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A Data-Driven Iterative Design Optimization Approach for Smart Textile Installations Based on Physical Prototype Feedback Cover

A Data-Driven Iterative Design Optimization Approach for Smart Textile Installations Based on Physical Prototype Feedback

By: ,   and    
Open Access
|Sep 2026

Figures & Tables

Figure 1

The physical prototype and its 3D model: (a) Image of the physical prototype; (b) 3D model of the prototype.

Source: Authors’ contribution.

Figure 2

Multi-view drawing of the prototype.

Source: Authors’ contribution.

Table 1

Optical fiber processing experiment

Sample codeLight source colorDiameter (mm)Processing technologyProcessDaytime photosNighttime photos
ARed0.25Normal stateConnect light source on one end graphic/j_ftee-2026-0008_fx_001.jpg graphic/j_ftee-2026-0008_fx_002.jpg
BRed0.25, 0.5CoilCoil optical fiber and connect light source at one end graphic/j_ftee-2026-0008_fx_003.jpg graphic/j_ftee-2026-0008_fx_004.jpg
CRed0.5EngravedCut around fiber skin with a scissor or a utility knife graphic/j_ftee-2026-0008_fx_005.jpg graphic/j_ftee-2026-0008_fx_006.jpg
DRed0.25, 0.5RubRub fiber skin with sandpaper graphic/j_ftee-2026-0008_fx_007.jpg graphic/j_ftee-2026-0008_fx_008.jpg
ERed0.5BendBend anywhere on the fiber graphic/j_ftee-2026-0008_fx_009.jpg graphic/j_ftee-2026-0008_fx_010.jpg
FRed0.5Turn on the light source at both endsConnect the light source at both ends of the optical fiber graphic/j_ftee-2026-0008_fx_011.jpg graphic/j_ftee-2026-0008_fx_012.jpg
GRed0.5Bend and coilCoil after bending anywhere on the optical fiber graphic/j_ftee-2026-0008_fx_013.jpg graphic/j_ftee-2026-0008_fx_014.jpg
HRed0.25EmbroiderEmbroidery on black fabric with optical fiber graphic/j_ftee-2026-0008_fx_015.jpg graphic/j_ftee-2026-0008_fx_016.jpg
IRed0.75RubLay optical fiber on tape flatly, make a pattern by rubbing with sandpaper, and connect the light source at one end graphic/j_ftee-2026-0008_fx_017.jpg graphic/j_ftee-2026-0008_fx_018.jpg

Source: Authors’ contribution.

Figure 3

Visual effects under different colored lights.

Source: Authors’ contribution.

Figure 4

Design attributes of the optical fiber art installation.

Source: Authors’ contribution.

Table 2

Demographic characteristics of respondents (N = 103)

Demographic dimensionCategoryNumber (n)Proportion (%)
GenderMale5957.3
Female4442.7
Age18∼25 Years2827.2
26∼30 Years3533.98
31∼40 Years2423.3
>40 Years1615.52
EducationSenior high school and below1918.45
College degree and above8481.55
OccupationMarketing/Sales/Commerce1615.53
Enterprise employee3231
Student2524.3
Freelancer1817.5
Others (Teacher/Medical, etc.)1211.67

Source: Authors’ contribution.

Table 3

Summary of KANO Model analysis results

No.Features/ServicesMOAIRQResult
1Views on the optical fiber art installation20.39%9.71%49.51%20.39%0%0%A
2Cultural connotations0%0%0%65.05%34.95%0%I
3Conveying concepts0%0%0%11.65%88.35%0%R
4Integration of art and technology20.39%43.69%19.42%16.5%0%0%O
5Spatial positioning considerations40.78%6.8%35.92%16.5%0%0%M
6Fiber optic materials26.21%36.89%17.48%19.42%0%0%O
7Engraving technique58.25%9.71%14.56%17.48%0%0%M
8Technical controls0%0%0%45.63%54.37%0%R
9Modeling and previewing21.36%16.5%50.49%11.65%0%0%A
10Expressions methods0%0%0%19.42%80.58%0%R
11Form selection54.37%12.62%9.71%23.3%0%0%M
12Color expression18.45%1.94%17.48%62.14%0%0%I
13Layout and arrangement23.3%33.01%27.18%16.5%0%0%0
14Perception of color effects25.24%9.71%15.53%49.51%0%0%I
15Perception of spatial atmosphere10.68%7.77%49.51%32.04%0%0%A
16Perception of dynamic effects57.28%9.71%13.59%19.42%0%0%M
17Emotional resonance experience51.56%8.74%17.48%22.33%0%0%M

Result Classification:.

A: Attractive attributes; O: One-dimensional attributes; M: Must-be attributes; I: Indifferent attributes; R: Reverse attributes; Q: Questionable attributes.

Source: Authors’ contribution.

Figure 5

The Better–Worse coefficient plot. Note: This figure is based on the Kano model results and plots the Better and Worse values calculated using equations (1) and (2) from the paper. Each point in the figure represents one design attribute. The Y-axis corresponds to the Better value, while the X-axis corresponds to the absolute value of the Worse value. Attributes are mapped onto a two-dimensional coordinate system according to their coefficient profiles and are divided into four quadrants: Quadrant I represents One-dimensional attributes (high Better, high |Worse|); Quadrant II represents Attractive attributes (high Better, low |Worse|); Quadrant III represents Indifferent attributes (low Better, low |Worse|); and Quadrant IV represents Must-be attributes (low Better, high |Worse|). This visualization intuitively illustrates the non-linear impact characteristics of design attributes on user satisfaction.

Source: Authors’ contribution.

Table 4

Sensitivity Coefficients and Bootstrap 95% CIs for Design Attributes

No.Features/ServicesBetterWorseω 95% CI Lower Bound95% CI Upper BoundResultsSort
1Views on the optical fiber art installation59.22%−30.1%0.6640.6120.715A8
2Cultural connotations0%0%000I14
3Convey ideas0%0%000R/
4Art and technology merge ideas63.11%−64.08%0.8990.8610.934O1
5Spatial positioning thinking42.72%−47.57%0.6390.5880.690O10
6Fiber optic materials54.37%−63.11%0.8330.7900.875O2
7Engraving technique24.27%−67.96%0.7220.6700.773M5
8Technical controls0%0%000R/
9Model the form of a production preview66.99%37.86%0.7690.7200.817O4
10Expressions0%0%000R/
11Pattern selection22.33%−66.99%0.7060.6540.757M7
12Color expression19.42%−20.39%0.2820.2410.325I13
13Layout arrangement60.19%−56.31%0.8240.7810.866O3
14Color effect perception25.24%−34.95%0.4310.3780.483I12
15Spatial atmosphere perception57.28%−18.45%0.6020.5500.653A11
16Dynamic effect perception23.3%−66.99%0.7090.6570.760M6
17Emotional resonance experience26.21%−60.19%0.6560.6050.706M9

Note: Bootstrap resampling (1,000 iterations) was used to calculate confidence intervals (CIs). Values for No. 2, 3, 8, and 10 are 0 because these attributes are classified as Indifferent (I) or Reverse (R) needs, with no valid Better–Worse coefficient values for sensitivity calculation.

Source: Authors’ contribution.

Table 5

Priority ranking of design attributes by category

CategorySort
M: Must-be attributes7 > 16 > 11 > 17
O: One-dimensional attributes4 > 6 > 13 > 9 > 5
A: Attractive attributes1 > 15
I: Indifferent attributes 14 > 12 > 4

Source: Authors’ contribution.

DOI: https://doi.org/10.2478/ftee-2026-0008 | Journal eISSN: 2300-7354 | Journal ISSN: 1230-3666
Language: English
Page range: 90 - 100
Submitted on: Nov 14, 2025
Accepted on: Jul 14, 2026
Published on: Sep 17, 2026
Published by: Łukasiewicz Research Network-Łódź Institute of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2026 Shun Tian, Linxi Cai, Jincha Ye, published by Łukasiewicz Research Network-Łódź Institute of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.