The same Sense, Learn, Act methodology applied to three laser manufacturing domains. Select a domain below to see how our monitoring and inspection capabilities apply to your process.
We combine multi-modal sensing with domain-adapted ML to detect process anomalies in real time, predict feature quality from in-situ signals, and inspect machined surfaces for defects and dimensional conformance before parts leave the machine.
Multi-modal sensors observe laser material removal continuously. ML models classify process state and flag anomalies in milliseconds, enabling closed-loop correction before quality is compromised.
Models learn the correct process signature and flag deviations without needing labelled fault data. Explainable AI diagnostics identify which signal is driving the alert and why.
ML regression models map laser process signals to feature quality outcomes, predicting geometry, edge quality, and surface condition without measuring every part. Bayesian optimisation suggests parameter improvements.
AI imaging evaluates machined surfaces automatically, detecting defects, measuring feature geometry, and characterising surface roughness. Every part inspected at production speed.
Laser material removal always leaves a surface whose roughness, texture, and integrity directly affect part performance. Whether drilling turbine blades, scribing wafers, or ablating implant surfaces, the resulting surface condition matters as much as dimensional accuracy.
Our AI inspection systems characterise surface roughness and texture from imaging data automatically, flagging surfaces outside specification without slow tactile measurement on every part.
Quantified from imaging data without contact measurement. Flagged automatically against process-specific thresholds.
Cracks, recast layer, burrs, and incomplete ablation detected and categorised by deep learning models trained on domain-specific defect libraries.
Feature geometry measured and compared to nominal, with deviation maps generated automatically for every part.
Our systems track energy coupling, thermal signatures, and surface state during laser treatment in real time, and characterise the resulting surface quality, roughness, and microstructural condition afterwards.
Multi-modal sensors track energy coupling, plasma emission, and thermal response during laser surface treatment. ML models detect process deviations in real time before they affect surface integrity.
Unsupervised models learn the correct treatment signature and flag deviations without requiring labelled fault data. Root cause diagnostics identify whether anomalies stem from beam quality, contamination, or parameter drift.
ML models map in-process signals to treatment outcomes, predicting hardness depth and surface condition without destructive testing. Bayesian optimisation suggests parameter improvements actively.
AI imaging evaluates treated surfaces automatically for roughness, texture uniformity, oxidation, and defects. Every surface characterised at production speed without slow tactile measurement.
In laser surface treatment the process goal is always a specific surface state, whether that is a hardened layer, a clean oxide-free surface, a compressively stressed zone, or a specific roughness profile. Monitoring the process alone is not enough if you cannot verify the outcome.
Our post-treatment inspection systems close that loop, characterising surface condition automatically and feeding the result back into quality records and optimisation models.
Ra, Rz, and surface texture parameters quantified from imaging data. Flagged against specification limits automatically.
Spectral and imaging analysis detects oxidation, residual contamination, and surface chemistry changes post-treatment.
Spatial maps of treatment coverage and uniformity generated automatically, identifying under or over-treated zones.
We monitor melt pool dynamics and layer-by-layer process stability in real time, and inspect finished surfaces for roughness, porosity indicators, and geometric deviation, addressing one of additive manufacturing's most persistent quality challenges.
The surface roughness problem in laser additive manufacturing is well known and largely unsolved at scale. Parts produced by LPBF and DED have inherently rough as-built surfaces that often require post-processing. Measuring roughness on complex geometries is slow, expensive, and typically sampled rather than 100% inspected. Our AI inspection systems change that.
Multi-modal sensors observe melt pool dynamics, spatter, and thermal signatures layer by layer. ML models detect instabilities that predict porosity, delamination, and surface defects before the build is complete.
Models learn the correct build signature for your material and parameters, then flag layer anomalies without requiring labelled defect data. Diagnostics identify whether anomalies stem from powder quality, beam stability, or thermal gradients.
ML regression models map in-process signals to final part quality outcomes, predicting surface roughness, density, and dimensional accuracy from layer-by-layer data.
AI imaging evaluates as-built surfaces automatically, measuring roughness, detecting porosity indicators and surface defects, and flagging parts outside specification. Every part characterised at production speed.
Traditional measurement approaches for as-built surfaces are too slow for production, require contact with the part, and typically sample only a fraction of the surface area.
Our AI approach characterises surface roughness and detects anomalies from imaging data across the entire part surface, automatically, without contact, at production speed. The result feeds directly into quality records, process optimisation loops, and accept or reject decisions.
Quantified from imaging data across the full part surface. Mapped spatially and compared to specification limits automatically.
Surface porosity indicators and near-surface defects detected from imaging. Correlated with in-process anomaly signals for root cause insight.
Dimensional deviation from nominal detected and mapped, identifying warping, shrinkage, and layer registration errors.