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Design-to-Plan: A Large Language Model-Based Multi-Agent Framework for Manufacturing Process Planning from 3D CAD Models and 2D Engineering Drawings

Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng, Seung Ki Moon

arXiv:2608.24039v1cs.ROcs.AI

TL;DR

Existing literature lacks integration with downstream manufacturing planning and the design-to-manufacturing reasoning chain. Design-to-Plan presents an end-to-end agentic framework connecting heterogeneous design artifacts to manufacturing planning; its parallel architecture improves robustness, coverage, and computational efficiency.

  • Problem

    Existing literature lacks integration with downstream manufacturing planning and the design-to-manufacturing reasoning chain.

  • Method

    Design-to-Plan is an end-to-end agentic framework whose Context Fusion Agent bridges components in the design-to-plan workflow.

  • Results

    The parallel architecture improves robustness, coverage, and computational efficiency, while the sequential architecture provides stronger performance in quality-oriented reasoning.

  • Takeaways & Limitations

    Design-to-Plan connects 3D CAD feature recognition and 2D engineering drawing information within an end-to-end manufacturing planning framework.

  • Takeaways & Limitations

    Future development must move beyond prompt-level design toward more reliable harness engineering mechanisms for robust industrial deployment.

Abstract

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Manufacturing process planning transforms heterogeneous design information into coherent manufacturing decisions. However, existing approaches focus on isolated subtasks, such as feature recognition, drawing interpretation, or tool selection, and struggle to support the full reasoning chain from design artifacts to process plans. This is critical when planning must interpret 3D CAD models, 2D engineering drawings, materials, and domain-specific rules. To address this gap, this paper presents Design-to-Plan, a large language model (LLM)-based multi-agent framework for end-to-end manufacturing process planning. An orchestrator coordinates specialized agents for 3D feature recognition, 2D drawing analysis, 2D-3D context fusion, knowledge retrieval, process sequencing, tool selection, and report generation. Rather than using LLMs as standalone text generators, the framework deploys them as reasoning agents that interact with deterministic modules and knowledge sources to produce consistent and traceable decisions. In this hybrid design, deterministic modules and specialized agents extract structured information from CAD and drawing inputs, while LLM agents perform context-aware reasoning, retrieve manufacturing rules, resolve conflicts, and generate planning outputs. The framework is evaluated using 300 benchmark cases across three downstream ReAct-enabled agents, plus separate evaluations of CAD feature recognition, drawing analysis, and 2D-3D context fusion. The parallel architecture achieves 100% success across downstream agents, Tool F1 scores of 95.9%-97.6%, 90% source detection accuracy in conflict analysis, and a 60%-68% reduction in token usage for key planning tasks. Results show that structured LLM-based multi-agent coordination can bridge design representations and manufacturing knowledge, enabling scalable, efficient, and traceable design-to-plan automation.

1. Introduction

Manufacturing planning must integrate heterogeneous design information and domain constraints, but prior methods largely address isolated subtasks. Design-to-Plan introduces a coordinated hybrid multi-agent workflow linking design artifacts to executable planning outputs.

  • Research gap: Existing approaches often isolate feature recognition, drawing interpretation, or rule-based planning rather than connecting the full manufacturing reasoning chain.These solutions remain disconnected from downstream knowledge retrieval, process recommendation, and manufacturability analysis.
  • Motivation: LLM-powered multi-agent systems offer coordinated workflows in which specialized agents exchange information, invoke tools, and retrieve domain knowledge.This capability is relevant to planning across heterogeneous design representations, interdependent stages, and domain-specific constraints.
  • Research gap: Manufacturing applications of LLM-based multi-agent systems remain early-stage, with limited inter-agent communication, workflow coordination, and end-to-end evaluation.Existing studies are described as largely conceptual or task-specific.
  • Proposed framework: Design-to-Plan connects heterogeneous design artifacts with executable manufacturing process planning through six specialized agents exchanging structured information asynchronously.The agents perform feature extraction, context fusion, knowledge retrieval, process sequencing, tool selection, and report generation.
  • Evaluation: The framework is evaluated end to end across diverse input-complexity scenarios, including sequential and parallel ReAct architectures and conflicting knowledge sources.The reported contributions emphasize structured data exchange and traceable intermediate outputs.
  • Proposed framework: The hybrid deterministic-agentic workflow assigns well-defined perception tasks to deterministic modules and uses LLM agents for reasoning, retrieval, conflict resolution, and decision synthesis.Structured coordination supports state persistence, thread tracking, asynchronous information flow, and consistent, traceable, executable outputs.

2. Literature Review

Manufacturing intelligence has advanced across control, process planning, LLM applications, feature recognition, drawing analysis, and tool selection, but these capabilities remain insufficiently integrated. The literature motivates a fully implemented framework that fuses design representations with downstream planning.

  • Manufacturing multi-agent systems: Earlier manufacturing multi-agent systems mainly addressed flexible control, scheduling, shopfloor coordination, and distributed production management.These architectures demonstrated distributed task allocation and improved adaptability and fault tolerance compared with conventional approaches.
  • Manufacturing multi-agent systems: More recent systems exchange engineering constraints, coordinate digital twins, or support STEP-NC planning, but most rely on predefined rules and static knowledge representations.Their scope remains concentrated on coordination, control, or restricted component classes rather than end-to-end planning from design artifacts.
  • LLMs for manufacturing: LLM-based manufacturing applications cover design understanding, tolerancing, material selection, manufacturability analysis, process planning, and quality control.Most approaches still use single LLMs without structured tool use, multi-source knowledge integration, or coordinated agent interaction.
  • LLMs for manufacturing: General-purpose multi-agent frameworks provide role specialization and structured communication but require manufacturing-specific tools, knowledge sources, and traceable workflows.Existing systems therefore do not provide fully implemented end-to-end solutions spanning perception, context fusion, retrieval, sequencing, and tool selection.
  • Feature and drawing extraction: CAD feature-recognition research has progressed from rule-based and voxel methods to graph-based, topology-aware, and vision-language approaches.Vision-language models address complex geometries but are reported to have higher computational cost and reduced geometric precision.
  • Feature and drawing extraction: Engineering drawings add tolerances, surface finishes, and manufacturing notes, while document-understanding pipelines detect and parse these annotations using OCR, deep learning, and layout-aware models.Hybrid detection-and-parsing pipelines decompose extraction into subtasks to improve robustness.
  • Feature and drawing extraction: A central limitation is that feature and annotation outputs are rarely linked to corresponding 3D features or downstream knowledge retrieval, process planning, and tool selection.Tolerance and surface-finish information becomes meaningful for manufacturing decisions when associated with specific CAD features.
  • Process planning and tool selection: Variant and generative CAPP methods, knowledge-based systems, LLM planning approaches, and tool-selection methods each address parts of the planning pipeline.Template-based and manually engineered systems struggle with diverse materials, geometries, incomplete information, and requirements outside predefined templates.

3. Framework Architecture and Methodology

Design-to-Plan uses a three-tier, multi-agent architecture to coordinate manufacturing planning from CAD and drawing inputs. Specialized agents exchange structured information asynchronously, while deterministic services support design extraction and human review preserves traceability.

  • Interaction and coordination: The architecture uses structured message passing and asynchronous coordination to support non-blocking execution, traceable intermediate outputs, and modular coordination.The workflow progresses from extraction and review through context fusion, retrieval, sequencing, tool selection, and report generation.
  • Interaction and coordination: Human-in-the-loop review allows users to correct extracted features, annotations, or mappings before downstream planning continues.The interface supports asynchronous monitoring and review of intermediate results.
  • Overall architecture: The Orchestrator initializes sessions, dispatches tasks, monitors execution, aggregates results, and routes structured information without performing domain-specific reasoning.This separation allows specialized agents to focus on assigned tasks while preserving traceability.
  • Planning agents: The Context Fusion, Knowledge Retrieval, Process Sequence, Tool Selection, and Report Generation agents transform extracted information into manufacturing planning outputs.The workflow links design context to retrieved knowledge, validated operation sequences, suitable tools, and a structured report.

3.3 Context Fusion Agent

The Context Fusion Agent bridges CAD-derived features and drawing-derived specifications by enriching annotations, matching them to 3D features, and incorporating human corrections. Its output provides feature-specific context for downstream manufacturing reasoning.

  • Motivation: The agent addresses the problem that separately extracted CAD features and drawing specifications are not actionable until correlated at the feature level.Tolerances, GD&T callouts, and surface finishes must be linked to their applicable holes, pockets, slots, chamfers, or faces.
  • Semantic enrichment: Semantic interpretation enriches each 2D annotation with manufacturing meaning, including feature type, spatial context, and functional intent.The VLM representation also captures descriptive interpretation, likely feature category, and approximate part location.
  • Hybrid 2D-3D mapping: Hybrid matching combines enriched annotation context with 3D feature type, size, and location to identify plausible 2D-3D correspondences.This addresses cases where different feature types share identical numerical dimensions.
  • Downstream use: The resulting enriched 3D representation becomes the primary input for agents that retrieve rules, generate process sequences, and select tools.This converts isolated design artifacts into complete and traceable manufacturing context.
  • Semantic enrichment: Prompt-guided semantic enrichment prevents false matches based only on numerical similarity and supplies context for robust feature correlation.The prompt explicitly encodes manufacturing vocabulary and spatial reasoning heuristics.
  • Hybrid 2D-3D mapping: Adaptive scoring emphasizes dimensional agreement when 3D dimensions are available and semantic or spatial cues when they are not.The formulation is intended to improve robustness under incomplete or ambiguous inputs.
  • Validation: Confidence-aware mapping accepts high-confidence matches automatically, retains medium-confidence matches cautiously, and sends low-confidence cases for human review.User corrections are incorporated before finalization, producing enriched feature objects with specifications, confidence, and mapping method.

3.4 Knowledge Retrieval Agent

The Knowledge Retrieval Agent combines multi-source manufacturing knowledge with stateful ReAct reasoning to retrieve, reconcile, and return traceable planning constraints. Sequential and parallel configurations balance iterative refinement against source coverage and efficiency.

  • The Knowledge Retrieval Agent queries a multi-source manufacturing knowledge base, resolves inconsistencies, and returns structured constraints, rules, and process recommendations.
  • Multi-Source Knowledge Base: Six complementary knowledge modalities combine relational, textual, graph, and rule-based representations, with external fallback for cases exceeding local coverage.
  • Multi-Source Knowledge Base: Input normalization maps informal or non-standard terminology to canonical forms through staged matching and cached results.
  • Sequential Retrieval: Sequential ReAct alternates reasoning, tool selection, observation, and response generation while retaining retrieved evidence for iterative validation and refinement.
  • Grounded Reasoning: Prompt-guided workflows prioritize relevant sources and constrain fallback generation, keeping recommendations grounded, traceable, and domain-rule compliant.
  • Parallel Retrieval: Parallel ReAct assigns structured-data, text-knowledge, and material retrieval to specialized sub-agents whose coordinator merges outputs, resolves conflicts, and reports provenance and confidence.

3.5 Process Sequence Agent

The Process Sequence Agent converts fused feature context and manufacturing constraints into ordered, justified operations. It prioritizes authoritative specifications, validates sequences, and uses confidence scoring to flag issues for review.

  • The Process Sequence Agent receives unified feature context and manufacturing constraints, then generates ordered operations with associated rationale.
  • Sequence Decision Strategy: A three-path hierarchy prioritizes validated human overrides, design-specified processes, and generated sequences when explicit specifications are absent.
  • Sequence Decision Strategy: Generated sequences use process templates, material-process compatibility mappings, and dependency rules to constrain feasible workflows and valid precedence relationships.
  • Validation and Confidence Scoring: All sequences are validated for process combinations, ordering, capability, and manufacturing-rule compliance before confidence scoring.
  • Validation and Confidence Scoring: Confidence uses source-dependent base values and penalties for critical violations and warnings, with user-defined and design-specified sequences initially rated above generated ones.
  • Validation and Confidence Scoring: Lower confidence indicates inconsistencies requiring review, while complex or non-standard cases can invoke iterative ReAct reasoning with specialized tools.

3.6 Tool Selection Agent

The Tool Selection Agent translates validated process sequences into operation-level tooling and machining parameters. It filters tools against material, geometry, and tolerance constraints and reports coverage for review.

  • The Tool Selection Agent receives validated processes and selects cutting tools and machining parameters, bridging process planning and machining execution.
  • Tool Assignment: Candidate tools are filtered using material, geometry, and tolerance constraints, while process-to-tool mappings restrict consideration to applicable categories.
  • Tool Assignment: A four-stage procedure checks tooling requirements, identifies categories, filters incompatible candidates, and selects a primary tool for each operation.
  • Parameter Calculation: Machining parameters use material-dependent reference values, coating adjustments, and operation-specific depth and width-of-cut rules.
  • Parameter Calculation: The milling feed-rate equation computes table feed from feed per tooth, cutting edges, and spindle speed.
  • Confidence Scoring: Tool-selection confidence is an interpretable completeness indicator that reaches 1.0 only when all required tooling steps have valid assignments.

3.7 Report Generation Agent

The Report Generation Agent consolidates upstream outputs into consistent, auditable manufacturing reports. Template-based narration and multiple presentation formats support both human workflows and downstream computational systems.

  • The Report Generation Agent synthesizes upstream agent outputs into structured manufacturing analysis reports.
  • Template-based narration prioritizes consistency, reproducibility, and auditability over open-ended generation.
  • Reports contain seven sections covering summaries, specifications, rules, process plans, tooling, risks, and recommendations.
  • Executive, intermediate, standard, and detailed views plus web, print, editable, and structured-data exports support human and computational use.

4. Evaluation Methodology

The evaluation uses stratified benchmarks and structured execution traces to assess agent behavior, tool use, correctness, efficiency, fallback behavior, and conflict handling. Separate benchmarks support comparisons across knowledge retrieval, process sequencing, and tool selection agents.

  • Benchmark Design: The evaluation covers realistic manufacturing inputs using standard and complex conditions with explicit difficulty stratification and category labels.This design enables performance analysis across varying input difficulty.
  • Benchmark Design: The knowledge retrieval, process sequencing, and tool selection agents are evaluated under controlled conditions using the same benchmark cases for sequential-versus-parallel comparison.Controlled inputs isolate agent-level behavior from workflow-level variability.
  • Evaluation Protocol: Each test case records an execution trace, quantitative evaluation metrics, and a human-readable summary for reproducibility and post-hoc analysis.Traces include tool calls, arguments, retrieved results, intermediate reasoning steps, and token usage.
  • Benchmark Design: The primary knowledge-retrieval benchmark contains 100 cases across ten input-complexity categories, plus 10 conflict-detection cases with 13 known inconsistencies.The conflicts span machinability discrepancies and design-constraint inconsistencies across heterogeneous knowledge sources.
  • Metrics: Tool usage is evaluated with precision, recall, and F1, while additional metrics assess severity, rule sufficiency, efficiency, fallback behavior, and process or tool correctness.Efficiency includes iteration count, tool calls, token usage, and execution time; conflict detection uses detection, resolution, and explanation components.
  • Metrics: Conflict Detection Score combines detection, resolution, and explanation, with resolution weighted slightly higher because authoritative-value selection directly affects downstream planning and tool selection.The weighting is an interpretable rubric rather than a statistically learned parameter set.

5. Results and Discussion

Results show that the hybrid deterministic-agentic framework supports robust multimodal integration and reliable downstream planning, while parallel coordination generally improves coverage and efficiency. The main weaknesses involve authoritative conflict resolution, coating selection, incomplete source access, and task-dependent coordination trade-offs.

  • Context Fusion: The full context-fusion pipeline achieves the highest F1 score by combining domain-specific heuristics with reasoning-based disambiguation.Removing heuristics reduces precision, whereas removing disambiguation reduces recall.
  • Cross-Agent Results: 100% benchmark completion is achieved across the evaluated downstream agents, with parallel coordination generally improving Tool F1 and source or tool coverage.The improvement is most pronounced for knowledge retrieval and tool selection.
  • Cross-Agent Results: Parallel reasoning improves tool coverage and recall, whereas sequential reasoning provides slightly higher precision or deeper validation in some tasks.This reflects a trade-off between distributed source exploration and iterative validation.
  • Conflict Analysis: Conflict analysis detects and identifies inconsistencies more effectively than it resolves them into a single authoritative value.The agent often retains multiple plausible values when evidence is inconsistent, and incomplete source access is the primary failure mode.
  • Efficiency and Planning Quality: The parallel configuration is substantially more token-efficient for process sequencing and tool selection, while sequential reasoning can produce more comprehensive plans.For knowledge retrieval, coordination overhead can offset shorter reasoning paths.
  • Tool Selection: Coating selection remains the weakest tool-selection aspect, while deterministic parameter calculation maintains high parameter accuracy.The coating limitation is associated with underrepresentation in the current tool library.
  • Discussion: The hybrid architecture combines deterministic reliability with flexible LLM reasoning, with Tool F1 remaining above 0.87 across agents.Normalization supports non-standard inputs, and fallback access enables recovery on out-of-KB cases.

6. Conclusions

Design-to-Plan integrates heterogeneous design interpretation, manufacturing knowledge, process decisions, tooling, and reporting through coordinated multi-agent reasoning. Its hybrid architecture combines deterministic extraction with LLM-based reasoning to produce traceable process-planning outputs.

  • Integrated design-to-plan workflow: Design-to-Plan connects 3D CAD feature recognition, 2D engineering drawing analysis, context fusion, knowledge retrieval, sequencing, tool selection, and reporting in one workflow.The fused representation links CAD-derived features with dimensions, tolerances, GD&T, surface finish, material information, and manufacturing notes.
  • Hybrid agentic design: LLM agents coordinate with deterministic modules, manufacturing knowledge sources, and specialized agents to perform context-aware, tool-grounded reasoning over incomplete or conflicting information.Deterministic modules extract CAD and drawing information, while LLM agents reason over the resulting structured inputs.
  • Evaluation findings: The evaluation shows reliable coordination across diverse input conditions, with sequential execution favoring quality-oriented reasoning and parallel execution improving robustness, coverage, and efficiency.The architectures therefore present a practical quality-efficiency trade-off between deeper validation and scalable execution.
  • Supported planning capability: The workflow maintains traceability from original design inputs to final manufacturing outputs while transforming CAD models and engineering drawings into executable process plans.The framework links design interpretation, manufacturing knowledge, process decisions, tooling, and reporting.
  • Future scope: Future work targets CAM strategy generation, machining-parameter optimization, cost-time-quality analysis, process monitoring, inspection feedback, and more reliable closed-loop industrial deployment.The authors identify richer context, validated tools, reusable skills, and verification mechanisms as future development directions.

Declaration of Competing Interest

The authors declare no known competing financial interests or personal relationships that could have influenced the reported work.

  • The authors declare no known competing financial interests or personal relationships that could have influenced the reported work.

Appendix. Knowledge Retrieval Agent: Parallel ReAct System Prompts

The parallel ReAct system partitions knowledge retrieval across specialized sub-agents and uses a coordinator to merge outputs under shared provenance and conflict-resolution rules. Restricted tool scopes support structured-data, text-knowledge, and material-specific retrieval.

  • Parallel ReAct architecture: Three specialized sub-agents operate concurrently, while a coordinator merges their outputs using the source-priority hierarchy.The parallel arrangement partitions knowledge tools across structured-data, text-knowledge, and material-retrieval agents.
  • Specialized retrieval agents: The structured-data sub-agent handles SQL, tabular, rule-based, and decision-tree queries for quantitative constraints.Conflicting outputs are forwarded for resolution.
  • Specialized retrieval agents: The text-knowledge sub-agent retrieves qualitative guidelines from RAG text and knowledge graphs while preserving source provenance.
  • Specialized retrieval agents: The material sub-agent retrieves material-specific constraints, including machinability and dimensional limits.
  • Coordination and resolution: The coordinator resolves conflicts and produces a unified response with provenance and confidence scores.Its prompt integrates the specialized outputs under explicit conflict-resolution rules.
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