Fusion2Print: Multimodal Fusion Technique

• Fusion2Print is a multimodal framework that integrates complementary methodologies in additive manufacturing, biometric recognition, and software testing to overcome domain-specific limitations.
• In additive manufacturing, it leverages a dual-stage photopolymerization process that uses blue light pre-sensitization to lower energy thresholds for rapid, high-resolution 3D printing.
• For contactless fingerprint recognition and automated environment setup, Fusion2Print employs deep learning pipelines and specialized metrics to achieve state-of-the-art performance and enhanced efficiency.

Fusion2Print (F2P) refers to a set of techniques, frameworks, and metrics across multiple research domains, unified by the core idea of combining (“fusing”) complementary modalities, processes, or measurement criteria to enhance performance or efficiency. In academic literature, the term encompasses three distinct, technically unrelated research themes: (1) photo-polymerization for high-resolution additive manufacturing, (2) deep learning-based fusion for fingerprint recognition, and (3) a benchmarking metric for automated software environment construction. Each F2P context is defined by its domain-specific methodology, evaluation, and contribution.

1. Single-Photon–Assisted Two-Photon Polymerization ("Fusion2Print" in Additive Manufacturing)

Fusion2Print in the context of additive manufacturing designates a hybrid photopolymerization method that leverages single-photon absorption (1PA) to “pre-sensitize” a photoresist, thereby reducing the threshold for nonlinear two-photon absorption (2PA) by a femtosecond (fs) laser pulse. This protocol enables high-resolution, rapid 3D printing and is built on the chemical principle that 1PA efficiently populates an intermediate excited state, from which 2PA more readily initiates radical polymerization (Unlu et al., 2024).

Key Photophysical Mechanisms

• Energy-Level Scheme: The photoinitiator (PI) ground state is first excited by a 405 nm CW blue light source (1PA), forming a population of PI*. The subsequent absorption of a 780 nm fs photon (2PA) drives the PI* into its dissociative state, generating free radicals.
• Kinetics: The photoinitiator's radical formation kinetics can be modeled as a two-step, two-timescale system, with 1PA rapidly building PI* and 2PA from PI* controlling the polymerization rate.
• Effective Dose Threshold: The minimum fs dose needed for polymerization is reduced by a term proportional to the product of blue light intensity and exposure time:

$$D_{fs,th}^{eff} = \frac{[R\cdot]_{th} - (\Phi_1 \sigma_1 I_{1PA}/h \nu_{1PA})\tau}{\Phi_2 \delta}$$

Experimental Implementation and Results

• Dual-Stage Setup: A blue-light sheet (≈405 nm, ≈0.3 mW, 50 ms, ≈3 mJ/cm²) is used for presensitization, followed by focused scanning with a Ti:Sa fs IR beam (780 nm, 7–30 mW, 50 ms per spot).
• Spatial Control: Lateral hatching (Δx=760 nm, Δy=430 nm), axial stepping or digital scanning (Δz as fine as 1.5 µm) permit volumetric printing with sub-micron resolution.
• Performance Metrics: Introduction of the blue pre-illumination enables an order-of-magnitude reduction in the 2PA dose and printing time for a 150 nm voxel. For larger voxels (1.5 µm), fs power requirements drop from 15 mW to 7 mW with blue presensitization.
• Operational Limits: Blue dose must remain below 8 mJ/cm² to prevent bulk polymerization, and control of fs power and SLM response is necessary to balance feature size, speed, and material stability.

Process Optimization Guidelines

Optimal PI selection (strong 1PA at 400–420 nm; residual 2PA at IR), blue sheet dose control, and use of SLM for fast digital focusing are recommended for achieving high-throughput, high-resolution prints. Fusion2Print can also be adapted to volumetric and tomographic additive manufacturing (Unlu et al., 2024).

2. Deep Flash–Non-Flash Fusion for Contactless Fingerprint Recognition ("Fusion2Print" Framework)

In biometric recognition, Fusion2Print is the designation for an integrated deep learning pipeline that fuses paired flash and non-flash images, acquired in rapid succession from a contactless fingerprint capture system, in order to optimize ridge clarity and cross-sensor compatibility (Sahoo et al., 5 Jan 2026).

Data Acquisition and Preprocessing

• Paired Data ("FNF Database"): 3,140 paired images were collected via a custom smartphone app (Samsung A54), capturing flash and non-flash finger photos with sub-second inter-capture delay and minimal finger displacement. ROI cropping and phase-correlation-based alignment precede channel-wise enhancement.
• Spectral Subtraction: Low-frequency background is subtracted from flash images using a radial low-pass mask and per-channel difference, followed by normalization with CLAHE, adaptive binarization, Gaussian smoothing, and Gabor filtering to create high-quality, ridge-focused targets.

Architecture and Learning Components

• DualEncoderFusionNet: Two parallel convolutional encoders extract features from flash/non-flash images, fusing them with channel-wise attention weights computed by a small MLP with sigmoid. The fused map is passed to a decoder with frequency and edge residuals amplified.
• U-Net Enhancer Module: Processes fused output, combining per-channel contrast statistics to produce an optimally weighted grayscale image. Losses are channel-weighted and include local contrast, SSIM, edge, and Gabor components.
• TripletDistilNet: A ResNet-18 backbone produces l2-normalized embeddings (dim = 128 or 256) using a triplet loss with cosine distance and a distillation term from a DeepPrint teacher. Fine-tuning on contact fingerprints achieves cross-domain compatibility.

Training and End-to-End Optimization

Sequential training for each sub-module is followed by pipeline-level fine-tuning, combining triplet and embedding cosine losses. Typical hyperparameters include Adam optimizers with learning rate schedules, epoch limits, and empirically determined loss weights.

Evaluation and Comparison

System/Stage	AUC	EER (%)	Notes
VeriFinger (baseline)	0.94	7.60	Single-channel enhanced images
DeepPrint (baseline)	0.97	8.92	Single-channel enhanced images
F2P (128-dim, base)	0.993	3.93	Post-fusion, pre-finetune
F2P (128-dim, finetuned)	0.998	1.91
F2P (256-dim, base)	0.997	1.97
F2P (256-dim, finetuned)	0.999	1.30
F2P + Spatial Transform	0.999	1.12	Best reported

The Fusion2Print pipeline outperforms all single-modality baselines on the FNF dataset, achieving state-of-the-art results.

Limitations and Future Work

• Single-device data restricts cross-sensor generalization, currently mitigated by posthoc fine-tuning.
• Explicit adversarial/spoof resistance is not considered.
• Ridge enhancement is presently limited to two-capture (flash/non-flash) fusion, future extensions may include photometric-stereo and multi-illumination strategies (Sahoo et al., 5 Jan 2026).

3. Fail-to-Pass (F2P) Metric in Automated Environment Building for Software Engineering

Within software engineering automation, F2P (Fail-to-Pass rate) is a primary evaluation metric for automated build/test environment construction. It quantifies an agent's ability to generate a containerized (Dockerized) test environment that exposes known bugs (test fails pre-patch) and validates their patches (test passes post-patch) (Fu et al., 7 Dec 2025).

Metric Definition

Given $N$ problem instances $(R_i, P^*_i, T_i(\cdot))$, F2P is the fraction of cases where

• The original code $R_i$ fails its associated test $T_i$.
• After automatic setup (Docker environment + test script), $T_i(R_i \oplus P^*_i)$ passes.

$\mathrm{F2P} = \frac{1}{N} \sum_{i=1}^N S_i$

where $S_i = 1$ only if both above conditions are true.

Experimental Protocol

• Dataset: 334 issue/patch pairs from 40 repositories in 9 major programming languages.
• Evaluation Procedure: Each agent must (1) submit a buildable Dockerfile and test script, (2) build completes within a 30-minute timeout, (3) test harness fails before patch, passes after. Each run is repeated 3× to filter flakiness.
• Agent Frameworks: SWE-Builder (multi-agent, repair loop) and RepoLaunch (single-agent, bash-based).

Benchmarked Results

System	F2P (%)	Observations
DeepSeek-v3.1	37.72	Best overall
Kimi-K2-0905	37.62	Comparable to SOTA
SWE-Builder (avg)	30.58	Multi-agent, feedback-driven
RepoLaunch (avg)	8.85	Single-agent, lower success
Go (language SOTA)	54.5	Ecosystem with uniform modules
Java	10.5	Multi-tool build, diverse configs

Environment construction failures (especially Docker build failures and test harness misconfiguration) are the dominant causes of low F2P. Current best models do not exceed 38% F2P in aggregate.

Identified Bottlenecks and Recommendations

• Docker build errors are the largest single class of failure (36.09%).
• Test harnesses missing or non-executable (18.12%).
• "Silent false passes"—tests passing pre-patch (12.7%).
• Recommended practices include multi-agent, iterative repair architectures, dependency reasoning, memory/caching of successful configs, targeted prompt engineering, and expansion of training data for system-level dependency resolution.

4. Fusion2Print in 3D Printing for Inertial Confinement Fusion Target Fabrication

An additional usage of the F2P concept, often implicitly, is in the fabrication of microscale, 3D-printed foams for inertial confinement fusion (ICF) targets using advanced two-photon polymerization. Experiments and simulations substantiate the role of F2P protocols in achieving controllable laser–foam coupling and ablation (Cipriani et al., 4 Jun 2025).

3D Printing Protocol and Morphology Control

• Two-photon polymerization using a femtosecond Yb:KGW laser (λ₀ = 1030 nm, pulse τₚ ≈ 180 fs) with SHG to 515 nm for the pre-polymer SZ2080®.
• Achievable voxel sizes ≈ 0.3 × 0.3 × 1 μm³, with foam geometries (log-pile lattices, filament t ~14 μm, pitch ~39 μm).
• Densities tuned via filament/pitch ratio, allowing control from future O(10 mg/cm³) to >100 mg/cm³.

Laser Ablation Performance and Modelling

• 99%+ energy deposition achieved for targets <500 μm thick, confirmed by negligible transmission and backscattered/reflected diagnostics.
• Erosion front speeds (ablation) v_ero = 20–30 μm/ns at I_L = 10¹⁴–10¹⁵ W/cm², with close match to simulation (FLASH hydrocode).
• Two-plasmon decay (TPD) observed as a source of localized preheat but <5% of total energy, manageable for ICF ablation requirements.

Design Recommendations and Scaling

• Filament diameters/pitches ≤10 μm and lower foam densities <100 mg/cm³ are suggested for more ideal shock formation and energy coupling.
• Stochastic lattices can reduce coherent scattering, promoting homogenization.
• Scale-up through acousto-optic scanning and multi-voxel writing for mass-manufacture of ICF targets is technically feasible (Cipriani et al., 4 Jun 2025).

5. Open Problems and Future Directions

• In additive manufacturing, extending F2P to multi-focus, tomographic light-sheet, or TVAM architectures could bridge speed-versus-resolution limitations.
• For contactless fingerprinting, future F2P pipelines may incorporate more general multi-illumination, photometric-stereo, and joint spoof-resistance training, with expanded, multi-device datasets (Sahoo et al., 5 Jan 2026).
• In software automation, benchmarks integrating F2P as a primary gatekeeper could shape the roadmap toward more robust, generalizable LLM-driven build systems and automated reasoning for complex dependency graphs (Fu et al., 7 Dec 2025).
• In ICF target fabrication, implementation of graded-density lattices, exploration of lattice randomness, and in-depth mapping of laser–plasma instability thresholds will further refine the utility and generalizability of F2P-enabled 3D printing (Cipriani et al., 4 Jun 2025).

Fusion2Print thus represents a paradigm of modality fusion to overcome inherent physical or algorithmic trade-offs—whether in scientific imaging, manufacturing, or automation pipelines—consistently with application-specific architecture, process control, and evaluation metrics.