Cold-energy fabrication avoids high thermal gradients that typically introduce residual stresses, grain coarsening, and microstructure collapse. L.O.M.A. research is grounded in experimentally supported processes such as:

NOTE: Formula within is written in LaTeX, the standard markup language used in science and mathematics to format equations cleanly.

• Cold-spray deposition (particle kinetic bonding)

Bond formation occurs when particle kinetic energy exceeds bonding threshold:

\frac{1}{2} m v^2 \geq E_{\text{bond}}

allowing densification without melting.

• Electrochemical reduction and deposition

Material deposition rate follows Faraday’s law:

m = \frac{Q M}{nF}

where Q is charge, M molar mass, n valence number, and F Faraday constant.

• Non-thermal plasma activation

Surface activation governed by electron impact reactions at temperature ranges where bulk remains near-ambient. Energy input is mainly electronic rather than thermal.

• Bond-specific excitation (BSE)

Energy delivery tuned to specific vibrational modes:

E = h\nu

to trigger selective bond alignment or lattice nucleation without bulk heating.

LupoTek’s material research extends to:

• metal–ceramic laminates with anisotropic stiffness

• high-toughness polymer–graphene composites governed by effective modulus predictions:

  E_{\text{eff}} = V_f E_f + (1 - V_f)E_m

• corrosion-resistant alloys parameterised by Nernst potentials

• micro-lattices optimised using FEA-driven topology control

Microstructures are evaluated using grain orientation distribution functions (ODFs) and misorientation profiles derived from EBSD (electron backscatter diffraction), ensuring fabrication remains grounded in measurable crystallographic data.

Precision fabrication requires real-time regulation of deposition, alignment, and densification processes. LupoTek employs neuromorphic controllers, which mimic biological spiking neural networks, enabling rapid response to high-frequency fabrication feedback.

These systems regulate:

• deposition thickness (feedback error: e(t) = d_{\text{target}} - d(t))

• plasma or field strength modulation (via PID or adaptive control)

• microstructure consistency (using in-situ spectral feedback)

• anomaly detection in deposition morphology using clustering of deviation vectors

Neuromorphic control is advantageous because spike-driven computation naturally encodes temporal derivatives, allowing approximate real-time estimates of:

\frac{d\mathbf{x}}{dt}

for deposition states x(t) with minimal computational overhead.

Companion-Intelligence Integration

Companion-Intelligence modules perform:

• statistical process control (SPC)

• Bayesian updating for process-parameter refinement:

  p(\theta \mid x) = \frac{p(x \mid \theta)p(\theta)}{p(x)}

• predictive fatigue modelling using S–N curves

• multi-batch optimisation via regression on microstructural quality metrics

This dual system - fast neuromorphic controllers + analytical Companion-Intelligence - ensures the fabrication process remains scientifically transparent, measurable, and adjustable.

LOMA’s circular fabrication pipeline is rooted in scientifically validated separation and recovery strategies.

• Material identification

Performed using Raman spectroscopy (peak-fitting of modes \nu_1, \nu_2, \ldots), XRF elemental fingerprinting, or LIBS optical signatures.

• Electrochemical dissociation

Material separation follows Nernst–Planck transport equations:

\mathbf{J} = -D\nabla c + z u c \mathbf{E}

describing how ionic species migrate under concentration gradients and electric fields.

• Field-assisted delamination

Surface separation driven by interfacial energy reduction:

\Delta G = \gamma_{\text{interface}} - \gamma_{\text{separated}}

• Microstructure reset and densification

Achieved using cold isostatic pressing (CIP) or field-driven compaction, where densification is proportional to pressure:

\rho = \rho_0 + kP

Recovered materials are validated through:

• XRD lattice parameter measurements

• EBSD misorientation maps

• mechanical testing for elastic modulus and yield strength

All of this ensures the circular cycle remains scientifically grounded rather than hypothetical.

Different mission profiles impose different requirements. LupoTek fabricates materials and structures based on quantifiable mechanical constraints.

Aerial / trans-atmospheric

Requirements driven by:

• aerodynamic loads (lift/drag equations)

• vibrational analysis using:

  \omega_n = \sqrt{\frac{k}{m}}

• thermoelastic stress:

  \sigma = E \alpha \Delta T

Maritime / subsurface

Constraints include:

• hydrostatic pressure P = \rho g h

• corrosion potentials

• cavitation thresholds determined by Bernoulli’s equation

Hardened terrestrial systems

Require:

• ballistic impact modelling using energy dispersion

  E_d = \frac{1}{2} m v^2 - \sigma_y \epsilon

• fatigue resistance using Basquin’s law

L.O.M.A.’s adaptive recipes adjust composite ratios, deposition rates, layer thicknesses, and microstructure orientation based on these measurable constraints.

LupoTek’s fabrication research progresses through validated scientific improvements rather than speculative leaps.

Key research directions:

• higher-resolution, field-driven nano-deposition (sub-10 μm control)

• improved energy-flux shaping using Maxwell equation modelling

• dynamic recipe adjustment via real-time Bayesian estimation

• microstructural prediction using crystal-plasticity modelling

  \dot{\varepsilon} = \sum_s \dot{\gamma}_s \mathbf{m}_s \otimes \mathbf{n}_s

• fatigue prediction through Paris’ law:

  \frac{da}{dN} = C (\Delta K)^m

Companion-Intelligence continuously assimilates data from prior fabrication cycles, operational wear profiles, and recovery processes, expanding the fabrication knowledge base using clustering algorithms, kernel density estimation, and multi-variable regression on quality metrics.

This allows fabrication outputs to incrementally converge toward optimal structural performance, not through speculation, but through measurable feedback, validated physics, and iterative scientific refinement.