[Paper] Utilizing LLMs for Industrial Process Automation

Source: arXiv - 2602.23331v1

Overview

The paper investigates how Large Language Models (LLMs) can be harnessed to streamline industrial process automation, a niche where developers work with proprietary, domain‑specific languages (DSLs) rather than mainstream languages like Python. By tackling real‑world tasks—such as auto‑generating robot‑arm movement scripts—the study shows that LLMs can cut development time and reduce the expertise barrier in manufacturing software pipelines.

Key Contributions

• Domain‑specific LLM fine‑tuning for several widely‑used industrial automation DSLs (e.g., RAPID, Structured Text).
• End‑to‑end integration pipeline that plugs an LLM into existing PLC/robotic development environments.
• Benchmark suite of realistic automation tasks (trajectory planning, safety‑check code, HMI screen generation) with quantitative speed‑up and quality metrics.
• Human‑in‑the‑loop evaluation involving automation engineers, demonstrating that LLM‑suggested code is accepted after minimal review in >80 % of cases.
• Guidelines & best‑practice checklist for safely deploying LLM‑assisted code generation in safety‑critical manufacturing settings.

Methodology

1. Data Collection – The author gathered a corpus of ~150 k lines of code from proprietary automation projects (with anonymization), covering multiple vendor‑specific languages.
2. Model Adaptation – A base LLM (GPT‑3.5‑style) was further pre‑trained on the collected DSL corpus, then instruction‑tuned using a set of “prompt‑response” pairs that mimic typical engineer queries (e.g., “Create a pick‑and‑place routine for a 6‑DOF arm”).
3. Toolchain Integration – A lightweight plugin was built for popular IDEs (TIA Portal, RobotStudio) that forwards developer prompts to the fine‑tuned model and injects the returned code directly into the project.
4. Evaluation – The system was tested on 30 benchmark tasks. Metrics included:
   • Generation time vs. manual coding,
   • Syntax/semantic correctness (automated static analysis + simulation),
   • Engineer effort (seconds spent reviewing/editing).
5. User Study – 12 automation engineers used the tool over a two‑week pilot, providing qualitative feedback on trust, usability, and safety concerns.

Results & Findings

Key takeaways: the fine‑tuned LLM reliably produces syntactically correct DSL code, and engineers spend far less time polishing the output. The few failures were traceable to ambiguous prompts or edge‑case hardware configurations, both mitigated by the suggested best‑practice checklist.

Practical Implications

• Faster prototyping – Engineers can prototype robot motion or PLC logic in seconds, accelerating proof‑of‑concept cycles and reducing time‑to‑market for new production lines.
• Lower skill barrier – Junior staff or cross‑functional teams can generate correct automation scripts without deep vendor‑specific language expertise, easing staffing constraints.
• Continuous integration – The LLM plugin can be embedded into CI pipelines to auto‑generate boilerplate code for recurring patterns (e.g., safety interlocks), ensuring consistency across projects.
• Cost savings – Early data suggests up to a 30 % reduction in engineering hours for routine automation tasks, translating into measurable ROI for manufacturers adopting the approach.
• Safety compliance – By coupling LLM output with static analysis and simulation checks, companies can maintain compliance with IEC 61508/IEC 62443 standards while still benefiting from AI assistance.

Limitations & Future Work

• Proprietary data constraints – The study relied on anonymized internal code; replicating results in other firms may require similar data access, which can be a legal or logistical hurdle.
• Domain coverage – Only a subset of popular automation DSLs were evaluated; extending to niche controllers or legacy ladder‑logic systems remains open.
• Safety assurance – While static checks catch many errors, the authors acknowledge that formal verification of LLM‑generated code for safety‑critical loops is still an open research problem.
• Prompt engineering – The quality of output heavily depends on well‑crafted prompts; future work will explore UI‑driven prompt templates and interactive clarification loops.
• Scalability – Real‑time generation for large, multi‑module projects may strain inference latency; investigating model distillation or edge‑deployment strategies is planned.

Bottom line: This research demonstrates that, with careful fine‑tuning and tooling, LLMs can become a practical co‑developer for industrial automation, promising faster development cycles and broader accessibility of complex manufacturing software.

Authors

• Salim Fares

Paper Information

• arXiv ID: 2602.23331v1
• Categories: cs.SE, cs.AI
• Published: February 26, 2026
• PDF: Download PDF