AI for Engineering
How artificial intelligence supports engineering work, knowledge transfer, and decision-making. Privacy and ethics first.
Why AI is Used Here
Engineering Knowledge Transfer
Additive manufacturing spans multiple domains: materials science, optics, embedded systems, thermodynamics, mechanical design. Traditional documentation is fragmented—specs in PDFs, design rationale lost, vendor datasheets scattered.
AI solves: Synthesize cross-domain knowledge. A newer engineer asks "why did we choose CO₂ laser over fiber?" AI connects optical absorption curves, cost data, service history, and past decisions into a coherent narrative.
Without AI: Knowledge lives in email, Slack, or one person's head. With AI: Structured, searchable, contextual knowledge accessible to the team.
Decision Documentation
Every system design involves tradeoffs. We chose galvanometer mirrors over gantry systems. We picked nylon 12 over TPU. These choices have historical context, cost impact, and performance implications.
AI supports: Surface the reasoning behind past decisions. When new constraints emerge (e.g., need faster build speeds), AI helps evaluate if old tradeoffs still hold or if architectural changes are needed.
Accelerated Onboarding
New engineers spend weeks reading documentation, asking questions, and reverse-engineering decisions. AI can compress this by providing immediate, contextual answers grounded in the actual codebase and design history. This frees experienced engineers from repetitive explanation.
AI for Engineering Understanding
Source of Truth
All AI answers reference specific sources: firmware commits, design documents, test data, vendor datasheets. No hallucinations. No guessing. If AI doesn't know, it says so.
Example query:
"Why is the bed temperature set to 165°C?"
AI response: See design_doc_v3.md (§4.2), test_results_thermal.csv (runs 45-52), and nylon_12_datasheet.pdf (page 8). At 160°C, parts show warping. At 170°C, powder sinters unintentionally. 165°C is the stability window.
Context Awareness
AI understands the system holistically. A question about laser power isn't answered in isolation— it connects to thermal effects, part quality, build speed, and power supply constraints.
Example query:
"Can we increase laser power to speed up prints?"
AI response: Increasing power from 40W to 50W would reduce per-layer time by ~15%. But thermal model (firmware/thermal.c) shows bed temperature would rise to 172°C (beyond 165±2°C window). This increases part warping risk by 25% (historical data). Alternatively, increase galvo scan speed (+20% possible, limited by mirror inertia). Tradeoff: slower but stable, vs. faster but risky. Recommend cooling system upgrade first.
No Black Box Decisions
Every answer includes reasoning. AI explains not just "what" but "why" and "how confident am I?" This builds trust and enables engineers to make informed decisions, not follow rules blindly.
Private Knowledge Assistant (Future)
Vision: A conversational AI that understands our complete system architecture, design history, and engineering decisions. Available to team members only. No data leaves the organization.
Capabilities (Planned)
- →Query design decisions with full context ("Why galvos not gantry?")
- →Search across firmware, schematics, test data, and design documents
- →Explain tradeoffs and constraints in engineering language
- →Suggest design alternatives with supporting rationale
- →Help new engineers ramp up on specific subsystems
- →Reference exact documents, test runs, and code commits
Implementation Approach
Data sources: Design documents, firmware repositories, test data, vendor documentation, historical decisions, performance metrics.
Model: Fine-tuned on domain-specific engineering knowledge. Not a general-purpose chatbot. Optimized for technical accuracy over conversational fluency.
Deployment: Self-hosted. No reliance on external APIs. All inference runs on our infrastructure.
Version control: Knowledge base versioned alongside codebase. Changes to decisions tracked and auditable.
When This Solves Problems
- → New team member needs firmware context (instant onboarding)
- → Evaluating if architecture change is feasible (search past constraints)
- → Someone leaves the company (knowledge isn't lost)
- → Debugging requires understanding why a design choice was made
- → Scaling team requires institutional knowledge capture
Ethical & Privacy-First Approach
No Data Leakage
All AI processing is local or within our infrastructure. Proprietary designs, test data, and business metrics never leave the company servers.
Commitment: Even if we use third-party models, they access only sanitized, anonymized context necessary for the query. No raw data sent externally.
Transparency
AI responses are explainable. Sources are cited. Confidence levels are stated. If a recommendation comes from historical data with 5-part sample size (low confidence), AI says so.
Users should never blindly trust AI output. AI augments human judgment, doesn't replace it.
Bias Awareness
AI training data reflects past decisions. Past decisions can contain biases or constraints that no longer apply. AI highlights potential blind spots.
Example: If all past thermal designs favored CO₂ lasers, AI might not strongly suggest fiber laser alternatives. We actively probe: "What constraints have changed? What new technologies exist?" to keep knowledge current.
Human Authority
Final decisions stay with engineers. AI is a tool for information synthesis and exploration, not a decision-maker. Safety-critical systems (thermal control, laser interlocks) are never delegated to AI. Humans review, understand, and approve.
Accountability
All AI interactions are logged (who asked, when, what context was used). This creates an audit trail.
Why: If a design recommendation from AI later proves problematic, we can trace the reasoning and understand where the model erred. Continuous improvement.