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arxiv: 2605.21026 · v1 · pith:7GYDQ26Gnew · submitted 2026-05-20 · 💻 cs.RO

Component Influence-Driven Fastener Reduction for Robotic Disassemblability-Aware Design Simplification

Takuya Kiyokawa , Tomoki Ishikura , Shingo Hamada , Genichiro Matsuda , Kensuke Harada This is my paper

Pith reviewed 2026-05-21 04:33 UTC · model grok-4.3

classification 💻 cs.RO
keywords robotic disassemblyfastener reductiondesign for disassemblycomponent influenceCCC graphCAD modelremanufacturingstructural simplification
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The pith

A framework scores components by their disruption to robotic disassembly sequences and recommends fastener removals that cut structural constraints and robot travel.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops an analytical method to help designers simplify products for robotic disassembly by targeting fasteners. It builds a Contact-Connection-Constraint graph from a CAD model and uses outcomes from disassembly sequence planning to assign influence scores to components. These scores are visualized as 3D heatmaps on the geometry to show which parts create the most problems. The method then simulates removal of the highest-scoring fasteners while checking geometric stability to avoid unsafe changes. Experiments on seven household appliances confirm that the suggested removals reduce constraints on the graph and shorten robot paths.

Core claim

The framework translates robotic disassembly sequence planning outcomes into component influence scores that reflect how often a component causes structural constraint violations or evaluation objective deteriorations. These scores are projected onto the CAD geometry as 3D heatmaps to highlight structural hindrances. The system then analytically simulates the removal of highly influential fasteners and reports expected reductions in structural constraints, tool changes, and robot travel distances while using geometric stability metrics to prevent unsafe modifications. Experiments confirm that recommended removals eliminate between 8 and 132 structural constraints and shorten travel distances

What carries the argument

Component influence scores derived from robotic disassembly sequence planning results on an automatically generated Contact-Connection-Constraint (CCC) graph from the CAD model, used to rank and simulate fastener removals.

If this is right

  • Removing the recommended fasteners eliminates between 8 and 132 structural constraints on the graph for each product.
  • Robotic disassembly gains efficiency by removing unnecessary tool change operations.
  • Robot travel distances shorten by 165 to 1675 millimeters wherever the removal remains structurally safe.
  • Designers obtain quantitative, visual feedback on which structural elements most hinder robotic operations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The scoring approach could be built into CAD tools to suggest simplifications automatically during the design process.
  • Similar influence calculations might improve planning for robotic assembly or maintenance tasks beyond disassembly.
  • Applying the method to larger or more intricate products such as vehicles or industrial machinery would test whether the reported gains scale.

Load-bearing premise

The geometric stability metrics correctly flag every unsafe fastener removal and the CCC graph fully captures all physical contacts, connections, and constraints in the real product.

What would settle it

Perform physical robotic disassembly trials on one of the tested appliances before and after removing the recommended fasteners, then measure whether actual instability occurs or whether the observed reductions in constraints, tool changes, and travel distances match the simulated values.

Figures

Figures reproduced from arXiv: 2605.21026 by Genichiro Matsuda, Kensuke Harada, Shingo Hamada, Takuya Kiyokawa, Tomoki Ishikura.

Figure 1
Figure 1. Figure 1: Illustrative CCC graph structure for a seven-part [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: a large and a small dual-arm cell. Both utilize dual [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 2
Figure 2. Figure 2: The seven case-study products (cs1–cs7): condenser unit, television, air-conditioner outdoor unit, microwave oven, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The two robotic cells used to evaluate ∆T and ∆D. Both utilize dual-arm robots equipped with multi-view vision sensors, linear and rotary stages to rotate target objects, and tool racks placed near each arm. violate structural constraints. Base chassis parts, such as the condenser unit side covers and the television base plates, are consistently highlighted. This visually confirms that these base parts act… view at source ↗
Figure 4
Figure 4. Figure 4: Assembly-level cconst heatmaps for the seven products. Darker colors denote high scores, indicating major structural constraints [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Assembly-level cobj heatmaps for the seven products. Darker colors highlight components degrading operational efficiency [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Top-down directive view of cconst identifying structurally critical fasteners clustered on upper faces [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Top-down directive view of cobj identifying fasteners whose removal most improves efficiency. However, because high-influence fasteners tend to be con￾centrated spatially, the proposed rule returns more significant decreases in ρJ or ρA than random selection. D. Sensitivity to the Removal Limit Rmax [PITH_FULL_IMAGE:figures/full_fig_p005_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Sensitivity of maximum possible ∆E, ∆T, and ∆D to the limit Rmax. Currently, designers can reduce and replace these con￾nections by manually substituting multiple small fasteners with fewer robust joints or snap-fits. By leveraging recent advancements in multimodal LLMs and deep generative networks for computer-aided design [11], [12], [16], this redesign process can be highly automated. Recent studies hav… view at source ↗
read the original abstract

To accelerate automated remanufacturing, robotic disassembly must be considered during the product design phase. However, designers currently lack quantitative feedback to identify which structural elements hinder robotic operations. To address this, this study proposes an analytical framework that provides actionable redesign guidance focused on fastener reduction, as fasteners are numerous and ubiquitous components found in almost all manufactured products. Using a Computer-Aided Design (CAD) model and its automatically generated Contact-Connection-Constraint (CCC) graph, the framework translates robotic disassembly sequence planning outcomes into component influence scores. These scores reflect how often a component causes structural constraint violations or evaluation objective deteriorations in the robotic disassembly sequence. To visually highlight structural hindrances, the framework projects these scores onto the CAD geometry as 3D heatmaps. The system then analytically simulates the removal of highly influential fasteners. It reports the expected reductions in structural constraints, tool changes, and robot travel distances, while preventing structurally unsafe modifications by evaluating geometric stability metrics. Experiments on seven household appliances demonstrate that the framework successfully targets redundant fasteners. Removing the recommended fasteners simplified the structural dependencies by eliminating between 8 and 132 structural constraints on the graph depending on each product's structural configuration. Furthermore, it improved robotic operational efficiency by eliminating unnecessary tool change operations and shortening travel distances by 165 to 1675 millimeters wherever structurally permissible.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes an analytical framework for robotic disassemblability-aware design simplification focused on fastener reduction. It uses a CAD model to automatically generate a Contact-Connection-Constraint (CCC) graph, derives component influence scores from robotic disassembly sequence planning outcomes (reflecting constraint violations and objective deteriorations), projects these as 3D heatmaps on the geometry, and analytically simulates removal of high-influence fasteners while applying geometric stability checks to avoid unsafe changes. Experiments on seven household appliances report that recommended removals eliminate 8–132 structural constraints per product and shorten robot travel distances by 165–1675 mm while reducing tool changes.

Significance. If the CCC graph fidelity and stability metrics are reliable, the work offers a practical, quantitative method to provide designers with early feedback on disassembly bottlenecks, which could accelerate remanufacturing automation. The influence-score approach and heatmap visualization provide interpretable, geometry-linked guidance that goes beyond purely geometric or graph-theoretic simplifications. The analytical simulation of removals is a strength, as it avoids exhaustive physical re-testing.

major comments (3)
  1. [§3] §3 (CCC graph construction): The framework's core outputs—constraint reductions of 8–132 and travel-distance savings of 165–1675 mm—depend entirely on the automatically generated CCC graph faithfully encoding all relevant contacts, connections, and constraints. The manuscript supplies no algorithm details for graph extraction from CAD, no quantitative validation (e.g., precision/recall against manual disassembly or physical inspection), and no sensitivity analysis, leaving the headline numbers unsupported.
  2. [§4] §4 (Experiments and results): The reported performance gains are given only as ranges across seven appliances with no per-product breakdowns, no comparison against baseline fastener-removal heuristics (random, degree-based, or existing disassembly planners), and no error bars or statistical tests. This makes it impossible to assess whether the influence-driven method meaningfully outperforms simpler alternatives and weakens the claim that the framework 'successfully targets redundant fasteners.'
  3. [§3.3] §3.3 (Geometric stability metrics): The safety filter that 'prevents structurally unsafe modifications' is load-bearing for all positive claims, yet the metrics are neither defined mathematically nor calibrated against known unstable configurations. Without explicit formulation or validation, it is unclear whether the accepted removals are truly safe or merely pass an untested heuristic.
minor comments (2)
  1. [Abstract] Abstract and §4: The seven appliances are not named and no table lists per-product fastener counts, removals, or exact metric values, reducing reproducibility.
  2. [§3.1] Notation: The terms 'component influence scores' and 'evaluation objective deteriorations' are used without a compact mathematical definition or pseudocode, making the translation from disassembly planner output to scores difficult to follow.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. The comments highlight important areas for clarification and strengthening of the evidence. We address each major comment below and outline the revisions we will incorporate.

read point-by-point responses

  1. Referee: [§3] §3 (CCC graph construction): The framework's core outputs—constraint reductions of 8–132 and travel-distance savings of 165–1675 mm—depend entirely on the automatically generated CCC graph faithfully encoding all relevant contacts, connections, and constraints. The manuscript supplies no algorithm details for graph extraction from CAD, no quantitative validation (e.g., precision/recall against manual disassembly or physical inspection), and no sensitivity analysis, leaving the headline numbers unsupported.

    Authors: We agree that the current manuscript provides only a high-level description of CCC graph construction in Section 3 without the underlying extraction algorithm or supporting validation. This limits the ability to fully substantiate the reported results. In the revised version we will expand Section 3 with a complete algorithmic description of contact detection, connection inference, and constraint modeling from CAD geometry, including pseudocode. We will also add quantitative validation results comparing the automated graphs to manually constructed references for the appliances, reporting precision and recall, along with a sensitivity analysis on key extraction parameters. revision: yes

  2. Referee: [§4] §4 (Experiments and results): The reported performance gains are given only as ranges across seven appliances with no per-product breakdowns, no comparison against baseline fastener-removal heuristics (random, degree-based, or existing disassembly planners), and no error bars or statistical tests. This makes it impossible to assess whether the influence-driven method meaningfully outperforms simpler alternatives and weakens the claim that the framework 'successfully targets redundant fasteners.'

    Authors: The presentation of results solely as aggregate ranges does restrict detailed evaluation of the method. We will revise Section 4 to include a table with per-product breakdowns of all reported metrics. We will further incorporate comparisons against baseline heuristics (random removal and degree-based selection) executed within the same simulation environment. With only seven products we will note the constraints on formal statistical testing but will report any available variability measures from the runs. revision: yes

  3. Referee: [§3.3] §3.3 (Geometric stability metrics): The safety filter that 'prevents structurally unsafe modifications' is load-bearing for all positive claims, yet the metrics are neither defined mathematically nor calibrated against known unstable configurations. Without explicit formulation or validation, it is unclear whether the accepted removals are truly safe or merely pass an untested heuristic.

    Authors: We concur that the geometric stability metrics require explicit mathematical formulation and supporting validation. The manuscript references these metrics for safety filtering but does not supply the equations or calibration evidence. In the revision we will add the formal definitions, including the specific criteria based on center-of-mass location and support geometry. We will also include validation on synthetic configurations with known stability properties to demonstrate that the filter correctly identifies unsafe removals. revision: yes

Circularity Check

0 steps flagged

No circularity: results are computed outputs from external planning on product graphs

full rationale

The paper describes a pipeline that ingests CAD models, auto-generates CCC graphs, runs robotic disassembly sequence planning to produce influence scores, then analytically simulates fastener removals subject to geometric stability checks. The reported constraint reductions (8–132) and travel shortenings (165–1675 mm) are measured by re-running the planner on the modified graphs for seven specific appliances. No equation or step redefines a fitted parameter as a prediction, imports uniqueness via self-citation, or renames an input as an output; the claims remain independent of the input data by construction and rest on external simulation results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework depends on the assumption that the automatically generated CCC graph encodes all disassembly-relevant constraints and that the stability metrics used in simulation are sufficient to guarantee physical safety after removal.

axioms (1)
  • domain assumption The Contact-Connection-Constraint graph generated from the CAD model accurately represents all structural constraints relevant to robotic disassembly.
    This graph is the sole input to the influence-score calculation and removal simulation.

pith-pipeline@v0.9.0 · 5784 in / 1290 out tokens · 25436 ms · 2026-05-21T04:33:08.023926+00:00 · methodology

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Reference graph

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