IndustrialCAD-1400: Benchmarking Detailed Industrial Assemblies from Text

Hebao Zhu; Kang An; Zhuoke Liao; Faqiang Qian; Ziliang Wang; Mingang Chen; Qibing Ren

cs.AIOct 17, 2026

IndustrialCAD-1400: Benchmarking Detailed Industrial Assemblies from Text

Authors: Hebao Zhu, Kang An, Zhuoke Liao, Faqiang Qian, Ziliang Wang, Mingang Chen, Qibing Ren

Organizations: Chongqing University · Shanghai Jiao Tong University · SenseTime · Shanghai Development Center of Computer Software Technology

Abstract

Industrial assemblies require more than a recognizable shape: functional parts, interfaces, dimensions and service features must satisfy a detailed request together. We introduce IndustrialCAD-1400, a text-to-CAD benchmark for this industrial setting. Its 1,400 requests combine 400 equipment categories across 40 domains with 250 four-case design families. Family requests specify a median of 17 named component or configuration roles and six explicit numeric targets; controlled variants test whether dimensions change while shared structure is retained. Task construction draws on documented manufacturer information, owner specifications and engineering guidance. Models generate geometry programs from requests, and a common Blender environment executes them to produce scenes, optional measurements, and standardized views for evaluation. A request assessment of front, isometric, and top views measures visible component, arrangement, and interface fulfillment without requiring a single reference geometry. Five stratified panels preserve complete families while covering the benchmark exactly once, enabling balanced comparisons and separating industrial breadth from controlled numerical responsiveness. In conclusion, IndustrialCAD-1400 extends text-to-CAD from recognizable part geometry to industrial assembly synthesis under joint component, interface, spatial, and numerical specifications.

Figures & tables

Figure 1: Part-level and assembly-level modeling requirements. Left: a single-part example rendered from the published L1 reference code in Text2CAD-Bench ( Wang et al., 2026 , Appendix F) . Right: a pump-skid reference assembly from IndustrialCAD-1400. The examples illustrate local geometric features and coordinated component, layout, and dimensional requirements, respectively. The complete family-request structure appears in Figure 4 .

Figure 2: Overview of IndustrialCAD-1400. Industrial coverage motivates category-based and controlled-variant generation. Blind three-view review provides the shared visual-conformity score; geometric diagnostics denote optional measurement extensions. Five disjoint, stratified panels cover all requests once. Equipment and views are schematic; the four conveyor dimension pairs are actual task targets.

Provider	Document	Task-construction context
ADNOC	Pressure Vessel Specification	Nozzles, manways, supports and access
Flexco	Belt Conveyor Products handbook	Belt accessories, pulleys and tensioners
NSW government	MDG 4 conveyor-tensioning guideline	Tensioning and maintenance context

Table 1: Industrial documentation informing task construction. Source names and descriptions appear in Appendix Table 8 . Numerical task targets are defined by the benchmark.

Track	$N$	Domains	Organization	Roles	Numeric	Enhanced
Taxonomy	400	40	10 per domain	4 [3,5]	0 [0,0]	45/228
Family variants	1,000	5	250 families $\times$ 4	17 [10,22]	6 [5,8]	91/479

Table 2: Profiles of the two generation tasks. Roles and numerical items are per-request medians [min,max], counting named component/configuration targets and explicit numerical requirements, respectively. Enhanced lists requests/additional checks (136/707 overall).

Figure 3: Examples from the two generation tasks. Controlled-variant examples (a,b) show reference assemblies for a pump skid and a covered conveyor, together with component and numerical requirements. Category-based examples (c,d) specify equipment identity and required features without reference geometry.

Figure 4: Representative family request example. The request fixes the equipment identity, numerical targets, named component roles, and three-view evidence requirements while allowing multiple valid geometries.

	Category		Variant		Overall
Model	D	S	D	S	D	S
DeepSeek-V4-Pro	95.00	56.00	97.00	72.28	96.43	67.63
DeepSeek-V4-Flash	88.75	54.10	94.50	66.68	92.86	63.08
GLM-5.3	94.00	55.30	86.00	56.14	88.29	55.90
MiniMax-M3	92.50	54.60	82.00	52.39	85.00	53.02

Table 3: Delivery and visual conformity on the two generation tasks. Delivery is the percentage of valid outputs; Score is the mean 0–100 score including model-failure zeros. Overall uses 2:5 task weights.

	Pro		Flash		GLM-5.3		MiniMax-M3
Equipment domain	D	S	D	S	D	S	D	S
Electrical enclosures	100.00	67.33	97.50	58.10	96.00	57.05	100.00	54.08
Cooling towers	100.00	68.98	100.00	63.70	91.00	56.89	87.50	54.20
Conveyors	97.50	78.38	97.50	76.70	90.00	68.74	85.00	66.83
Pressure vessels	95.00	74.10	85.00	64.13	68.00	41.92	57.50	33.88
Pump skids	92.50	72.60	92.50	70.75	85.00	56.10	80.00	52.95

Table 4: Controlled-variant results by equipment domain. Pro refers to DeepSeek-V4-Pro, and Flash refers to DeepSeek-V4-Flash. D is delivery rate(%); S is the mean visual score including model-failure zeros. Domains have equal weight. Bold denotes the highest score in each row.

Figure 5: Visual conformity across models. (a) Cumulative score distributions; (b) mean scores for the two tasks; (c) means over all outputs and over valid deliveries only. All-output means include model-failure zeros. Conditional means use each model’s own delivered subset.

Model	Task	Round 1	Round 2	Round 3	Round 4	Round 5	Mean
DeepSeek-V4-Pro	Category	53.90	57.80	55.35	58.40	54.55	56.00
	Variant	69.83	74.33	71.38	75.03	70.81	72.28
	Overall	65.28	69.61	66.80	70.28	66.16	67.63
DeepSeek-V4-Flash	Category	51.84	56.20	53.35	56.55	52.56	54.10
	Variant	64.53	68.98	65.58	69.13	65.16	66.68
	Overall	60.90	65.33	62.09	65.54	61.56	63.08

Table 5: Completed scores across five shared, disjoint stratified panels, each containing 80 category and 200 variant requests. Means include model-failure zeros; Overall uses 2:5 task weights.

	Category		Variant		Overall
Model	MAE	$\rho$	MAE	$\rho$	MAE	$\rho$
DeepSeek-V4-Pro	5.41	0.923	6.36	0.872	5.89	0.924
DeepSeek-V4-Flash	6.31	0.825	6.24	0.847	6.28	0.861
GLM-5.3	7.09	0.877	6.90	0.884	6.99	0.889
MiniMax-M3	6.95	0.893	6.49	0.799	6.72	0.865

Table 6: Completed human–judge comparison using 80 requests per model and two independent reviewers. MAE and Spearman correlation compare the judge with the mean human score.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Industrial domain	$N$	Industrial domain	$N$
Advanced filtration and membrane separation	10	Material handling and conveying	210
Agricultural machinery and farm processing	10	Medical and laboratory equipment	10
Compressed air, gas and vacuum systems	10	Mining and mineral-processing equipment	10
Automotive manufacturing and assembly	10	Mixing, agitation and mass transfer	10
Battery manufacturing and testing	10	Motors, gearboxes and transmission	10
Chemical process units	10	Oil and gas wellhead and gathering	10

Appendix

Table 7: Distribution across forty industrial domains. Counts combine the two tasks; domains with 210 requests contain ten taxonomy cases and fifty four-case families.

Provider / document	Inspected type	Task-construction context
ABB: library portal	HTML shell	Portal pointer; previously inspected application shell only.
Alfa Laval: CAD portal	HTML page	Hygienic-equipment portal pointer; no CAD asset obtained.
Pall: filter CAD portal	HTML page	Filter-model download page; no CAD asset obtained.
Xylem: technical drawings	HTML page	Pump/drawing index pointer; no CAD asset obtained.
Siemens: drive configurator	HTML page	Configurator description; motor/drive selection context.
ADNOC: vessel specification	Direct PDF	Owner specification; nozzle, support and access context.

Appendix

Table 8: Representative industrial sources, previously inspected content and task-construction context. Sources are identified by provider and document title.

Resource	Included content
Task collection	All 1,400 requests, identifiers, domain/category/family assignments and evaluation specifications
Modeling interface	Common instructions, unit conventions and geometric primitive helpers
Evaluation definitions	Shared 0–100 visual-conformity standard, evidence rules and judge configuration
Rotation protocol	Five fixed panel lists, stratification rule and random seed
Evaluation software	Execution, standardized rendering, image assessment and score aggregation tools

Appendix

Table 9: Benchmark release. All resources are versioned together to identify the task set and evaluation conditions used in a comparison.

$J$	Reference anchor
0	No meaningful demonstrated fulfillment of the requested external assembly.
25	Rough outline of the requested category; most core external structure is absent or wrong.
50	Substantial partial match; a major visible issue or the main external arrangement remains unestablished.
75	Mostly consistent main assembly; localized deviations or uncertainty concern secondary details.
100	Consistent requested external assembly, without a substantive visible issue or unresolved external requirement.

Appendix

Table 10: One common visual-conformity scale for every task. A valid delivered output receives any integer from 0 to 100. These reference anchors are neither fixed grades nor additive checks.

Figure 6: Reference assemblies from 150 design families across five industrial domains. Each three-row band contains 30 families. The gallery covers 60% of the family track and illustrates its design diversity.

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