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Applications

Built for the problems you actually have.

Pre-built process knowledge across 11 active application areas, from anode and cathode lines to ceramic sintering and carbon fiber layup.

Energy Storage

6 applications

Lithium-Ion Cell Manufacturing

Formation, aging, and final test: full cell genealogy

Defects in lithium-ion cell manufacturing originate upstream of where they are detected. A coating pinhole formed at 1.2 m/min web speed shows up as voltage fade three weeks later in formation. Niobia AI closes that loop by tying inline vision to process telemetry across every step from slurry mixing to formation.

Anode Manufacturing

Coating, calendering, slitting: graphite, silicon, Nb₂O₅

Graphite anode coating is a precision process where slot-die speed, binder ratio, and calendering pressure interact to determine cycle life. A pinhole or agglomerate invisible at 30 m/min web speed becomes a lithium plating site that fails after 200 cycles. Niobia AI links every coating frame to the upstream slurry state that produced it.

Cathode Manufacturing

Slurry, coating, drying, calendering: NMC, LFP, NCA

NMC, LFP, and NCA cathode lines share the same equipment but differ in where they fail. NMC811 secondary particles fracture above 200 MPa calendering load. LFP aqueous slurries are sensitive to pH and mixing time. NCA requires stricter moisture control in drying. Niobia AI's detection models are recalibrated for each cathode chemistry.

Electrolyte Manufacturing

Mixing, filling, QC: liquid and dry electrolytes

Electrolyte quality is invisible until it shows up in formation yield. LiPF₆ decomposition from moisture above 10 ppm produces HF that corrodes current collectors. Filling weight variance of ±0.2 g shifts ESR distribution by 15-25%. Niobia AI connects fill records, moisture logs, and formation curves to find electrolyte-driven yield loss before it becomes a scrap cost.

Battery Recycling & Second Life

Black mass, hydromet, sorting: from pack to precursor

Battery recycling economics depend on sorting accuracy and black mass purity. Cross-contamination of LFP and NMC black mass destroys the value of both streams. Chemistry misidentification at the sorting stage propagates through hydrometallurgical processing, reducing precursor yield by 20-40%. Niobia AI brings AI-driven sorting and yield prediction to recycling lines.

Flow Cell Manufacturing

Stack assembly, membrane QC, electrolyte handling

Vanadium redox flow battery manufacturing is constrained by membrane quality and stack assembly consistency. A Nafion membrane pinhole causes cross-contamination that degrades electrolyte capacity by 15-30% per year. Torque non-uniformity in stack assembly creates local compression gradients that accelerate carbon felt degradation. Niobia AI links inline membrane inspection to stack assembly QC and electrolyte monitoring.

Emerging Chemistries

5 applications

Lithium-Sulfur Batteries

Sulfur sublimation, polysulfide handling, high-loading cathodes

Li-S manufacturing inherits the coating and calendering chain but adds constraints that invalidate most Li-ion inspection assumptions: sulfur sublimation above 60 °C, polysulfide reactivity with atmospheric moisture, and high-loading cathodes at 5 mg/cm² or more that turn mechanically fragile after calendering. Standard NMC-calibrated vision systems miss the dominant defect classes entirely.

Sodium-Ion Batteries

Hard carbon, layered oxides, Prussian white outgassing

Na-ion uses Li-ion equipment but carries chemistry-specific defect modes that NMC-calibrated systems miss entirely: hard-carbon anodes with wider particle size distributions, layered-oxide cathodes more moisture-sensitive than NMC, and Prussian-white cathodes that outgas during drying. The assumption that Na-ion is just Li-ion with a different salt breaks at dry-room standards, calendering limits, and formation protocols.

Solid-State Batteries

Sulfide moisture reactions, sintering camber, compression cracking

SSB production introduces defect modes with no Li-ion analogue: sulfide-electrolyte moisture reactions that generate H₂S at parts-per-million moisture levels, oxide-electrolyte sintering camber that warps sheets 200 μm across a 200 mm wafer, and composite-cathode compression cracking under 300 to 700 MPa. Each defect class needs a different detection modality and a different process control loop.

Supercapacitors & EDLC

BET surface area spread, ESR variability, electrolyte wetting

EDLC manufacturing is close to Li-ion electrode production in process terms, but the performance driver is different. Cell-to-cell ESR, capacitance, and self-discharge variability on mature lines is dominated by activated-carbon BET surface area distribution and electrolyte wetting, not particle size or coating thickness. Tightening that spread is worth more than reducing scrap.

Fuel Cells

CCM defect rates, IR thermography, stack traceability

PEM fuel cell manufacturing is bottlenecked by catalyst-coated-membrane defect rates. A 4 to 8 percent missing-catalyst defect area can drive a cell to end-of-life within 50 hours of accelerated stress testing. The detection gap is not sensor capability, it is data plumbing between IR thermography, optical line-scan, slot-die telemetry, and stack performance systems that share no cell identity.

Polymer Processing

2 applications

Injection Molding

Cavity-pressure + vision: zero-escape defect control

Injection moulding defect detection is no longer constrained by sensor technology: it is constrained by data plumbing between cavity-pressure sensors, machine PLCs, and downstream vision systems. Niobia AI creates the part-level data record that links every vision frame to its cavity-pressure trace and machine setpoints.

Polymer Extrusion

Gel, gauge variation, die-line intelligence at line speed

Polymer extrusion defects originate in melt quality and propagate through the die into the cooling stack. Inline vision systems running at line speed detect defects across tube and film profiles. Niobia AI creates a per-meter record linking every gel detection frame to the upstream melt-state that produced it.

Advanced Materials

3 applications

Ceramic & Technical Ceramics

Sintering, green body inspection: solid-state and power electronics

Technical ceramics for solid-state batteries and power electronics carry sintering yields below 50% in many pilot programs. Green body defects: density gradients, microcracks, inclusion agglomerates: survive the binder burnout and amplify during sintering at 1,000-1,700 °C into warpage, porosity pockets, and surface fractures that reject the entire part. Niobia AI inspects before sintering to prevent committing the thermal budget to defective green bodies.

Carbon Fiber & Composites

Layup, cure, NDT: aerospace and EV structural

Carbon fiber composite manufacturing for aerospace and EV structural applications has a defect tolerance measured in fractions of a percent void content. Fiber misalignment above 2°, dry spots in AFP layup, and voids above 0.5% from cure cycle deviations can all reject a part worth thousands of dollars. Niobia AI connects AFP inline inspection, autoclave cure monitoring, and NDT image analysis into a single part genealogy.

Thin Film Deposition

PVD/CVD inspection: batteries, solar, and semiconductors

Thin film deposition for battery cathode coatings, solar TCO layers, and semiconductor metallization requires thickness uniformity within ±2-5% across large substrates. Target erosion in sputtering shifts composition over runs; macroparticles from cathodic arc sources create pinholes in films as thin as 10 nm. Niobia AI provides inline optical thickness monitoring and particle detection on moving substrates.

Electronics & Solar

2 applications

Semiconductor Manufacturing

Wafer fab, etch, deposition, CMP

AI process intelligence for semiconductor wafer fabrication: connecting etch rate, deposition uniformity, CMP planarization, and yield outcomes across the full fab process flow. The analysis methods below are live on the platform today. Fab-specific integrations and wafer-map intelligence are in active development, so this area is early access.

Photovoltaic & Solar Cell Manufacturing

Cell, module, and encapsulant quality control

AI process intelligence for photovoltaic manufacturing: connecting cell efficiency variance to deposition conditions, encapsulant quality, and module assembly outcomes. Niobia's thin-film deposition and materials characterization capabilities already cover much of the PV process chain. PV-specific module and field-performance work is early access.