ArticleScientific reports2025
Selection of optimal fabrication parameters of an innovative pressure sensor using fuzzy-AHP method based on sensor characteristics for robotic gripper.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Clarifying the landscape of mechanical sensors from stress to strain.Nature sensors · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
With increasing demand for diverse sensors across various applications, the development of innovative sensors is crucial to advancing emerging technologies. Fabric-based sensors, valued for their flexibility and softness, are gaining popularity in fields like medicine and healthcare. This study presents a new method for designing and fabricating a textile-based resistive pressure sensor using pad printing and conductive inks, specifically for robotic grippers. Key fabrication factors include fabric type, ink composition, and the number of print passes. Sensor performance was assessed based on sensitivity, linearity, repeatability, and fatigue resistance. The main goal was to identify and optimize fabrication parameters to achieve the best performance within a robotic gripper using the fuzzy-AHP (Fuzzy Analytic Hierarchy Process) method. The study was carried out in two phases. First, the optimal sample was chosen by evaluating general sensor properties through fuzzy-AHP modeling. In the second phase, to enhance the gripper's performance when handling objects with varying surface hardness, the model incorporated sub-criteria of sensitivity and linearity across different ranges. Three scenarios were developed to identify the best sensor sample for gripping objects of low, medium, and high hardness. Across four rounds of fuzzy-AHP modeling, the sensor printed on polyamide-based taffeta label fabric with three print passes and 100% carbon nanoparticle ink delivered the best overall performance, surpassing other configurations.
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Registered trials
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