Case Studies

Representative deep-tech work across simulation, controls, and intelligent systems.

ALE friction welding simulation

Problem: Conventional simulation workflows were failing under large deformation and contact complexity.

Constraints: High nonlinearity, thermal coupling, and limited opportunities for expensive physical testing.

Approach: Implemented an ALE-based thermomechanical simulation stack with explicit contact and frictional heat modeling.

Outcome: Improved stability across long runs and provided earlier process-window insights for engineering decisions.

Technical highlights: Adaptive mesh motion, contact formulation tuning, validation-focused postprocessing.

Problem: A plant team needed predictive behavior from sparse and noisy machine data.

Constraints: Edge compute limits, intermittent connectivity, strict runtime and reliability requirements.

Approach: Built a compact hybrid model with domain-informed features and deployment-aware inference paths.

Outcome: Deployed to production equipment with lower false alarms and better operator trust.

Technical highlights: Model compression, runtime profiling, data-quality guardrails.

Problem: Robotic system behavior was inconsistent between simulation and real-world runs.

Constraints: Sensor noise, partial observability, and integration pressure across control and software teams.

Approach: Defined a unified simulation-estimation-control loop and tuned observer/controller interaction under uncertainty.

Outcome: More predictable closed-loop behavior and faster integration cycles.

Technical highlights: State estimation redesign, controller validation harness, simulation-to-real checks.

Problem: Engineering analytics pipelines were difficult to scale and reproduce across teams.

Constraints: Distributed workloads, variable data quality, and cross-team deployment dependencies.

Approach: Designed infrastructure patterns for distributed training, evaluation, and model lifecycle management.

Outcome: Higher throughput with better reproducibility and operational visibility.

Technical highlights: Distributed orchestration, metric governance, deployment automation.

Problem: High-fidelity simulation was too expensive for iterative design and embedded use.

Constraints: Tight compute budgets and the need to preserve physically meaningful behavior.

Approach: Constructed reduced-order surrogates calibrated against high-fidelity baselines and operational data.

Outcome: Faster design iteration with acceptable error bounds for decision support.

Technical highlights: ROM basis design, error tracking, deployment-oriented model packaging.

If your team is facing a similar simulation, controls, or deployment challenge, we can scope a focused engagement.