JSCAD Tutorial: Parametric 3D Design with Code

Start With a Simple Functional Part

Begin with a plate that has two holes. This gives enough complexity to demonstrate parameter control while staying easy to debug.

Define width, height, thickness, hole diameter, and hole spacing as parameters from the start.

Use Functions for Reuse

Encapsulate repetitive geometry in helper functions. A clean function structure makes scripts easier to maintain and share.

Readable naming and small functions are more important than clever one-liners in parametric CAD scripts.

Build With Boolean Logic

Create base body first, then subtract holes and slots. This keeps intent clear and reduces debugging complexity.

If geometry fails, inspect one operation at a time instead of changing the whole script.

Expose Useful Parameters Only

Good parametric design is not exposing every number. Expose only what end users actually change.

A focused parameter set makes shared templates easier for beginners and non-coders.

Export and Validate

After rendering, export STL and run a quick slicer preview check. Confirm wall thickness and fit-critical dimensions before print.

Parametric scripts become powerful when combined with disciplined validation per variant.

How to Apply This in a Real CADFaber Workflow

Parametric code workflows create reusable design systems instead of one-off geometry edits. In CADFaber, the practical target is a reusable script that can generate multiple validated variants. Start with a small model, verify that the geometry exports cleanly, then increase complexity only after the first result works in your slicer or downstream tool.

A useful first step is to convert one manual model into a parameterized script with three tested variants. This gives you a known-good baseline before you change dimensions, add decorative detail, or create several variants. Keep the original project and exported file together so you can compare later iterations instead of guessing what changed.

Pre-Production Checklist

Run this short checklist before you invest time in final modeling. It is intentionally practical: each item should reduce one common print, export, or workflow failure.

• Define which dimensions should be parameters.
• Add constraints and safe min/max ranges.
• Separate geometry helpers into clear functions.
• Test script with edge-case parameter sets.
• Export and slice at least two variants.

Validation and Quality Checks

Set safe parameter limits, then test min, max, and typical values before publishing. Use the same checks whenever you revise the model so the comparison stays fair. If a later version fails, you can return to the last successful export and isolate the exact change that caused trouble.

Track only a few metrics at first. Clear measurements beat vague impressions, especially when you compare tools, formats, tolerances, or repeated design variants.

• Variants generated per script.
• Average time saved versus manual remodeling.
• Failure rate at parameter boundaries.
• Reusability score across project types.

Common Mistakes to Avoid

The most common failure here is writing code without defining parameter boundaries and validation assumptions. Fix it with smaller iterations, explicit assumptions, and a repeatable export check instead of changing several variables at once.

• Mistake: exposing every internal variable. Fix: expose only user-meaningful controls.
• Mistake: skipping edge-case tests. Fix: validate minimum and maximum values.
• Mistake: blending logic and constants. Fix: centralize configurable parameters.
• Mistake: no documentation for future use. Fix: add inline comments and usage notes.

Scaling the Workflow

Teams can standardize frequently used parts by sharing script templates and parameter presets. When you share the workflow, include the CADFaber project file, export format, slicer assumptions, and the reason behind important dimensions. That makes the result easier to audit and reuse.

7-Day Implementation Plan

Execution beats intention. Use a one-week plan with small daily outcomes instead of waiting for a perfect long session. This keeps momentum high and gives you measurable progress that compounds over time. By the end of one week, you should have both a working result and a repeatable method you can reuse for the next project.

Keep this plan lightweight and realistic. Consistency matters more than intensity. If you complete the daily steps below, you will create a durable workflow advantage that translates directly into better output quality and faster iteration speed.

• Day 1: Select one repetitive model to parameterize.
• Day 2: Build first script version.
• Day 3: Add parameter definitions and defaults.
• Day 4: Test boundary values and fix failures.
• Day 5: Export validated variants.
• Day 6: Write usage notes for others.
• Day 7: Publish template and collect feedback.