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Vision-Based Artificial Intelligence for Adaptive Peen Forming: Sensing Architectures, Learning Models, and

Sehar Shahzad Farooq^1,2, Abdul Rehman³, Fuad Ali Mohammed Al-Yarimi⁴

¹Department of Control and Robot Engineering, Gyeongsang National University, Jinju 52828, Gyeongsangnam-do, Republic of Korea.

Sensors (Basel, Switzerland)

|May 4, 2026

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This summary is machine-generated.

Adaptive peen forming uses AI and vision sensing for better aerospace panel manufacturing. Current limitations include data scarcity and harsh conditions, requiring more validation for industrial use.

Area of Science:

Manufacturing Engineering
Artificial Intelligence
Aerospace Engineering

Background:

Peen forming shapes aerospace panels using shot impacts, inducing residual stresses and curvature.
Current monitoring relies on indirect measures (Almen intensity, coverage), limiting precise deformation assessment and closed-loop control.

Purpose of the Study:

To review the transferability of vision-based AI and sensing strategies to adaptive peen forming.
To assess AI models and vision modalities for surface mapping, temporal prediction, and robustness in peening applications.

Main Methods:

Comparison of six vision sensing modalities.
Assessment of major AI model families for peen forming applications.
Analysis of constraints and opportunities for adaptive peening.

Keywords:

Industry 4.0 artificial intelligence closed-loop control deep learning edge AI intelligent sensing machine vision multi-sensor fusion peen forming smart manufacturing

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Main Results:

Progress is hindered by limited validated datasets and harsh in-cabinet sensing conditions.
Few closed-loop demonstrations and weak validation on curved aerospace geometries exist.
Vision sensing and AI show potential for adaptive peen forming.

Conclusions:

Foundations for adaptive peen forming using sensing and AI are emerging.
Industrial translation requires enhanced experimental validation and standardized benchmarking.
Robust multi-sensor integration and edge-capable feedback pipelines are crucial for adoption.