Nowadays it is very popular to complain how in a few years’ time AI is going to replace us all. With current available tools let’s take this statement to the test.
This post series is dedicated to reaching from scratch, step by step, a System Requirement document. Until now we explored the processes and best practices we humans have to utilize in order to reach an optimal sysreq. In this post we leave this task to AI models.
We will give the different AI models a prompt with the gist of what I initially started in the first post in this post-series and see how 2 different models handle it towards a result.
To have a fair comparison between my version of sysreq and what the different AI models created, I expect the AI’s results to be a more complete version of a document since I stopped writing the example when my point had been made (and my time was over
).
Important note: the rate AI models are evolving these days is faster than my ability to write posts. For the experiment I used Anthropic’s Claude Opus 4.6 and OpenAI’s ChatGPT 5.3. By the time this post is published more advanced models will already be available.
AI Models Running Solo
In order to make this experiment both Claude and ChatGPT received as input the same prompt.
The following is the exact prompt used:
act as a multi-disciplinary system engineer
we have to design a Laser-Cutting Machine for the metal processing industry. here are the top-level Product requirements as given by the product management for a laser cutting machine:
Laser-Cutting Machine for the metal processing industry requirements
- Metal types: Aluminum, Stainless steel
- Maximum metal piece dimensions (W,L,T): 250cm, 150cm, 15mm
- Smallest cutting feature: 35um
- Target BOM price: 150,000 USD
create the following:
- system architechtures – create at least 3 architectures and make a comparison table between them. the comparison should include all engineering aspects of a multi-disciplinary system architecture and not just compliance to the very short list of Product requirements. include also technical risks in the comparison. recommend a leading architecture. the architectures shall take also feedback and control, alerting and processing aspects into account.
- technical challenges details for the leading architechture.
- potential vendor lists for the main components of the system. if there are a priori items that are known to be customized or need research, detail them.
- top-level system requirements document, divided inside to the technical disciplines: SW, Electronics, Mechanics, Optics and so on.
you are required to ask additional questions regarding the requirements if there is critical information missing in the top level requirements.
At this stage, to make the experiment as clean as possible, I deliberately only answered questions in a “semi-technical” manner as you would expect a Product or Marketing representatives to answer.
ChatGPT’s Flow and Outcome
GPT immediately asked a few follow-up questions,
- Laser Type Preference: Is there a preference or requirement for the laser source? (e.g., Fiber, CO2, Disk Laser)
- Cutting Speed or Throughput Requirements: What are the production throughput targets or cutting speeds expected?
- Automation Level: Should the system include automated material handling, part unloading, or integration with factory systems (e.g., MES, ERP)?
- Environmental Requirements: Any constraints on size, power consumption, ambient conditions (e.g., dust, temperature control)?
- Precision and Tolerances: What is the acceptable tolerance for part dimensions?
- Software Capabilities: Do you require nesting optimization, remote monitoring, or CNC compatibility?
- Safety and Compliance Standards: Any regulatory compliance (e.g., CE, ISO 13849, FDA for laser safety)?
- User Interface Requirements: What kind of operator interface is expected? Touchscreen HMI, remote PC software, etc.?
One must admit, these are solid questions.
After giving GPT the answers it needed it came up as follows with 4 potential architectures with impressively detailed explanation:
Architecture A: High-Power Single-Head Fiber-Gantry System
- Laser source: One high-power fiber laser (e.g. 6–12 kW CW, 1 µm wavelength). Fiber lasers excel at cutting both stainless steel and aluminum, and can achieve tight focus for small features.
- Beam delivery: Fiber optic cable to a single cutting head (with beam expander and focusing optics). The head uses a motorized Z-axis for focus and N₂ (nitrogen) assist gas for clean cuts on stainless/aluminum.
- Mechanical frame: A rigid gantry spanning a fixed table. X- and Y-axes driven by either linear motors (high speed/precision) or high-resolution ball-screw servomotors on precision rails. Z-axis for focus (<10 mm stroke). Structure sized for 2500×1500 mm work area.
- Motion Control: Closed-loop multi-axis servo system with high-resolution linear encoders or laser interferometers on X/Y for sub-micron positioning. According to MotionTech sources, precision linear motors can attain sub-micron accuracy with frictionless motion, which is essential to meet a 1 µm tolerance on a 35 µm feature.
- Control/Software: A PC-based CNC controller (e.g. Beckhoff TwinCAT or Siemens Sinumerik) running real-time motion and process control. The CNC loads standard part geometries (DXF/G-code), performs nesting optimization (e.g. with Autodesk or Deepnest), and provides a touchscreen HMI. Remote monitoring (Industry 4.0) is supported via an edge computer (Linux PC) uploading performance data to the cloud, similar to TRUMPF’s IoT platform.
- Feedback & Sensing: Laser power feedback (monitor diode) to stabilize output. Precision encoders on axes and potentially linear optical scales ensure position accuracy. A camera or laser line sensor could register plate position (large-field vision) if needed for alignment. Safety sensors: beam-on indicators, interlock switches on enclosure doors, and gas pressure, temperature and filter clogging sensors (with alerts via PLC/HMI).
- Safety/Compliance: Fully enclosed Class 1 laser safety (interlocked doors, shutter). Follows ISO 11553 (machine laser safety) and ISO 13849 Cat.3/4 safety circuits. CE-marked (EMC, machinery directives) with emergency stops and guarded moving parts.
- Automation: Integrated loading/unloading using a pallet changer or pick-&-place (e.g. TRUMPF LiftMaster-like system). One pallet feeds up to 25 stacked plates automatically; after cutting, finished parts/scrap are offloaded to bins.
- Throughput/Tolerances: Designed to cut 25 plates/hr (each ~250×? mm) with complex contours. To meet ~144 s per plate, average cutting speeds of ~0.5–1 m/s (depending on path length) are required. Messer data: a 6 kW fiber laser can cut 25 mm mild steel at high speed, so 15 mm stainless likely needs ~6–8 kW. Achieving 1 µm tolerance on 35 µm features is extremely demanding; typical laser cutters yield tens of microns, so Arch A assumes ultra-precision mechanics and on-machine calibration.
- Risk & Notes: This is a conventional, proven architecture in high-end laser cutters. Key risks include achieving the 1 µm accuracy over large travel (requires expensive linear drives and perhaps laser interferometer feedback) and meeting BOM cost (~$150k) given high-end components.
Architecture B: Multi-Head Gantry System (Parallel Processing)
- Laser sources: Two or more moderate-power fiber lasers (e.g. two 4–6 kW lasers) feeding separate heads on a common gantry. Parallel cutting doubles throughput. Alternatively, one very high-power source split into two beams (more complex).
- Beam delivery: Each laser has its own fiber-delivered head or a split beam system. Heads are spaced on the gantry so they can cut different zones simultaneously.
- Mechanical frame: Larger or stiffer gantry to support dual heads and maintain alignment. Y-axis may be split or two independent Y-carriages on the same bridge. X-axis spans work area.
- Motion Control: Essentially two independent CNC axes sets sharing a controller. Requires synchronizing both heads and possibly sharing a master PLC to coordinate.
- Control/Software: CNC must manage two tool-heads; part nesting can assign nests to each head. Increases software complexity (multithreading paths).
- Feedback & Sensing: Each head has its own sensors (encoders, camera). Additional sensors monitor alignment between heads.
- Safety/Compliance: Similar to Arch A (common enclosure). Safety PLC must monitor both lasers and interlocks on doors.
- Automation: Similar automatic loader; may need more scrap handling capacity. Dual heads cut in parallel means scrap/small parts accumulates faster.
- Throughput/Tolerances: The parallel operation can almost double capacity (up to ~50 plates/hr if balanced). But complexity is higher. Tolerance 1 µm still applies per head; however, each head might be tuned/calibrated individually.
- Risk & Notes:
- Risks: Coordination of two heads is complex. Interference (laser plumes, heat) between heads could affect cut quality. Cost and maintenance roughly double (two lasers, two heads).
- If one head is idle, throughput drops.
- Potential mechanical challenges: stiff structure to avoid vibration coupling.
- Benefit: Throughput gain can be significant, but at cost of complexity.
Architecture C: Hybrid Gantry with High-Speed Scanner Head
- Laser source: Single medium-power fiber laser (e.g. 4–6 kW) or a dual-source (one fiber, one scanning) arrangement.
- Beam delivery: Main fiber head for general cutting; an optional galvanometer scan head on the gantry for fine features. The galvo scanner (fast mirrors) can cut small intricate shapes with extremely fast beam steering, supplementing the slower mechanical motion.
- Mechanical frame: Standard gantry. The galvo head is mounted on the moving bridge and can cover a limited field (e.g. 300×300 mm) within the larger sheet. The gantry moves the scan head to different sections.
- Motion Control: Gantry axes as Arch A; additionally, control of galvo mirrors (integrated control card). Motion coordination needed: e.g. coarse positioning by gantry, fine cuts by scanner.
- Control/Software: CNC integrates point-and-shoot instructions for the scanner. For very small features, the CNC sends scan patterns. Nesting software must separate tasks for galvo vs standard head.
- Feedback & Sensing: Galvo head needs its own focusing control (auto-focus) and process monitoring (some scanners have integrated pyrometers or cameras). Edge-finding camera may guide scanner placement.
- Safety/Compliance: Scanner beam stays within enclosure, but note: scanning lasers can be hazardous if not fully enclosed. All same safety measures apply.
- Automation: Loader/unloader as before. Scanned cuts may reduce mechanical movement time, improving effective throughput per plate for certain geometries.
- Throughput/Tolerances: The scan head can draw features (~35 µm resolution) much faster than moving the gantry, boosting cutting speed for detailed patterns. Overall throughput goal is met by combining scanner (for small shapes) and standard head (for rough cuts). A drawback: galvo fields are small, so many repositionings by gantry are needed.
- Risk & Notes:
- Risks: Integrating a galvo adds complexity: sync between gantry and scanner, calibration of both paths, managing two focal lengths. The limited scan field means large sheets still need mechanical cuts.
- Galvo heads are expensive; the benefit is higher speed on fine detail.
- Achieving 1 µm tolerance is more feasible with a scan head (which inherently has micron-level beam control), but only within its small field.
- Cost and supply risk for high-quality galvo heads (e.g. Raylase, IntelliScan).
- If not needed for many features, the extra cost may not justify the gain.
(Optional) Architecture D: CO₂ Laser System (for comparison)
- Laser source: High-power CO₂ laser (e.g. 10–12 kW). Historically common but bulky.
- Pros/Cons: CO₂ can cut thick carbon steel slightly faster and yields smoother edges on thick sheets. However, for stainless and aluminum, fiber is superior: CO₂ lasers need >6 kW for similar thickness, and they produce more heat-affected zone.
- Beam delivery: Free-space mirrors (heavier and alignment-sensitive) vs fiber (flexible).
- Cost: CO₂ lasers and optics are larger, and machines need more beam enclosure (hence bigger cabinet).
- Tolerances: CO₂ spot size is larger (worse for sub-mm features). Achieving 1 µm tolerance with a CO₂ beam is essentially infeasible.
- Risks: Lower precision, heavier maintenance (CO₂ gas handling), large footprint.
- Use Case: A CO₂ architecture is unlikely to meet the small-feature/tolerance requirements; it is included as a benchmark showing why fiber/disk solid-state is preferable.
with a comparison table and with a recommendation for architecture A – High-Power Single-Head Fiber-Gantry System.
At least 3 of these 4 architectures are indeed valid engineering-wise. We will not do a full technical analysis of the suggested architectures in this post because that is not our purpose however, architectures A, C and D are definitely to be considered and if we look at existing systems, they are some sort of a weird combination of the different structures detailed in these architectures.
Furthermore, when looking deeper into these architectures there is some kind of a mix between top-level view and the small details of the implementation however, this is a common mistake that happens all the time with System Requirements so let’s not be too strict with the model.
And furthermore, the following section was added after the suggestion for architecture A:
Technical Challenges (for Arch. A)
For the chosen architecture, key engineering challenges include:
- Submicron Precision: Maintaining 1 µm positional accuracy on a 2.5×1.5 m table under cutting loads is very demanding. Even the best linear guides and ball screws have thermal expansion and hysteresis. We must use linear motors or air bearings with interferometric feedback. Environmental temperature control (±1°C) may be needed. Calibration routines (Renishaw laser interferometer, laser tracker) are required during assembly.
- Beam Quality & Focus: To cut 35 µm features cleanly in 15 mm metal, the laser spot must be extremely stable and well-focused. We might require a beam expander or variable zoom to achieve a <50 µm spot with a good M². Thermal lensing in high power lasers can shift focus – active focus control with distance sensors is needed.
- Cut Quality & Heat Management: Thick stainless steel (15 mm) requires powerful laser (6–12 kW) and N₂ assist to avoid oxidation. High power creates heat distortion – the gantry and bed must be heavy/rigid and possibly water-cooled. Also manage smoke/fume extraction to avoid beam attenuation. (Spec: >1500 m³/h fume extractor.)
- Motion Control: Coordinating smooth acceleration/deceleration for high speeds (to meet ~1 m/s cuts) while preserving precision is complex. High-dynamic servo drives and real-time CNC interpolation (5th-order polynomials) are needed. Jerk control algorithms minimize vibration.
- Software Integration: The CNC must handle nesting optimization (to pack parts efficiently) and generate toolpaths that respect part precision. Integration with remote monitoring (IoT/Industry4.0) requires an edge computer and secure data connection (e.g. via MQTT to cloud dashboards). Implementing this adds cybersecurity and software maintenance overhead.
- Safety and Fault Handling: Continuous laser operation demands robust safety interlocks (doors, light curtains, vapor sensors). The control system must implement safety PLC logic (e.g. emergency stop circuits with Category 3 redundancy). Implementing ISO 13849-compliant architectures (dual-channel PL- e) is needed. Laser-specific standards (IEC 60825-1) mandate fail-safes and warning lights.
- Precision Cutting Control: For 1 µm tolerance, laser power stability (via closed-loop CO₂/MOPA current control) and positioning accuracy must be tightly controlled. Feedback may include monitoring cut kerf (with coaxial camera or pyrometer) to adjust focus or power in real-time. Such process sensing loops are advanced.
- Cost & Supply: Sourcing a >$50k fiber laser and precision linear axes may strain the $150k BOM. Trade-offs might be needed (e.g. slightly lower power laser or mass-produced linear modules). Long lead times (for high-end lasers, motors) are a risk.
- Automation Reliability: Automatic pallet changers and parts sorters introduce moving parts (gantries, robots) that can fail or misalign. Integrating and testing this subsystem (often from 3rd-party vendors) is a project risk.
Main Component Vendors and Customization Needs
Based on the architecture above, key subsystems and example vendors:
- Laser Source:
- Vendors: IPG Photonics, nLIGHT, TRUMPF (TruDisk), Coherent, JPT/Raycus (Asia).
- Notes: Fiber lasers (~6–12 kW) are modular (IPG G-series, nLIGHT HP series). Choose based on cost and support. If aluminum cutting is significant, consider a green fiber laser (IPG Green Laser) – but these are very expensive and might be beyond scope.
- Customization: Standard off-the-shelf lasers suffice. Custom research only for extremely high brightness (e.g. 20 kW) which is likely unnecessary.
- Laser Cutting Head / Optics:
- Vendors: RayTools, Precitec, Bin Han (OEM heads), or IPG’s LiteCut head.
- Notes: Heads include adjustable-focus lens and nozzle. RayTools and Precitec offer autofocus and collision protection. Nozzle designs may be customized for 1 µm precision (e.g. special pinhole).
- Customization: Possibly custom-designed beam expander or internal adaptive optics if beam quality correction needed for micro-features – an advanced R&D area.
- Motion Mechanics (Rails/Guides/Frame):
- Vendors: Bosch Rexroth (linear guides and racks), THK or HIWIN (ball screws or linear guides), IAI/HIWIN/Aerotech (linear motors).
- Notes: To achieve sub-µm, linear motors (Aerotech, IAI) or high-precision ball screws with dual-nut (THK, Nabtesco) and Heidenhain linear scales should be used. Gantry frame welded steel (or cast iron) with granite table top is typical.
- Customization: Likely custom design of large welded frame and gantry. Buying pre-assembled modules (like Aeotec or Guericke machine beds) is possible but integration is needed. Ensuring thermal stability (maybe water-cooled bed) is a design point.
- Drives and Motors:
- Vendors: Siemens (1FK series motors), Beckhoff AM8000 series, Kollmorgen, Yaskawa, Panasonic.
- Notes: Use digital servo drives (EtherCAT-based) integrated with control. If linear motors chosen, need water-cooled linear motor drives.
- Customization: Standard selection; no major R&D. But careful sizing for acceleration and precision.
- Control Electronics (CNC/PLC):
- Vendors: Beckhoff TwinCAT (PC-based with EtherCAT I/O), Siemens (Sinumerik or S7+Simatic CNC), Fanuc, Mitsubishi. For safety PLC: Pilz PNOZ or Siemens S7 Failsafe.
- Notes: Beckhoff offers flexible PC-based control (similar to TRUMPF’s Linux IoT box), including TwinCAT CNC. Siemens Sinumerik is used in many lasers (Bystronic, Mazak).
- Customization: Possibly need custom CNC macros for nesting and scanner control. No bleeding-edge R&D, but careful software development.
- Sensors:
- Encoders: Heidenhain or Renishaw linear scales for axes. (Example: Renishaw RESOLUTE for µm resolution).
- Vision: Basler or Cognex cameras for edge-finding; Keyence laser displacement sensors for focus/distance.
- Gas and Flow: Parker Hannifin or Beko regulators for N₂; Teledyne Dalsa flow sensors for gas.
- Environmental: Temperature/humidity sensors (siemens, Honeywell) if needed for compensation.
- Customization: Possibly develop a vision algorithm for edge detection if automated alignment is desired.
- User Interface / Software:
- HMI: Beckhoff CP series panel PCs, Siemens operator panel, or custom Windows/Qt interface.
- Nesting & CAM: SigmaNEST, Lantek, ProNest, Autodesk TruNest, or open-source DeepNest. Choose based on budget.
- IoT Cloud: AWS IoT or Azure IoT edge (as TRUMPF did), or simpler OPC UA data logging.
- Customization: Software integration for CNC+nesting may need custom CAM post-processor. IoT stack development is a software engineering effort (Docker, cloud, security).
- Safety Systems:
- Vendors: Pilz, SICK, Allen-Bradley for safety PLC and light curtains. (E.g. Pilz PNOZ/multi for E-stop; SICK Flexi Soft for interlocks).
- Customization: Safety software (e.g. sensor integration logic) will be custom. Meeting ISO 13849 requires calculation of MTTF and architecture categories.
- Cooling & Ventilation:
- Vendors: Heat exchangers by Hiflor (Hifkor), Lytron for laser diode cooling; Nederman or Donaldson for fume extraction.
- Notes: Laser diodes require water chiller (10–20 kW), often from IPG or Lytron. Metal cutting fumes require a high-capacity fan/filter (Dana or TRUMPF filter).
- Customization: Sizing ductwork and custom hood over cut area is needed, but standard engineering.
- Material Handling (Automation):
- Vendors: TRUMPF (LiftMaster series), LVD (AutoLoad), Mecal or Cutlite Penta. For smaller scale, local gantry robot with vacuum gripper.
- Customization: Integrating a third-party loader often needs software and possibly mechanical adaptation to the specific machine.
Items Requiring Special R&D or Customization: Achieving 1 µm accuracy on a large machine is beyond typical off-the-shelf specs, so expect research on:
- Advanced calibration procedures (laser interferometer systems for on-the-fly axis calibration).
- Custom cutting head design (beam shaping for minimal kerf).
- Novel software for active feedback (monitoring cut kerf or plasma plume to adjust focus/power).
- Possibly development of a proprietary nesting algorithm optimized for both cut speed and feature precision.
Top-Level Requirements (by Discipline)
Mechanical & Structural
- Work Envelope: ≥2500 mm (W) ×1500 mm (L) ×15 mm (T) maximum plate. Gantry travel must cover at least these.
- Frame/Stability: Rigid welded steel/iron frame with high natural frequency; steel plate thickness ~80–100 mm or granite table. Thermal expansion <5 µm/m·K.
- Axes:
- X-axis travel ≥2600 mm, Y-axis ≥1600 mm (to ensure full coverage).
- Z-axis stroke ~100 mm (auto-focus range).
- Motion Elements:
- Linear motors or precision ballscrews with 2nd nut (THK/Bosch Rexroth) on X/Y. Y-axis may be dual rails for stability.
- Linear guide rails (e.g. THK, Hiwin) and recirculating bearings for smooth motion.
- Actuators sized for accelerations ~5 m/s² (for fast cuts), payload (laser head ~20 kg).
- Precision/Accuracy:
- Positioning repeatability ≤1 µm (verified by encoders). Straightness <5 µm over travel.
- Thermal management: integrated chillers or heat pipes on frame if needed.
- Material Handling:
- Pallet system: Holds stack of ~25 plates, auto index. Materials: steel pallets.
- Fixture/Clamping: Pneumatic vacuum or mechanical clamps for each plate.
- Exhaust: Integrated hood or snorkel to capture smoke; connectors for duct to fume extractor.
- Housing:
- Enclosed cabinet (Class 1 laser), with windows made of appropriate OD (optical density).
- Dust-tight doors with interlocks; adequate space around machine for maintenance.
Optics & Laser
- Laser Source:
- High-power fiber laser (≥6 kW) or equivalent solid-state. Wavelength ~1070 nm (for steel, Al).
- Beam quality: M² <1.3 for tight focus. Energy stability ±1%. Warm-up <30 min.
- Beam Delivery:
- Fiber cable (e.g. 15–20 m) with protective covering. Quick-disconnect connectors for head changeover.
- Cutting head: Adjustable lens system (e.g. f-theta lens). Beam expander if needed for small spot.
- Assist Gas:
- Primary: Nitrogen (to prevent oxidation, required for stainless/aluminum). Gas consumption ~300–500 L/min at 15 MPa for 12 kW cutting (flow control needed).
- Secondary: Oxygen (for mild steel to speed up; optional).
- Focusing:
- Motorized Z for focus; range ~ ±50 mm. Accuracy ±5 µm.
- Nozzle changer: Interchangeable nozzles for different kerf widths (e.g. standard and fine-cut nozzles).
- Safety Optics:
- Beam shutter (fast turn-off) and dump. Laser head with beam-protective glass window (with auto-flap) to protect lens.
Electronics & Control
- CNC/PLC:
- Controller: Real-time PC (Beckhoff C6920) or CNC controller (Siemens Sinumerik 840D) with multi-axis motion modules.
- I/O: EtherCAT or PROFIBUS network for drives and I/O. ~200 I/O points (motors, sensors, valves).
- Safety PLC: Independent safety controller (Pilz PNOZ or Siemens F-CPU) managing E-stops, door interlocks, and laser-enable.
- Drives:
- Servo drives for each axis with digital feedback (e.g. Beckhoff AM8000 series).
- If linear motors: water-cooled linear drives (e.g. Kollmorgen).
- Sensors:
- Absolute linear encoders (Renishaw or Heidenhain) on each axis (10 nm resolution).
- Laser power monitor diode. Temperature sensors on laser head. Gas flow meters.
- Emergency physical limit switches on home/home-return.
- Electrical:
- Enclosure: IP54 or better. Electrical cabinet with HVAC.
- 3-phase power, ~100 A service for laser and drives (to be calculated precisely).
- Software:
- Real-time OS (TwinCAT or dedicated CNC). HMI PC (Windows/Linux) for operator station.
- CNC software: Accepts DXF/G-code, tool library for cutting parameters (speed, gas).
- Nesting: Integrated or offline nesting software. Capable of optimizing 2D layouts for multiple parts.
- Data logging: Records production metrics, fault logs, for up to 1 year (for traceability).
- Remote Monitoring: Ethernet/Wi-Fi for connection to cloud or VPN. Implements OPC UA or REST API for status queries.
Software & Interfaces
- User Interface:
- Touchscreen HMI (10–15″) at machine for job setup, status, manual jog.
- Desktop/Web interface for remote monitoring (dashboard, KPIs).
- Intuitive UI for part nesting previews, cutting simulation. CNC-Compatible (ISO G-code).
- Nesting & CAM:
- Software must handle arbitrary 2D shapes (DXF, DWG, native drawings). Supports common file formats.
- Automatic nesting engine (minimize waste) with manual override.
- Generate lead-in, lead-out paths, and microcuts for corners (to ensure quality on small features).
- Maintenance Software:
- Diagnostics mode (sensor status, encoder signals).
- Calibration routines (e.g. laser auto-calibrate using test patterns).
- Alert system: Automatic notification (email/SMS) for errors (e.g. lens dirty, axis overload, door open, etc.).
- Network:
- Onboard industrial PC with firewall. Connect via Ethernet (Profinet/EtherCAT) to drives.
- IoT Gateway (e.g. OPC UA server) for SCADA integration.
Safety & Compliance
- Standards:
- CE Mark (EU), UL/cUL (US/Canada). Conform to Machinery Directive 2006/42/EC, EMC Directive, Low Voltage.
- Laser Safety: IEC 60825-1 (Class 1 enclosed system). Interlocks on all access panels. Emergency stop latches machine state.
- Safety PLC: Design to ISO 13849-1 Category 3 (or 4 if double-channel), PL e for Emergency Stop and critical interlocks.
- Risk Assessment: Perform per ISO 12100. Provide safety manual and markings per EN standards.
- Operator Protection:
- Laser-proof viewing window (OD appropriate for 1 µm).
- Safety light curtains or mats not needed if fully enclosed, but doors must auto-stop laser if opened.
- Warning lights (red/green) for laser-on status.
- Ergonomics:
- Controls at ergonomic height. Good lighting inside machine when door open.
- Compliant guarding on moving motion parts (axes covers).
- Environmental:
- No special climate control mandated, but indoor ambient 15–30 °C, <70% RH recommended for accuracy.
- If required, shop floor cleanliness to avoid dust on optics. Filters on intake.
- Electrical Safety:
- Main disconnect switch accessible. Grounding to <0.1 Ω. Residual-current device (RCD) for 30 mA leakage.
with some additional follow-up questions which can be found here in the full response document and there was some additional interaction with the chat further down to clarify some small points. Let’s face it, the logic flow of the GPT model as this stage was indeed to the point.
However, our aim is, eventually, to get to a go-to block diagram and a System Requirement document. When specifying to the model exactly what output to provide the model created 4 documents attached below
- ChatGPT Management Summary
- ChatGPT Requirements Traceability Matrix
- ChatGPT Risk Register
- ChatGPT System Block Diagram
To get an output of such low-level documents as these, after the first bit which was very detailed and seemingly professional, was no less than a complete anti-climax. Taking the output as is is definitely not good. As a starting point it is a very poor one for further discussion with the model.
Claude’s Flow and Outcome
Very much like GPT, Claude immediately asked some follow-up questions.
- Production requirements: What are the expected throughput rates (parts per hour) and duty cycle requirements? Will this be for prototype/small batch production or high-volume manufacturing?
- Precision and quality requirements: Beyond the 35µm smallest feature, what are the edge quality requirements, cutting speed expectations, and acceptable heat-affected zone (HAZ) specifications?
- Operating environment: Will this operate in a controlled factory environment or need to handle varying conditions? What are the power supply constraints (voltage, phases available)?
Once again, very much like its competitor, Claude asked some solid questions that are crucial for the initial system design.
After a quick Q&A the output of Claude was as follows:
Architecture Comparison Overview
Based on comprehensive analysis of current industrial systems from leading manufacturers (Trumpf, Bystronic, Mazak, Prima Power), three primary architectures were evaluated against the demanding specifications requiring 35µm feature resolution and ±7µm edge quality at production speeds of 3-5 m/min.
Architecture Technical Analysis
Flying Optics Systems demonstrate superior performance for the specified requirements through several key advantages. The stationary table configuration eliminates workpiece weight limitations while enabling 25-40% faster cutting speeds on complex geometries. Modern systems like the Mazak Optiplex Nexus achieve 20µm feature resolution with Variable Beam Parameter technology, directly meeting the 35µm specification with significant margin.
Gantry-Based Systems offer proven reliability and excellent thermal stability through granite construction, but lower dynamic performance limits throughput potential. While capable of achieving ±5µm edge quality, the increased structural mass results in slower acceleration and reduced efficiency on complex contours.
Hybrid Cantilever Systems provide 3D capabilities unnecessary for this flat-sheet application while introducing higher complexity and maintenance requirements. The throughput limitation of 18-34 plates/hour fails to meet the 25 plates/hour target consistently.
Recommended Architecture: Flying Optics Fiber Laser
Primary Recommendation: Mazak Optiplex Nexus 3015 Fiber S7 (7kW) Configuration
Technical Justification:
- Precision Excellence: 20µm feature capability with ±5µm edge quality achievement
- Throughput Leadership: 35-45 plates/hour realistic capacity with automation
- Speed Advantage: 30% faster complex geometry processing vs. gantry systems
- Operational Flexibility: Variable sheet sizes without reconfiguration requirements
- Maintainability Balance: Optimal complexity-to-performance ratio
Key Technical Features:
- Variable Beam Parameter technology for material-optimized processing
- 2D flying optics with dual pallet automation system
- Working envelope: 3000 x 1500mm minimum capacity
- Advanced beam shaping for thickness-optimized cutting
- EtherCAT-based integrated motion control
with a comparison table
What Claude did in this case was quite straight-forward: it found online a few similar systems “(Trumpf, Bystronic, Mazak, Prima Power)” and explored their architecture, as much as possible from the available data, of each system’s strengths and contribution to the suggested architecture. BTW, all 3 architectures are technically valid.
The recommended architecture is directly based on an existing product the Mazak Optiplex Nexus 3015 Fiber S7 which you may consider as a foul if it weren’t for the following paragraphs. Claude provided the following sections as complementary data, very similar to the detailed sections ChatGPT model provided, which took its answer to the next level.
Precision Requirements Challenge Matrix
Achieving 35µm Features with ±7µm Edge Quality represents the primary technical challenge, requiring coordinated solutions across multiple system domains. The physics fundamentals impose diffraction limits based on beam quality (M² factor) and thermal effects from high-power density processing.
Critical Challenge Areas and Mitigation Strategies
Beam Quality Maintenance Across Large Envelope
- Challenge: Optical path variations causing 50µm+ focus drift over 2.5m travel
- Root Cause: Thermal gradients and mechanical deflections in beam delivery system
- Mitigation: Fiber laser delivery eliminates beam path optics, maintains consistent M² < 1.1 across full envelope
- Implementation Cost: $3,000-8,000 for advanced fiber delivery system
Motion System Precision for Complex Geometries
- Challenge: Corner velocity limitations causing acceleration artifacts affecting edge quality
- Physics Limitation: Dynamic compliance variations with direction changes
- Solution: Linear motor systems with 1nm encoder resolution and active yaw control
- Advanced Control: Sky-writing algorithms enabling constant velocity through corners
- Performance Target: ±7µm accuracy achieved through dual-motor gantry with independent encoders
- Cost Impact: $30,000-50,000 premium for precision motion systems
Thermal Management for High-Power Operation
- Challenge: Continuous 6-7kW operation generating >1000 W/cm² heat flux
- Advanced Cooling Solutions:
- Microchannel liquid cooling: 50-200µm embedded channels providing 5-10x improvement
- Phase change material cooling: 40% energy reduction for transient thermal loads
- Heat pipe thermal spreading: >50,000 W/m·K conductivity for zero-energy equalization
- Implementation Strategy: Combination approach targeting critical components
- Budget Allocation: $20,000-35,000 for advanced thermal management
Material-Specific Process Optimization
- Aluminum Challenges: High reflectivity requiring +30% power levels, oxidation tendency
- Optimized Parameters: High-purity nitrogen (99.999%) at 1.5-2.0 MPa pressure
- Stainless Steel: Mid-thickness focus positioning, 105 Hz frequency optimization
- Real-time Adaptation: AI-based parameter adjustment achieving 25% efficiency improvement
System Integration Critical Success Factors
Multi-Domain Coordination requires precise synchronization between laser modulation, motion control, and gas delivery systems. EtherCAT communication provides <1ms cycle times enabling real-time parameter optimization based on cutting position and material conditions.
Vibration Control and Dynamic Performance achieved through granite machine base providing superior damping characteristics and air-bearing linear stages eliminating mechanical friction effects.
Vendor Analysis and Component Sourcing
Laser Source Vendor Analysis
Primary Recommendation: IPG Photonics YLS-6000 (6kW Fiber Laser)
- Technical Specifications: M² < 1.1 beam quality, ±2% power stability, >50,000 hour service life
- Cost Analysis: $45,000-65,000 depending on configuration
- Justification: Industry-leading reliability with global service network
- Beam Quality: Optimal for 35µm feature requirements with 15-25µm achievable spot sizes
Alternative Options Analysis:
- Coherent Systems: 10-15% premium, excellent for multi-vendor sourcing strategy
- Trumpf TruFiber: 20-30% premium but complete system integration advantages
- Raycus (Cost-Effective): 30-50% cost reduction, suitable for budget-constrained applications
Motion Control System Vendors
Recommended Configuration: Beckhoff TwinCAT with Kollmorgen Linear Motors
Motion System Components:
- Linear Motors: Kollmorgen LMCA series with ±7µm accuracy capability Tips
- Servo Drives: Beckhoff AX5000 series with EtherCAT integration Blog
- Encoders: Renishaw FORTiS enclosed linear encoders (1µm/m accuracy)
- System Cost: $40,000-60,000 for dual-axis precision system
CNC Control Platform: Beckhoff TwinCAT 3
- Advantages: Open architecture, PC-based control, excellent price/performance ratio
- Cost: $15,000-25,000 for complete software and hardware package
- Features: TF5290 Cutting Plus package with integrated laser control functions
Optical Components Sourcing
Cutting Head Selection: Raytools BM114 Series
- Power Capacity: 6kW fiber laser compatibility
- Cost: $12,000-18,000 (optimal value positioning)
- Features: EtherCAT communication, collision protection, automatic focus control
- Alternative: Precitec ProCutter Thunder ($25,000-45,000) for premium applications
Beam Delivery System Components:
- Fiber Delivery: $3,000-8,000 for complete system
- Protective Optics: $500-2,000 annual replacement budget
- Focus Control: $5,000-15,000 for automated systems
Support Systems Vendor Analysis
Gas Delivery Systems
- Nitrogen Generation: Atlas Copco PSA generators ($50,000-150,000)
- ROI Analysis: 1-2 year payback vs. bottled gas supply
- Purity Requirements: 99.95% minimum for stainless steel applications
Fume Extraction Systems
- Recommended: BOFA or Fumex industrial systems ($15,000-40,000)
- Filtration: HEPA + activated carbon for comprehensive fume management
- Capacity: 1,000-10,000 m³/h depending on machine size
Cost Optimization for $150K BOM Target
Recommended Budget Allocation:
- Core Laser System: $35,000 (Raycus 6kW fiber laser)
- Motion Control: $40,000 (Beckhoff + Kollmorgen linear motors)
- Cutting Head & Optics: $12,000 (Raytools BM114)
- Structural/Mechanical: $18,000 (fabricated frame assembly)
- Control Systems: $20,000 (Beckhoff TwinCAT complete system)
- Integration & Assembly: $15,000 (engineering and testing)
Total Core System: $140,000 (within BOM target)
Additional Systems (Phased Implementation):
- Nitrogen Generator: $75,000 (Year 1 upgrade)
- Advanced Fume Extraction: $25,000
- Safety & Automation: $20,000
Strategic Cost Optimization:
- Component Standardization: Mix premium performance-critical components with value alternatives
- Global Sourcing: Leverage Chinese laser sources while maintaining Western motion control quality
- Modular Design: Enable future capability upgrades without complete system replacement
The process Claude presented was more target-driven than OpenAI’s but it did answer a lot of technicalities for the first stages of system design.
The complete doc here.
In addition the following block diagram was created by Claude at this stage:

Again, without doing a full technical analysis of the result, the overall outcome as a whole is a very good starting point.
At this point the Claude model was requested to create a System Requirements document, which created the following output:
Claude System Requirements
This is definitely a very impressive result! Aside from the fact that no block diagrams or flow charts appear in the document, it has nearly everything that is a must in a sysreq document. The fact that it even included a verification method column in section 5.5 left me jaw-dropped for a few minutes. There is an executive summary section, architecture comparison, technical challenges, cost review and of course the System Requirements table itself.
In a sense Claude created some kind of a combination between preliminary architecture review, top-level Product Requirements, top-level System Requirements and drill-down to subsystems and sub-modules. Claude found a lot of engineering-good-practices to fit into the requirements which were relevant and surprisingly accurate for example power-on of 15 minutes and so on.
As a starting point this document is very satisfactory.
My original plan was to compare the output documents of these two models at this point of this post, but since there is an undisputed winner, I will skip this comparison.
Consolidation and Advanced Input
Now we get to the most interesting bit. Providing we have enough info from the different AI models topped with the human input how well an AI will put everything together.
To keep it fair with Claude and ChatGPT models I turned also to Gemini 3 Flash and DeepSeek V3 for the next task: the 3 meaningful outputs created by OpenAI, Claude and yours truly were given as input to all 4 AI models in their chat mode to create a joint document. In case of contradictions between the 3 input documents there is a clear order of reliability for the docs according to the amount of trust in their content.
This was the original prompt and a few more comments were required to make it a better outcome from all models:
act as a multidisciplinary system engineer. please create me a document that takes the input of these 3 file attached to be a system requirements document. the document should not include contradictory clauses logically or engineering wise i.e. if contradiction exists in certain sections the order of importance of the docs is as follows (descending order from most reliable to least): sysreq example, Claude-SRD… and lastly ChatGPT.
To get the following output files eventually:
- ChatGPT Consolidated SRD
- Claude Consolidated SRD
- Gemini Consolidated Sysreq
- DeepSeek Consolidated Sysreq
The funny thing was the amount of apologizes and pats on my back (and my ego) I got from the different models during this particular part of the experiment: “You’re right to push on this”, “You are absolutely right”, “I apologize for the confusion”. Thank you very much, I needed the morale boost! ![]()
The outputs of ChatGPT and Gemini are not up to standard. The output remained very lean even though I kept instructing the models over and over what to detail with and how. You may argue as to how well was the initial prompt was phrased however, the fact remains that DeepSeek gave adequate results and Claude reached an impressive result, all started with the same prompt.
In any case, all the models except Claude needed direct assistance after which the results were slightly better or much better. Claude model’s initial result was very close to the result I present here which tells me that out of the 4 models, Claude’s training material included enough related data input.
One of the main challenges in all the outputs of the different AI models is the models’ tendency to tl;dr a lot of the valuable contents for us. As DeepSeek model replied apologetically to me at one point “My instruction was to create a consolidated requirements document without contradictions, not to reduce or simplify the requirements”. In other words, all 4 models took the liberty to decide what’s important for us and what’s not as you would expect from a system engineer – but lack the correct judgment what indeed had to be omitted and what not. All including Claude, that decided in the first version to omit everything that has to do with the architecture selection.
There is an important bottom line here. The output level of Claude was far superior compared to the other models and may even be used as a viable reference when and if decided to be employed. Nevertheless, its output still needs a professional eye to verify that no hallucinations crept into the requirements and descriptions.
AI vs. Human
Taking the Claude input as our pivot, Claude would have gotten to a working product eventually. But this eventually would have required an assisting hand of someone to direct the AI model to the right direction.
However, how human engineers are different? We work in teams where each engineer holds a certain expertise and contribute to the team’s effort and progress. So, the AI model needs its own team beside it as well – a human engineer to guide it through the different steps. Furthermore, if it were a real case any mechanical engineering related input would have been checked by a Mechanical Engineer, SW input by a SW Engineer etc. very much like any sysreq I would have written which was reviewed separately or in Design Review (CDR \ PDR or alike).
So, the bottom line here is that the AI model does not know it all yet and needs its human assistants to watch over it exactly like we humans that require other humans watching over us.
To put it straight without any pink glasses for either side: the AI models need supervision exactly like humans need supervision – only the method and frequency of supervision change between us.
The main issue here is that the AI models do not tell us “this is where my knowledge stops” – they’d just give the output with whatever mistakes and non-realistic data then leaves it to us mortals to find the mistakes.
One solution for this is giving the output of a certain AI model as input to another but in our specific case the outcome was not good.
So, yes, we still need the human in the loop but we cannot write it as a downside for the AI only because we need the same human engineers in our own loop as well. If we consider the AI model as a team-member that would psychologically make it easier for us to accept the need to go over its outputs and correct it.
Summary
Nowadays, it is clear that most engineering disciplines live side-by-side with AI models and are not replaced by them. For the time being at least and yes, I also refer to SW engineering.
As much as Claude’s results were impressive, we clearly see it needed an assisting human hand. Don’t get me wrong, I use the different AI models for nearly everything I do in my day-to-day work, but with an uncompromising scrutiny.
It is interesting in this context to take a look at this article where Ford rehired veteran (human
) engineers not to eliminate the AI but to reinforce it when the AI models could not handle the tasks solo.
Specifically for System Engineering and putting aside a System Engineer’s personal creativity for a second, one of the major advantages of a human System Engineer is his\her experience. The more systems and subsystems you see in your career the better understanding you have and your ability to avoid systemic mistakes grows.
Most commercial companies and R&D departments do not publish their system architecture nor any other engineering documents. This fact slows down the AI models’ learning curve of complex system architectures, so a veteran System Engineer has the advantage – for now. The more data is leaked to the internet and the deduction capabilities of the different AI models grows this gap will be closed and System Requirements provided solely by an AI will be more and more concise and complete.
That said, the creativity of a non-deductive chain-of-thought cannot be replaced by current evolving models and that’s before we mention the Model Collapse phenomenon which is bound to happen sooner or later if the models are kept training over the same data with no outlier data added. Revolutions like the airplane by the Wright Brothers or the Theory of Relativity by Albert Einstein probably couldn’t happen when a statistical model gives you the most probable outcome.
Nevertheless, I am no prophet
who knows that the future holds for us mortals?

