The predictive quality platform for manufacturing
MoldSight combines sensor fusion, edge AI, and statistical process control to give manufacturing teams real-time defect prediction and quality drift detection across injection molding, die casting, and CNC operations.
Real-Time Defect Prediction
MoldSight ingests cavity pressure curves, melt temperature profiles, injection speed, hold pressure, and cooling data on every shot cycle. Machine learning models trained on your specific tooling and material combinations score each part in under 200ms. Operators see pass/fail results on the line display before the mold opens, enabling immediate divert or hold decisions.
- Injection molding operators divert suspect parts before they reach assembly
- Die casting cells flag porosity risk based on fill pressure anomalies
- CNC operations detect tool chatter from vibration signatures mid-cut
Quality Drift Detection
Traditional SPC catches problems after they've already produced scrap. MoldSight combines statistical control charts with ML-based trend detection to identify gradual process drift — tool wear, material lot variation, ambient temperature shifts — and alerts operators before yield degrades. Drift alerts include severity, affected parameters, and recommended corrective actions.
- Quality managers receive early warnings when cavity pressure trends shift outside normal bands
- Maintenance teams schedule tool changes based on predicted wear rather than fixed intervals
- Process engineers correlate material lot changes with quality shifts across production runs
Root Cause Analysis
When defect patterns emerge, MoldSight traces them back to specific process parameters using feature importance analysis and correlation mapping. The system identifies which cavity, which sensor channel, which time window, and which operating condition contributed most to the quality deviation. Engineers get a ranked list of probable causes with supporting data, not just a red alert.
- Process engineers diagnose short shot causes by correlating fill pressure with gate temperature
- Quality teams trace flash defects to specific cavity-level pressure imbalances
- Plant managers review cross-shift quality variance with parameter-level attribution
Edge Deployment & Fleet Management
MoldSight runs inference on ruggedized edge hardware installed at each machine or cell. Production never depends on cloud connectivity. Sensor data and predictions sync to the cloud when bandwidth allows, enabling fleet-wide analytics, model retraining, and cross-plant benchmarking without risking production uptime.
- Plants in low-connectivity environments run full prediction without internet dependency
- Corporate quality teams benchmark defect rates across plants using cloud-synced analytics
- Data science teams retrain models centrally and push updates to edge devices fleet-wide
Built for precision manufacturing
For Injection Molding
Predict short shots, flash, sink marks, and warpage from cavity pressure and temperature data on every cycle. Reduce scrap and eliminate end-of-line surprises.
For Die Casting
Detect porosity risk, cold shuts, and fill imbalances using plunger pressure curves and die temperature profiles. Catch quality issues before X-ray inspection.
For CNC Machining
Monitor tool wear, chatter, and dimensional drift from spindle load, vibration, and positional data. Predict tool changes before tolerance exceedances.
Connects to your existing stack
Ready to predict quality, not just inspect it?
Join manufacturing teams using MoldSight to reduce scrap, catch drift early, and bring data-driven quality control to the production floor.