AI-orchestrated material testing and simulation
labPoynt is a research-stage platform being developed from near-future PhD work. It explores how AI, finite-element simulation, analytical models, and sensor-based validation can support university and factory laboratory workflows for material testing.
Research preview today. Lab automation product tomorrow.
From a plain-language problem
to a validated result
The same architecture as the rest of ePoynt — an AI orchestration layer over a domain engine. TaxPoynt orchestrates over NRS/UBL; labPoynt orchestrates over finite-element solvers.
Interpret
Describe the engineering problem in plain language. An LLM interprets the geometry, loads, materials, and boundary conditions into a structured simulation spec.
Simulate
labPoynt configures and runs a finite-element simulation (FEniCSx / DOLFINx) from that interpretation — no hand-written solver script per case.
Cross-check
Results are compared against the closed-form analytical solution — e.g. Euler–Bernoulli tip deflection (δ = PL³/3EI) and max stress (σ = 6PL/bh²) for a cantilever.
Report
A structured report lays out the inputs, predicted values, the analytical comparison, and the percentage error — so a result can be read and defended, not just produced.
The first research prototype focuses on a single cantilever-beam case.
Measured against reality,
not just simulated
An instrumented cantilever closes the loop. The framework predicts, a sensor on a real beam measures, and labPoynt reports predicted-versus-measured — simulation-driven experimental validation, the part a mechanical engineer owns.
Analytical
The closed-form textbook solution (Euler–Bernoulli) — the reference truth.
FEniCSx
The framework’s finite-element prediction, orchestrated by labPoynt.
Physical rig
A sensor on a real instrumented cantilever — the measured truth.
Agreement and % error across all three — turning “we simulated it” into “we measured it against reality.”
Built for teaching and
research labs first
Universities, polytechnics, and engineering departments are the natural early adopters — they need affordable, defensible ways to connect simulation to experiment.
University & teaching labs
Factory QA labs
Factory quality labs need calibration, traceability, and recognised standards (e.g. ASTM E8/E8M) before they trust a tool for quality control. That comes after credibility is earned in teaching and research settings — not on day one.
Staged from research preview
to lab platform
Claims are kept honest by staging them. What exists today is a research prototype; the full lab platform is the intention, not a current capability.
Research preview
AI-orchestrated FEniCSx simulation with an analytical cross-check, demonstrated on a cantilever-beam case. The current thesis / prototype direction.
Pilot lab kit
A university demonstration with a single-sensor validation rig — predicted-versus-measured in the loop, packaged for a teaching lab.
Lab platform
Software + hardware + structured reporting for material-testing workflows: sensor-based measurement, live dashboards, cloud telemetry, and closed-loop re-simulation.
labPoynt is not a certified testing machine and is not a replacement for Instron / Shimadzu / MTS-class equipment at this stage.
Help shape AI-orchestrated material testing
The current work begins as a PhD / MPhil research prototype; the long-term intention is a software-and-hardware platform for university and factory laboratories. If that overlaps with your lab, teaching, or research, let’s talk.