How to Design Nesting Schemes to Improve Material Utilization

I. Core Principles

1. GeometrIC Nesting Optimization

   • Shape Complementarity: Interlock curved parts with straight-edged parts (e.g., gear embedded in rectangular slot).

   • Rotation Matching: Allow part rotation (0°/90°/180°) (Example: L-shaped part rotation reduces gaps by 12%).

2. Dynamic Boundary Control

   • Set sheet edge margins (standard: 5mm), internal part spacing ≥ 2×keRF width (typically 3-5mm).

II. Key Technical Steps

1. Data Preprocessing

   # Python example: Part sorting algorithm    parts = sorted(part_list, key=lambda x: max(x.length, x.width), reverse=True)  

2. Intelligent Nesting Algorithms

   • Greedy Strategy: Prioritize largest 10% parts by area, boosting utilization by 8-15%.

   • Genetic Algorithm: 200+ iterations improve global utilization by 5-8%.

3. Embedding Process Constraints

   Constraint Type	Parameter Range	Utilization Impact
   Grain Direction	0° or 90°	±3%
   Min. Hole Spacing	≥5mm	-1.2%

III. Case Study Comparison

Automotive Sheet Metal Production Data:

IV. Common Pitfalls & Solutions

1. Misconception: Ignoring material grain direction

   • Solution: Add weighting function to algorithm:
     cost = α*(area loss) + β*(grain deviation)

2. Hidden Losses:

   • Laser cutting thermal compensation: +0.1mm allowance for stainless steel

V. Recommended Efficiency Tools

1. Professional Software

   • Sheet Metal: Radan (common-edge cutting saves 7% material)

   • Apparel: Lectra (AI-driven wrinkle avoidance)

2. Self-Developed Toolchain

Material Utilization Formula:
$\eta =\frac{{\sum }_{i=1}^n{S}_i}{S_0}\times 100\mathrm{\%}$
Where:

• S_i = Net area of single part

• S₀ = Raw sheet area

• n = Total nested parts