Top ten common defects of plastic parts, each one is more serious than the other
Time：2025-05-17 14:10:42 / Popularity： / Source：
Following are top ten common defects of plastic parts and their systematic solutions, including formation mechanism, key parameter optimization and preventive measures:
1. Short Shot
| Influencing factors | Quantitative parameter range | Improvement plan |
| Insufficient melt fluidity | Melt flow index needs to be increased by ≥30% | Switch to high-fluidity materials (such as PP-M30 instead of PP-H30) |
| Insufficient injection pressure | Increase pressure to 120-150MPa | Check for hydraulic system leakage and calibrate pressure sensor error (need ≤±1%) |
| Mold temperature is too low | Mold temperature is increased to 80-110℃ | Add a fast response module to mold temperature controller (heating rate ≥5℃/min) |
| Insufficient gate size | Gate thickness needs to be ≥60% of wall thickness | Use hot runner System, gate diameter expanded to φ1.2-2.5mm |
II. Flash
| Root cause | Critical control point | Engineering and technical countermeasures |
| Insufficient clamping force | Clamping force needs to be ≥ injection pressure × 1.2 | Change to elbow clamping mechanism (rigidity increased by 30%) |
| Mold wear | Parting surface gap ≤ 0.02mm | Laser cladding repair, surface hardness restored to HRC52-54 |
| Material viscosity is too low | Melting index controlled at 20-50g/10min | Add 0.3-0.5% viscosity enhancer (such as PTFE powder) |
III. Gas bubbles (Gas Traps）
| Bubble type | Solution |
| Water vapor bubbles | Material drying: - Engineering plastics (PC/PA) 120℃×4h, dew point ≤-30℃ - Ordinary plastics (ABS) 80℃×3h |
| Decompose gas bubbles | Lower melt temperature: PC from 320℃→290℃, screw shear heat control ≤30℃ |
| Poor exhaust | Add exhaust groove: Depth 0.03-0.05mm, width 5-8mm, spacing ≤50mm |
IV. Sink Mark
| Position | Optimization method |
| Thickness area | Pressing curve optimization: Three-stage pressure holding (80%→60%→40%), total pressure holding time = wall thickness (mm)×8s |
| Strengthening rib root | Design improvement: Rib thickness ≤50% of main wall thickness, root R angle ≥0.5T (T is wall thickness) |
| Crystallization material | Mold temperature control: PP mold needs to be maintained at 60-80℃ to reduce crystallization shrinkage differences |
V. Weld mark (Weld Line)
| Parameters | Optimization range | Special process |
| Melt temperature | Increase 10-20℃ | Set local heating in weld line area (300W electromagnetic induction heating, heating to 200℃) |
| Injection speed | Increase to 90-95% of maximum speed | Use sequential valve gate control (opening time difference of each gate ≤0.1s) |
| Venting | Venting groove depth in weld zone is 0.03mm | Use porous alloy inserts (porosity 30%, pore size 15μm) |
VI. Black spot (Black Speck)
| Pollution source | Detection method | Removal plan |
| Material degradation | TGA analysis (weight loss temperature range) | Screw cleaning: Use PP transition material to clean 3 times, each time amount is 2 times barrel volume |
| External pollution | Infrared spectrum comparison | Workshop cleanliness is improved to ISO 7 level (particle size ≥0.5μm particles ≤35200/m³) |
| Mold corrosion | EDS element analysis | Surface chrome plating (thickness 15-20μm) |
VII. Stripes (Flow Mark）
| Stripe type | Forming mechanism | Solution |
| Uneven dispersion of color powder | Deviation of masterbatch concentration＞5% | Use twin screw compounding (L/D≥40:1), and increase temperature of mixing section by 20℃ |
| Shear stripes | Shear rate＞50000s⁻¹ | Enlarge gate size (thickness increased by 0.2mm), and use a trumpet-shaped gate |
| Cold material stripes | Insufficient cold material well capacity | Cold material well volume must be ≥150% of main channel volume |
VIII. Warpage
| Deformation direction | Control strategy |
| Uneven shrinkage | Mold temperature gradient control: Moving and fixed mold temperature difference ≤15℃, water channel spacing ≤25mm |
| Molecular orientation | Press holding pressure optimization: Press holding pressure = injection pressure 80%, press holding time = wall thickness (mm)×5s |
| Residual stress | Annealing treatment: Heat treatment at 20℃ below Tg temperature (such as ABS 80℃×2h) |
IX. Delamination
| Delamination type | Solution |
| Material incompatibility | Compatibilizer addition: PP/PA system adds 2-3% PP-g-MAH |
| Over-lubrication | Release agent concentration control: Silicone oil ≤0.01%, water-based solid content ≤1% |
| Shear delamination | Reduce injection speed: Speed is divided into 3 sections (slow-fast-slow), peak speed ≤80% maximum speed |
X. Peeling (Surface Peel)
| Triggering conditions | Improvement measures |
| Poor material mixing | Back pressure increased to 15-20MPa, screw speed reduced to 50-70rpm |
| Mold temperature is too low | Rapid heating: Use pulse mold temperature control (reach set temperature within 5min) |
| Surface crystallinity difference | Use rapid cooling treatment for materials such as PC (mold temperature drop rate ≥30℃/min) |
Defect diagnosis and parameter optimization tools
1. Mold flow analysis verification
Filling time difference controlled within ±0.2s
Volume shrinkage ≤5% (crystalline material ≤7%)
Volume shrinkage ≤5% (crystalline material ≤7%)
2. Process window index (PWI)
Qualification standard: PWI≤0.8
Allowable deviation
Actual parameters − optimal parameters
Allowable deviation
Actual parameters − optimal parameters
3. Online monitoring system
Install pressure sensor (accuracy ±0.5MPa)
Infrared thermal imager monitors mold temperature (resolution 0.1℃)
Advanced prevention solution
Infrared thermal imager monitors mold temperature (resolution 0.1℃)
Advanced prevention solution
1. Material pretreatment system
Vacuum dryer (vacuum degree ≤10kPa)
Automatic mixing system (metering error ≤±0.3%)
Automatic mixing system (metering error ≤±0.3%)
2. Intelligent mold technology
Variable mold temperature control (±1℃ dynamic adjustment)
Self-repairing coating (scratch depth ≤5μm automatic repair)
Self-repairing coating (scratch depth ≤5μm automatic repair)
3. Big data analysis
Establish defect database (≥100,000 mold data)
Apply machine learning to predict defects (accuracy ≥85%)
Through quantitative parameter control and systematic solutions, defect rate can be reduced to ≤3000PPM (Six Sigma level). It is recommended that enterprises establish an SPC control system to monitor key parameters (melt temperature, mold temperature, holding pressure) in real time to ensure process stability.
Apply machine learning to predict defects (accuracy ≥85%)
Through quantitative parameter control and systematic solutions, defect rate can be reduced to ≤3000PPM (Six Sigma level). It is recommended that enterprises establish an SPC control system to monitor key parameters (melt temperature, mold temperature, holding pressure) in real time to ensure process stability.
Recommended
Related
- How can I fix bubbles in my injection molded parts?12-05
- Analysis of 21 Typical Injection Molding Defects (Part 2)12-04
- Detailed explanation of injection molding problems of plug products12-03
- Analysis of 21 Typical Injection Molding Defects (Part 1)12-02
- Die-casting process optimization of truck fuel tank bracket12-01