Transformers and Attention Mechanisms for Additive Manufacturing

Self-attention architectures for sequence modeling, time series, and process control in AM

Transformers for AM

Research Papers 520+

Primary Focus Time series, sequences

Key Applications Monitoring, prediction

Emerging Since 2020

Advantage Long-range dependencies

Growth Rate +120% since 2022

Transformers, the architecture behind GPT and modern language models, are revolutionizing sequential data processing in additive manufacturing. Their self-attention mechanism captures long-range dependencies in time series data, process parameter sequences, and toolpath coordinates that traditional RNNs and LSTMs struggle with.

In AM applications, transformers excel at modeling the temporal evolution of process signatures (acoustic, thermal, optical), predicting build outcomes from parameter sequences, and generating optimal scan strategies. The attention weights themselves provide interpretability, revealing which process events most influence quality outcomes.

Contents

Attention Fundamentals
Time Series Analysis
Process Monitoring
Toolpath and G-code
Transformer Architectures
AM Applications
Key References

520+

Research Papers

15-20%

Accuracy Gain vs LSTM

10,000+

Sequence Length

120%

Growth 2022-24

Attention Fundamentals

Self-attention computes relationships between all positions in a sequence simultaneously, unlike RNNs which process sequentially:

Attention(Q, K, V) = softmax(QK^T/√d_k)V

Why Attention for AM?

Long-range dependencies: Layer N quality depends on layers 1...N-1
Parallel processing: Faster training than sequential RNNs
Interpretability: Attention weights show influential time steps
Transfer learning: Pre-trained models adapt to AM domains

Key Components

Multi-head attention: Parallel attention with different learned projections
Positional encoding: Inject sequence order information
Feed-forward layers: Non-linear transformations between attention
Layer normalization: Stabilize training dynamics

Time Series Analysis

AM processes generate rich time series data from multiple sensor modalities:

Signal Type	Sampling Rate	Transformer Application	Prediction Target
Acoustic emission	100 kHz - 1 MHz	Anomaly detection	Porosity, cracking
Melt pool thermal	1-10 kHz	Temperature forecasting	Overheating, lack of fusion
Photodiode intensity	10-100 kHz	Process stability	Keyholing, balling
Layer-wise images	Per layer	Sequence classification	Build success/failure

Temporal Fusion Transformer

TFT combines LSTM encoding with multi-head attention for interpretable time series forecasting. In AM, it provides both accurate predictions and attention-based explanations of which process phases most influence outcomes.

Process Monitoring

Transformers enable real-time monitoring by processing streaming sensor data:

Anomaly Detection

Reconstruction-based: Autoencoder transformers learn normal patterns
Prediction-based: Forecast next values, flag large residuals
Attention-based: Unusual attention patterns indicate anomalies

Monitoring Task	Architecture	Performance	Latency
Defect detection	Vision Transformer (ViT)	F1 > 0.95	10-50 ms
Process state classification	BERT-style encoder	Acc > 98%	5-20 ms
Quality prediction	Encoder-decoder	R² > 0.92	Per-layer
Remaining useful life	Temporal transformer	MAPE < 10%	Real-time

Multi-Sensor Fusion

Cross-attention mechanisms fuse information from heterogeneous sensors (thermal cameras, acoustic, optical) with different sampling rates and data formats, learning optimal combinations for prediction.

Toolpath and G-code

Treating toolpaths as sequences enables transformer-based optimization and generation:

Scan Strategy Optimization

Sequence-to-sequence: Input geometry → output optimal scan path
Autoregressive generation: Generate vectors one at a time
Constraint satisfaction: Encode boundary and overlap rules

G-code as Language

G-code commands form a structured language amenable to transformer processing. GPT-style models can learn to generate valid G-code from high-level specifications, correct errors in existing programs, and optimize for specific objectives (time, quality, thermal).

Toolpath Task	Input	Output	Method
Path generation	STL geometry	Scan vectors	Encoder-decoder
Parameter optimization	Path + material	P, v, h per segment	Regression head
Thermal balancing	Path sequence	Reordered sequence	Pointer networks
Error correction	Faulty G-code	Corrected G-code	Seq2seq + beam search

Transformer Architectures

Architecture	Description	AM Application	Advantage
Vision Transformer (ViT)	Patch-based image attention	Layer/melt pool images	Global context
Swin Transformer	Shifted window attention	High-res inspection	Efficient for large images
Informer	Sparse attention for long sequences	Full-build time series	O(L log L) complexity
Autoformer	Auto-correlation attention	Periodic process signals	Captures seasonality
Crossformer	Cross-dimension attention	Multi-sensor fusion	Variable interactions
PatchTST	Patching for time series	Sensor forecasting	State-of-the-art accuracy

Efficiency Considerations

Linear attention: Reduce O(n²) to O(n) for long sequences
Sparse attention: Only attend to relevant positions
Knowledge distillation: Compress large models for edge deployment
Quantization: INT8 inference for real-time monitoring

AM Applications

Process-Structure-Property Linkage

Transformers model the causal chain from process parameters through microstructure evolution to final properties:

Input: Parameter sequences (P, v, h over time/space)
Hidden: Thermal history → cooling rates → solidification
Output: Mechanical properties, defect probability

Foundation Models for AM

Pre-trained transformer models on large AM datasets enable:

Few-shot learning for new materials/processes
Zero-shot anomaly detection
Cross-machine transfer learning
Natural language interfaces for process control

Attention for Interpretability

Unlike black-box models, transformer attention weights reveal which process events most influence predictions. This interpretability is critical for qualification: engineers can verify the model focuses on physically meaningful features rather than spurious correlations.

Key References

Attention Is All You Need

Vaswani et al. | NeurIPS 2017 | 90,000+ citations

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Dosovitskiy et al. | ICLR 2021 | 25,000+ citations

Transformer-based deep learning for predicting melt pool geometry in laser powder bed fusion

Chen et al. | Additive Manufacturing | 2023 | 65+ citations

Vision Transformer for in-situ monitoring of laser powder bed fusion

Zhang et al. | Journal of Manufacturing Processes | 2023 | 45+ citations

Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

Lim et al. | International Journal of Forecasting | 2021 | 2,500+ citations