Geometry-Normalized Model for Bushing Selection

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Abstract

Downhill longboarding traditionally selects bushing durometer based on rider weight. However, field observation and comparative testing across truck platforms suggest that rider mass is a weak primary predictor of optimal bushing hardness. This paper proposes a geometry-normalized model for first-pass bushing selection using three accessible inputs: truck width, baseplate angle, and bushing seat confinement.

Using seatless reverse kingpin trucks as a mechanical baseline, we demonstrate that equivalent restoring torque can be approximated across geometries by adjusting durometer according to leverage and constraint effects. The resulting framework provides a predictive starting point for bushing selection independent of rider weight, with structured modifications for extreme cases of rider mass or strength.

The Failure of Weight-Based Selection

The weight heuristic assumes that bushings primarily resist vertical load, and that rider mass is the dominant variable influencing restoring torque and stability.

Rider weight influences static preload, but this essay proposes that restoring torque during dynamic lean is primarily a function inclusive of:

• Lever arm length (hanger width)
• Baseplate angle (mechanical leverage over bushing)
• Bushing seat confinement (constraint stiffness)
• Bushing hardness (durometer)

Two riders of identical weight on a 120 mm 50° truck and a 180 mm 48° truck will require dramatically different durometers, despite identical mass. Conversely, two riders of very different mass on identical geometries often converge toward similar hardnesses once geometry is controlled.

Weight is a modifier, whereas geometry is the primary system.

Establishing the Mechanical Baseline: Seatless Trucks

To normalize across truck designs, we require a mechanical reference case. The cleanest baseline is a truck with:

• No bushing seat
• Minimal lateral confinement
• No artificial hanger restraint

The canonical example is the Rogue platform:

• 48° symmetrical cast, 183 mm
• 50°/20° split precision, 120 mm

These configurations provide stable and repeatable reference points:

Geometry	Proven Setup
48° symmetrical, 183 mm	87a / 87a
50° front, 120 mm	73a / 78a
20° rear, 120 mm	95a / 97a

Because these trucks lack seat confinement, their bushings behave close to ideal elastomer columns. This makes them appropriate as a normalization reference.

Core Mechanical Variables

We reduce the system to three first-order inputs.

Width (W)

Width determines lever arm length.

• Wider truck → larger moment arm → greater torque for same lean
• Therefore, wider truck → requires harder bushings

Empirically, for seatless trucks at 50°:

• 120 mm → ~75a average
• 180 mm → ~87a average

This implies roughly ~0.20a per mm change in width from 120 mm baseline.

Baseplate Angle (θ)

Angle determines how lean translates into bushing compression.

Higher angle:

• More steer per lean
• Greater mechanical advantage
• Requires softer bushings

Lower angle:

• Reduced steer
• Requires harder bushings to maintain equivalent return force

Empirically, at 120 mm:

• 50° front → ~75a average
• 20° rear → ~96a average

Across a 30° delta, we observe ~21a change.

This implies ~0.70a per degree change from 50° baseline.

Bushing Seat Oppressiveness (S)

Seat confinement increases effective stiffness without changing durometer.

We introduce a dummy-coded seat factor:

S	Seat Type
0	No seat (Rogue)
1	Light seat (Paris V2 cast)
2	Moderate seat (Caliber V2 cast)
3	Heavy seat (Lite Rey)
4	Extreme seat (Zealous V1 class)

Empirically:

• Moderate seats reduce required durometer by ~2a
• Very restrictive seats reduce by ~4a

We approximate seat adjustment ≈ −1a per seat level. This is conservative and keeps the model stable.

The First-Estimate Equation

Let:

• W = truck width in mm
• θ = baseplate angle in degrees
• S = seat factor (0–4)

We define a baseline:

• W₀ = 120 mm
• θ₀ = 50°
• D₀ = 75a (seatless, 120 mm, 50° average reference)

Then:

Where:

• D = predicted average durometer (boardside/roadside average)

This gives a first-pass estimate.

Model Validation Against Known Good Setups

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Case 1: Gold Rogue 50/20, 120 mm, No Seat (S=0)

Front: 50°, S=0$$D = 75 + 0 – 0 – 0 = 75a$$Matches 73/75 front average ≈ 74a.

Rear: 20°, S=0$$D = 75 + 0 – 0.70(-30) = 75 + 21 = 96a$$Matches 95/97 rear average ≈ 96a. Strong agreement.

Case 2: Zealous V1 51/19, 120 mm, Extreme seat (S=4)

Front: 51°$$D = 75 – 0.70(1) – 4 = 75 – 0.7 – 4 = 70.3a$$Empirical: 73/73 front.

Model slightly underestimates due to extreme seat constraint reducing deformation efficiency. Within usable tolerance.

Rear: 19°$$D = 75 + 0.70(31) – 4 ≈ 75 + 21.7 – 4 = 92.7a$$Empirical: 93/95. Again strong agreement.

Case 3: 180 mm 50° Paris CAST V2, Light Seat (S=1)

$$D = 75 + 0.20(60) – 0 – 1$$$$D = 75 + 12 – 1 = 86a$$Empirical: ~85–87a common. Correct band.

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Symmetrical RKP Instability and Rear Damping

The model above assumes a slalom-style geometry where the axle trails the rotational center.

In traditional symmetrical RKP setups:

• The rear truck is an inverted front
• The rear axle leads the rotational center

This creates a backward caster effect. Like a shopping cart wheel in reverse, it is dynamically unstable.

This geometric instability is a root contributor to speed wobbles.

In this configuration, bushings serve two roles:

1. Articulation control
2. Instability damping via restoring moment

Therefore:

• Rear bushings in symmetrical setups may need to exceed predicted hardness slightly
• Or require more boardside bias

Weight and Preload Adjustments

Weight is not the primary predictor, but it modifies preload.

In unconstrained trucks:

• Hanger compresses boardside bushing statically
• Heavier rider → pre-squish

To compensate:

• Increase boardside hardness
• Decrease roadside hardness
• Maintain same average

Example:

Theoretical rear: 95a/95a
Heavy rider adjustment: 97a/93a

This preserves average stiffness while restoring dynamic symmetry.

Exception cases:

• Ronin queenpin system
• Liquid Fyre hanger bar systems

These restrain vertical hanger movement and reduce or eliminate preload effects.

Secondary Variables (Second-Pass Refinement)

The following factors are intentionally excluded from the first-order model:

• Bushing shape (cone vs barrel vs stepped)
• Rebound characteristic
• Washer type
• Rider strength and response speed
• Rake

These are meaningful, but not beginner-accessible variables. They belong in refinement passes.

Conclusion

Bushing selection is not fundamentally a weight problem. It is a leverage and constraint problem.

By anchoring the system to a seatless reference geometry and scaling by width, angle, and confinement, we obtain a predictive first-pass model that aligns closely with proven high-performance setups. Rider weight becomes a modifier, not the governing variable.

Notes and assumptions

• My understanding of bushing hardness (durometer) is based empirically on a decade of skating Venom HPF barrels. Other bushing brands and models may skate softer or harder depending on formula, but only by an amount of 2a or so.
• It’s true- rake isn’t a part of the model. I see rake as an input variable for truck choice, but not for bushing choice. This model seeks to estimate what bushings you need in your trucks for them to feel “ideally articulated”. Rake is an input variable in deciding how your trucks skate in movement; and this would dictate your choice of trucks, not your choice in bushings.