What are the specific steps to optimize the process parameters of injection molding machines?

Optimizing the process parameters of the injection molding machine should follow the principle of "goal orientation, step-by-step verification, and defect feedback", and make systematic adjustments based on material characteristics, equipment status, and product requirements. The following are the specific steps:
1. Preliminary preparation: clarify goals and basic inspections
Define optimization goals
Core demand classification:
▶ Quality orientation: solve defects such as short shots, flash, shrinkage, and weld marks, and improve dimensional accuracy (such as tolerance ±0.1%).
▶ Efficiency orientation: shorten cycle time (target ≤ 80% of the original cycle), increase production capacity (such as shift production increase by 10%).
▶ Cost orientation: reduce material consumption (scrap rate ≤ 1%) and energy consumption (power consumption reduced by 5%).
Confirmation of basic conditions
Equipment status:
▶ Check screw wear (screw clearance ＞0.5mm needs to be replaced), heating ring/thermocouple failure (temperature fluctuation ＞±10℃ needs to be calibrated).
▶ Test the clamping force accuracy (actual pressure and set value deviation ≤5%), and clean the mold cooling water circuit (flow ≥5L/min, temperature difference ≤3℃).
Raw material preparation:
▶ Confirm that the raw material is dry enough (e.g. PC moisture content <0.02%, use dew point meter to test), check the brand (PP with different fluidity needs to adjust injection speed).
Mold inspection:
▶ Clean the exhaust groove (depth 0.02-0.05mm), check the gate wear (diameter deviation >10% needs to be repaired).
II. Core parameter setting: from basic to refined adjustment
Step 1: Set temperature reference (barrel/mold/nozzle)
Barrel temperature
Set the initial value according to the melting point of the raw material (e.g. ABS 220-250℃), and increase it in stages (feeding section → compression section → metering section → nozzle, each section + 5-10℃).
Verification method: Manual injection to observe the melt state, the ideal state is continuous and smooth, without bubbles/particles; if the melt is yellow, reduce the metering section temperature by 5-10℃; if the material is fed slowly, increase the feeding section temperature by 10-15℃.
Mold temperature
The mold temperature of crystallizing materials (such as PA) is set to 60-90℃ (to increase crystallinity), and that of non-crystallizing materials (such as PS) is set to 40-60℃ (to speed up cooling).
Measurement calibration: Use an infrared thermometer to detect the mold surface to ensure that the cavity temperature difference is ≤5℃. Clean or replace the sealing ring when the water channel is blocked.
Step 2: Pressure and speed segmented debugging
Injection pressure
Initial pressure = empirical value (120-150MPa for thin-walled products, 80-100MPa for thick-walled products), use the "lack of material method" test:
▶ Gradually reduce the pressure until a short shot occurs, and take this value + 10% as the minimum effective pressure to avoid excessive pressure (＞150MPa is prone to flash and internal stress).
Injection speed
Complex structure / thin-walled products: Use high-speed section (80-100mm/s) to fill and avoid premature solidification of the melt;
Thick-walled / products prone to weld marks: Use low-speed section (30-50mm/s) to fill and reduce turbulence, and the speed at the gate can be increased by 20%.
Step 3: Time parameter optimization (holding pressure, cooling, cycle)
Holding pressure and time
Holding pressure = 60%-80% of injection pressure (such as injection pressure 120MPa, holding pressure is set to 70-90MPa), and the best time is determined by the "weight method":
▶ Shorten the holding time by 5s each time until the weight of the product no longer decreases (error ≤0.5%), at which time time + 2s is the critical holding time.
Cooling time
Formula estimation: Cooling time = product wall thickness ²×(4-6) (unit s, applicable to ABS), the actual standard is that the product does not deform after ejection (no dent when pressed by hand).
Limit test: gradually shorten the cooling time until the edge of the product turns white when ejected. This time + 3s is the minimum cooling time.
Step 4: Screw parameter adaptation
Speed ​​and back pressure
General speed 50-80rpm, heat-sensitive materials (such as PVC) reduced to 30-50rpm; back pressure 5-15MPa, materials that need to be vented (such as recycled materials) increased to 10-20MPa (reduce bubbles).
Abnormal handling: Screw slip (low feed section temperature) → increase feed section temperature by 10℃; melt discoloration (back pressure is too high) → reduce back pressure by 5MPa.
Metering and injection stroke
Metering stroke = product + flow channel volume × 1.1 (retain 10%-20% buffer), injection stroke = metering stroke - buffer (avoid empty shot to wear the screw).
3. Defect-oriented parameter fine-tuning and verification
Establish "Defect-Adjustment Comparison Table"
Defect Priority adjustment parameters Adjustment range Verification method
Short shot Injection pressure/speed, barrel temperature Pressure + 10%, speed + 20% Continuous production 3 molds Observe filling conditions
Flash Clamping force, injection pressure Clamping force + 5%, pressure - 10% Measure flash thickness (target ≤ 0.05mm)
Shrinkage Holding pressure/time, mold temperature Holding pressure + 15%, mold temperature + 10℃ Product weighing (weight fluctuation ≤ 1%)
Weld mark Mold temperature, injection speed, gate position Mold temperature + 5℃, speed + 15% Visually check weld mark depth (≤ 0.1mm)
Single factor test method
Adjust only one parameter each time (such as temperature ±5℃, pressure ±5%), produce 5-10 products, record defect changes and process data (such as cycle time, energy consumption), and compare with the benchmark parts to confirm the optimization effect.
4. Application of systematic optimization tools
Standardization of mold test reports
Record initial parameters, adjustment process, product inspection data (size, weight, defect level), and form a "Process Parameter File" to facilitate quick call of the same mold/material (such as reserving a 10% safety margin when copying parameters).
Moldflow pre-optimization
Simulate filling pressure and cooling time in the design stage, and output recommended parameters (such as when the pressure peak caused by the gate position is greater than 180MPa, it is recommended to increase the wall thickness by 0.2mm).
SPC process control
Continuously sample 25 groups of key dimensions (such as product inner diameter φ50±0.1mm), calculate CPK value (target ≥1.33), and trigger parameter warning when exceeding the limit (such as automatic calibration when temperature fluctuation is greater than ±3℃).
5. Continuous improvement and standardization
Parameter solidification and error prevention
The optimized parameters are stored in the injection molding machine formula library, password protection is set (to prevent accidental modification), and key parameters (such as temperature and pressure) enable over-limit alarm (stop when deviation is greater than 5%).
Periodic review
Analyze scrap rate and energy consumption data weekly, and compare standard parameter fluctuations (such as checking cooling water circuits when cycle time is extended by >5%); calibrate mold temperature sensors monthly (replace when error >±2℃).
Personnel training
Train operators to master the "3-3-3 rule": adjust no more than 3 parameters each time, adjust no more than 30% of each parameter, and observe at least 3 molds after adjustment to avoid blind trial and error.
VI. Precautions
Material differences: New materials and recycled materials (such as recycled PC containing impurities) need to increase barrel temperature by 5-10℃, and reduce injection speed by 10% to prevent degradation.
Equipment aging: For equipment used for more than 5 years, the screw shearing efficiency decreases, and the speed needs to be increased by 10% or the back pressure needs to be compensated by 5MPa.
Environmental control: When the workshop temperature drops sharply (such as from 25℃ to 15℃), the mold temperature needs to be increased by 5-8℃ to maintain cooling balance.
Through the above steps, the transition from "experience-based mold testing" to "data-driven optimization" can be achieved. Typical cases show that after reasonable adjustments, the defective rate of products can be reduced by 40%-60%, the cycle time can be shortened by 15%-25%, and the energy consumption can be reduced by 10%-15%. The key is to establish a standardized process, combined with real-time data feedback, to form a closed-loop optimization system of "setting - verification - curing - iteration".