electrode wear dynamics
│ │ │ └─ Surface integrity lever: trade removal rate vs “white layer”/recast and microcracking via lower pulse energy + better flushing
│ │ ├─ Why “metal-rod-based” EDM is a sweet spot for DIY
│ │ │ ├─ 1-axis plunge/sinker EDM: simplest motion stack (just Z) → holes, pockets, internal corners, broken-tap removal
│ │ │ ├─ Rod or tube electrode options: solid rod for stiffness; hollow tube for flushing and faster deep holes
│ │ │ └─ Electrode materials (choose by wear + machinability): copper, graphite, brass, tungsten, or composite stacks
│ │ ├─ 3-D printer parts you can re-use (cost control through re-parameterization)
│ │ │ ├─ Z-axis mechanics: stepper motor + lead screw + linear rails + anti-backlash nut → precise micro-feed toward the work
│ │ │ ├─ Control electronics: spare 3-D printer mainboard or GRBL-style controller for deterministic motion and limit switches
│ │ │ ├─ Structure: printer gantry/frame becomes the EDM head carriage (add splash shielding rather than rebuilding a mill frame)
│ │ │ └─ Optional spindle: slow rotation of the rod/tube electrode to stabilize debris evacuation and reduce taper in deep holes
│ │ ├─ Dielectric + flushing (cheap choices, but do not skip process hygiene)
│ │ │ ├─ Dielectric fluid: deionized water (clean + cheap) or EDM oil (often better finish, but higher fire/ventilation burden)
│ │ │ ├─ Flushing loop: small pump + nozzle + settling/filter stage; recirculate, because debris conductivity destabilizes sparks
│ │ │ └─ Waste handling: treat slurry as metal-contaminated waste; settle solids and avoid pouring fines into drains
│ │ ├─ When EDM beats “resource-lean CNC” in practice
│ │ │ ├─ Hardened steels, carbides (where applicable), and other difficult-to-cut alloys (reduce tool-cost burn rate)
│ │ │ ├─ Deep, small holes; narrow slots; sharp internal corners; removing broken taps/drills without destroying the parent part
│ │ │ └─ Features on already-heat-treated parts where you want to avoid rework cycles and expensive cutters
│ │ ├─ What it cannot do (and how to make it true by changing the material/system identity)
│ │ │ ├─ Non-conductors: EDM needs electrical conductivity
│ │ │ │ ├─ Make it “EDM-eligible” by adding a sacrificial conductive coating/foil, or by embedding a conductive starter insert
│ │ │ │ └─ If coating changes the part’s function/identity (e.g., dielectric surface), treat it as a temporary process layer and remove it
│ │ │ └─ Large bulk removal: EDM is not a bulk hogging process → rough with LASER-cut sheet/fab, then EDM only the hard features
│ │ ├─ Safety & compliance (do not treat this as benign hobby electronics)
│ │ │ ├─ Electrical: high-energy pulsed power, conductive fluids, and wet environments → isolation, fusing, and grounded enclosures
│ │ │ ├─ Fire/air: oil mist, vapor, and hot particles → ventilation, splash control, and conservative duty cycles
│ │ │ ├─ EMI (electromagnetic interference): pulsed currents radiate → twisted pairs, short leads, shielding, and clean grounding topology
│ │ │ └─ Documentation: record dielectric, pulse settings, and observed stability so the process is reproducible and reviewable
│ │ ├─ Open-source anchor points (start from a community baseline instead of reinventing everything)
│ │ │ ├─ OpenEDM ecosystem – community-driven compact EDM machine efforts (wire and plunge directions)
│ │ │ ├─ Open-source EDM pulse generator/ power-supply modules (use as a reference architecture, even if you redesign)
│ │ │ └─ Maker precedent: “3-D printer as EDM” proof-of-concept builds; useful for validating the concept before refining the process
│ │ └─ File/format pipeline
│ │ ├─ Geometry: define electrode profile and target cavity in CAD; export 2-D/3-D references into the same GitHub repo
│ │ ├─ Motion: treat EDM as a constrained toolpath problem (often 1-axis), log Z vs time + spark telemetry for debugging
│ │ └─ Process notebooks: store “settings recipes” (dielectric conductivity, pulse parameters, feed gains) as versioned YAML/JSON
│ ├─ Tape-based Engineering Solutions
│ │ ├─ Tape-based microfluidics
│ │ │ ├─ Define channels by stacking/laminating double-sided adhesive tape
│ │ │ ├─ Cut geometry with a hobby plotter, laser cutter, or craft-knife
│ │ │ ├─ Use wicking paper or PDMS-coated tape as a capillary membrane
│ │ │ ├─ Simple valves: perforated tape patches or pressure-actuated flap tape
│ │ │ └─ Couple reservoirs via zip-lock pouches or blister packs
│ │ ├─ Tape-based low-cost electronics
│ │ │ ├─ Copper-foil adhesive tape for trace routing (DIY flexible PCB)
│ │ │ ├─ Vinyl or polyester tape substrate – waterproof, foldable, inexpensive
│ │ │ ├─ Mount components with conductive epoxy or solder-less pressure-fit pads
│ │ │ ├─ Rapid redesign by peeling/re-positioning tape segments
│ │ │ └─ Integrate with the same adhesive-tape mechanical frames used elsewhere
│ │ └─ Nano-tape (van-der-Waals adhesion)
│ │ ├─ Gecko-inspired nanostructured adhesive film (graphene, polymer nanoribbons, nanocellulose)
│ │ ├─ Cut with laser/plotter; no glue, can be peeled & re-used many times
│ │ ├─ Shear strength > 10 MPa while thickness < 50 µm → essentially invisible bond line
│ │ ├─ Bonds directly to cardboard, PET, copper-foil tape, and 3-D-printed nodes
│ │ └─ Enables “click-and-release’’ assembly of fluidic/electronic modules without permanent fasteners
│ └─ Nanofabrication & Green Lithography
│ ├─ Water-based photoresists (AZ P4620, S1800, “green’’ SU-8 variants)
│ ├─ Egg-white albumen lithography resist (fully biodegradable & <$0.02/ cm²)
│ │ ├─ Mix fresh egg white (≈30 % protein) with a pinch of glycerol (improved adhesion)
│ │ ├─ Spin-coat 5–10 µm film; soft-bake at 80 °C for 2 min
│ │ ├─ UV expose (365 nm) – dose 50–100 mJ cm⁻² (adjustable via IBM Granite 4-generated scripts)
│ │ ├─ Develop in warm de-ionized water (≈30 °C) – 30 s rinse, no toxic chemicals
│ │ └─ Optional post-exposure bake at 100 °C for 1 min to increase contrast
│ ├─ TMAH-free developers – sodium carbonate, NaOH, citric-acid based
│ ├─ DIY spin-coater – 3-D-printed spinner, Arduino-driven motor, speed-control firmware
│ ├─ Low-cost hot-plate bake oven – repurposed soldering-iron base with PID control
│ ├─ Maskless exposure systems
│ │ ├─ Open-source DLP projector or laser-diode scanner
│ │ └─ Pattern-generation software: OpenLAF/ gLith/ PyLitho (Python)
│ ├─ Open-source GDSII/ OASIS layout tools → KLayout Python API, gdsfactory for photonic circuits
│ ├─ Nano-scale simulation → openEMS or Meep (FDTD) for plasmonic/ photonic structures
│ ├─ Documentation & Knowledge Capture
│ │ ├─ Every resist recipe, bake schedule, and exposure-parameter set is uploaded
│ │ │ • as a Markdown page in the project Wiki (auto-generated via GitHub Actions)
│ │ │ • and mirrored in a collaborative Google Doc SOP for quick lab-member access
│ │ └─ Revision history tracked in Git – each edit creates a new Wiki version
│ ├─ AI-assisted code & recipe generation
│ │ ├─ Open-source, memory-efficient LLMs (IBM Granite 4, Granite 4 Micro, etc.) run on local GPU/CPU
│ │ ├─ Used to draft Python spin-coater scripts, exposure-dose calculators,
│ │ │ and to review safety-check checklists
│ │ └─ LLM outputs are gated through a PEP-8/ MISRA-C linting step before merge
│ ├─ Coding & Hardware Design Standards
│ │ ├─ Python code → PEP-8 + Black formatting; C/C++ firmware → MISRA-C/ C++ Core Guidelines
│ │ ├─ PCB design → IPC-2221 (generic) & IPC-7351 (land-pattern) compliance
│ │ ├─ Symbol & schematic convention → IEC 60617 symbols, IEEE 315 naming
│ │ └─ A “standards-matrix’’ Wiki page lists required checks for every PR
│ └─ Green waste handling – aqueous rinse, biodegradable developer disposal,
│ recycling of mask substrates
│
├─ Plug-and-Play (Agnostic) Design Implementation
│ ├─ Contract tables – list inputs, outputs, units, tolerances, latency
│ ├─ Neutral data formats: CSV, JSON, YAML, DOT (graphs), STL/STEP (geometry)
│ ├─ Standard mechanical interface: 0.1-in pitch board-to-board connectors, snap-fit tabs
│ ├─ Solver-agnostic pipelines – swap GDSTK → openEMS ↔ Blender ↔ ParaView any time
│ └─ Python “dependency-injection’’ pattern to hot-swap back-ends
│
├─ Serviceability & Lifecycle Management
│ ├─ Embedded test points, watchdog timers, LED error codes
│ ├─ Health-check scripts run from Colab notebooks or GitHub Actions CI
│ ├─ Versioned configuration files (JSON/YAML) stored side-by-side with code
│ ├─ Raw experiment data + processed results kept together (Git LFS)
│ └─ Design-for-disassembly: labeled modules, snap-fit hardware, tool-free removal
│
├─ Compact Footprint, Mechanical Foldability & Water Compatibility
│ ├─ Origami-style hinges & flexure joints modelled in Blender → STL → 3-D printed
│ ├─ Water-based operation: sealed compartments, passive heat-pipes
│ ├─ Water-less alternatives: phase-change pads, thermally conductive tapes
│ ├─ Cardboard-compatible layouts: foil-coated paper for moisture barrier
│ ├─ Low-cost Structural Support - Tensegrity Engineering
│ │ ├─ Principle: isolated compression rods + tensioned strings
│ │ │ (e.g., carbon-fiber or bamboo rods + fishing-line/ high-modulus polymer cords)
│ │ ├─ Nodes 3-D-printed from PLA/ABS with integrated thumb-screw or snap-fit holes
│ │ ├─ Parametric geometry generated in Blender; static analysis with Calculix,
│ │ │ OpenSees, or Elmer FEM
│ │ ├─ Advantages – high stiffness-to-weight, deployable, fully reversible assembly
│ │ ├─ Use nano-tape at node-string interfaces for glue-free, repeatable bonding
│ │ ├─ Serves as chassis for electronics, heat-pipes, microfluidic panels, sensor arrays
│ │ └─ Scales from tabletop prototypes to metre-scale structures by adjusting rod
│ │ length and string count
│ ├─ Advanced Low-Cost Stabilization – Compliant Mechanisms
│ │ ├─ Monolithic flexure hinges laser-cut from thin PET, FR-4, or 3-D printed PLA/ABS
│ │ ├─ Bistable snap-through origami cells for zero-power latching or toggle switches
│ │ ├─ Low-cost vibration isolator: alternating layers of silicone sheet & cardstock
│ │ ├─ Design workflow
│ │ │ • Parametric model in Blender or OpenSCAD
│ │ │ • FEM analysis with open-source Calculix/ Elmer (or PyElastica for beam theory)
│ │ │ • optimization loop in Python (SciPy, DEAP) to meet target stiffness/ travel
│ │ │ • visualize deformation in ParaView; store results on S3 via AWS Palace
│ │ ├─ Integration points
│ │ │ • Attach compliant mounts to tensegrity nodes using nano-tape → self-aligning chassis
│ │ │ • Use flexure hinges to hold micro-fluidic chips, reducing stress on fragile bonds
│ │ │ • Provide compliant isolation for sensitive electronics (e.g., accelerometers)
│ │ └─ Materials – PET, thin FR-4, flexible TPU, silicone rubber, laminated cardboard-paper
│ │ composites; all <$0.10/ cm² and compatible with laser-cut or 3-D-print processes
│ ├─ Spot Fresnel lens sunlight capture – low-cost extreme-heat collector
│ │ ├─ Generate Fresnel-zone patterns with the hybrid tetrational-Bezier technique
│ │ ├─ Print/laser-cut zones onto thin PET or acrylic sheets (≤ 1 mm) using inexpensive
│ │ │ desktop plotters or DIY DLP exposure.
│ │ ├─ Assemble a lightweight frame (tensgrity or compliant-mechanism-based)
│ │ │ to hold the lens at the optimal focal distance.
│ │ ├─ Integrate passive heat-pipes or high-thermal-conductivity tapes behind the
│ │ │ focal spot to channel concentrated solar energy into a thermal store
│ │ │ (water-less phase-change pads or low-cost metal-plate absorbers).
│ │ └─ Uses only cardboard, PET, minimal adhesive (nano-tape) – keeping cost < $5/ collector.
│ └─ If folding not feasible → rigid-fold linkages or segmented enclosures
│
├─ Biomimicry & Biomimetics (Nature-derived, resource-lean patterns)
│ ├─ Definitions & heuristics
│ │ ├─ Apply biological principles to technical systems; emphasize function transfer over literal copying.
│ │ └─ Favor low-energy, passive, reversible, and repairable patterns first.
│ ├─ Adhesion & bonding
│ │ ├─ Gecko-inspired dry adhesive films (polymer/ nanocellulose micro-/nanopillars) → reusable, high-shear, glue-free.
│ │ └─ Mussel-inspired catechol chemistries (polydopamine coatings) → wet adhesion on metals, glass, PET; water-based recipes.
│ ├─ Surface textures & wetting
│ │ ├─ Lotus-effect superhydrophobic top-coats (spray-on nano-silica + wax) → self-cleaning moisture barriers for paper/PET.
│ │ ├─ Shark-skin riblets (micro-grooved films) → drag reduction in ducts/ microchannels; laser-etched or 3-D-printed skins.
│ │ └─ Insect-wing bactericidal nanospikes (cicada/dragonfly analogues) → antifouling liners for low-cost microfluidics.
│ ├─ Optical & photonic
│ │ └─ Moth-eye sub-wavelength “nipple” arrays on PET/acrylic → anti-reflection for sensors, solar concentrators, and covers.
│ ├─ Thermal management & ventilation
│ │ └─ Termite-mound-inspired chimney + baffle networks in cardboard enclosures → passive CO₂ flushing & day/night pumping.
│ ├─ Structural mechanics & toughness
│ │ ├─ Nacre-like “brick-and-mortar’’ laminates (paper/epoxy, PLA/glass micro-laminates) → impact tolerance with thin, offset bricks.
│ │ └─ Bamboo/ bone gradients → functionally graded lattices (Voronoi, variable-infill) for stiffness-to-weight optimization.
│ ├─ Fluidics & transport networks
│ │ ├─ Leaf-venation fractal manifolds (Murray-law scaling) → uniform distribution & clog-tolerance in tape-based microfluidics.
│ │ └─ Gill-leaflet check valves → PET flap valves for passive one-way flow without power.
│ ├─ Sensing & actuation
│ │ ├─ Whisker-like flow/tactile sensors (cantilever + piezoresistive track) for airflow or contact detection.
│ │ └─ Super-coiled nylon (fishing-line) muscle actuators → low-cost, heat-driven contraction for valves/ linkages.
│ ├─ Bio-based composites & growth
│ │ ├─ Mycelium-based foams/panels grown in molds → lightweight, biodegradable enclosures and vibration liners.
│ │ └─ Chitosan/cellulose laminates → biodegradable flexible substrates, temporary PCBs, or membranes.
│ └─ Bio-inspired algorithms (zero-cost software levers)
│ ├─ Ant-colony/ bee-swarm heuristics → routing, floorplanning, and pick-list optimization.
│ └─ Slime-mold-style network minimization → duct/ manifold topology with minimal material.
│
└─ Community & DIY Ecosystem (Low Barrier to Entry)
├─ Open-source hardware platforms - Arduino, ESP32, Raspberry Pi Pico
├─ Tutorials & notebooks hosted on GitHub Pages, Google Colab, Jupyter Book
├─ Starter-kit BOM: cardboard sheets, tape, low-cost microcontroller, breadboard,
│ 3-D-printer filament, basic hand tools
├─ Collaborative documentation
│ ├─ Project Wiki (Markdown) for design rationales, standards matrix, resistance recipes
│ ├─ Google Docs shared SOPs for safety, spin-coating, bake-out procedures (real-time editing)
│ ├─ Google Sheets for experiment logs, component inventories, and cost tracking
│ └─ Google Slides for quick design briefs, stakeholder presentations, and tutorial decks
├─ AI-enhanced development workflow
│ ├─ IBM Granite 4 (and open-source variants) run locally to suggest code snippets,
│ │ debug scripts, and auto-generate GDSII/ OASIS layout macros
│ └─ All generated code passes through automated linters that enforce PEP-8/ MISRA-C
│ and are reviewed against IPC/ IEC hardware standards before merge
├─ Contribution workflow: issue templates, pull-request reviews, CI testing
└─ License under MIT/ CERN-OHL to encourage remixing and community-driven extensions
| Abbreviation/ Acronym | Meaning |
|---|---|
| 2D | Two-dimensional |
| 3D | Three-dimensional |
| ABS | Acrylonitrile butadiene styrene (thermoplastic polymer) |
| AEDT | Ansys Electronics Desktop |
| AiiDA | Automated Interactive Infrastructure and Database (workflow/provenance platform) |
| AMI | Amazon Machine Image |
| API | Application Programming Interface |
| ASE | Atomic Simulation Environment |
| AWS | Amazon Web Services |
| BOM | Bill of Materials |
| CAD | Computer-Aided Design |
| CFD | Computational Fluid Dynamics |
| CI | Continuous Integration |
| CSV | Comma-Separated Values |
| DFT | Density-Functional Theory |
| DFTB+ | Density-Functional Tight-Binding Plus |
| DLP | Digital Light Processing |
| DOT | Graphviz DOT language (graph description) |
| EC2 | (Amazon) Elastic Compute Cloud |
| EM | Electromagnetics/ electromagnetic |
| FDTD | Finite-Difference Time-Domain (electromagnetics solver method) |
| FEM | Finite Element Method |
| FR-4 | Flame-Retardant 4 (glass-epoxy laminate for PCBs) |
| GDSII | Graphic Design System II (IC layout file format) |
| GDSTK | GDSII/ OASIS Tool Kit (geometry/GDSII/OASIS Python/C++ library) |
| Git LFS | Git Large File Storage |
| GPAW | Grid-based Projector Augmented-Wave (DFT code) |
| GPR | Ground Penetrating Radar |
| GROMACS | GROningen MAchine for Chemical Simulations |
| GPU | Graphics Processing Unit |
| HPC | High-Performance Computing |
| IBM | International Business Machines |
| IEC 60617 | International Electrotechnical Commission 60617 (graphical symbols for diagrams) |
| IEEE 315 | IEEE Std 315 (graphic symbols for electrical/electronics diagrams) |
| IPC-2221 | IPC Generic Standard for Printed Board Design |
| IPC-7351 | IPC Standard for PCB land-pattern (footprint) design |
| JSON | JavaScript Object Notation |
| LAMMPS | Large-scale Atomic/Molecular Massively Parallel Simulator |
| LED | Light-Emitting Diode |
| LLM | Large Language Model |
| MD | Molecular Dynamics |
| MEEP | MIT Electromagnetic Equation Propagation (FDTD solver) |
| MISRA-C | Motor Industry Software Reliability Association – C coding guidelines |
| MMTB | Multiscale Modeling Toolbox |
| MW | Microwave (portion of RF spectrum) |
| NURBS | Non-Uniform Rational B-Splines |
| OASIS | Open Artwork System Interchange Standard (IC layout format) |
| OOMMF | Object Oriented MicroMagnetic Framework |
| OMF | OOMMF magnetization file (file extension commonly used with OVF data) |
| OpenFOAM | Open Field Operation And Manipulation (CFD framework) |
| OVF | OOMMF Vector Field (file format) |
| PALACE | PArallel LArge-scale Computational Electromagnetics (open-source FEM EM solver by AWS) |
| PCB | Printed Circuit Board |
| PDMS | Polydimethylsiloxane |
| PET | Polyethylene terephthalate |
| PETSc | Portable, Extensible Toolkit for Scientific Computation |
| PID | Proportional–Integral–Derivative (control loop) |
| PLA | Polylactic acid (polylactide) |
| PR | Pull Request (version-control collaboration) |
| PyAEDT | Python API for Ansys Electronics Desktop |
| Qucs-S | Qucs with SPICE (SPICE-enabled Qucs circuit simulator) |
| RF | Radio Frequency |
| S3 | (Amazon) Simple Storage Service |
| SIESTA | Spanish Initiative for Electronic Simulations with Thousands of Atoms |
| SOP | Standard Operating Procedure |
| SPICE | Simulation Program with Integrated Circuit Emphasis |
| STEP | Standard for the Exchange of Product model data (ISO 10303) |
| STL | StereoLithography (triangulated mesh file format) |
| SU-8 | Negative epoxy photoresist (SU-8 family) |
| TMAH | Tetramethylammonium hydroxide |
| TPU | Thermoplastic polyurethane |
| UV | Ultraviolet |
| VTK | Visualization Toolkit |