A scalarization-based method for multiple part-type scheduling of two-machine robotic systems with non-destructive testing technologies

Foumani, Mehdi; Moghaddam, Reza Tavakkoli

doi:10.29252/iors.10.1.1

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…Ensuring product quality and detecting defects pre-consumption is imperative. With the global plastic market heading towards a $700 billion valuation by 2023, cost-effective quality control [22] and efficient mass production inspection [12], [23] are crucial. This underscores both the economic importance and the pressing need for effective quality control.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Framework for Visual Inspection of Plastic Bottles

Kazmi,

Hafeez,

Aftab

et al. 2023

IEEE Access

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This paper presents a deep learning-based framework for automating the visual inspection of plastic bottles in an Industry 4.0 context, detecting surface defects to enhance product quality. Our contributions include the acceleration of model development through knowledge transfer learning, an inventive data generation strategy that combines physical samples with synthetic data augmentation techniques, an extensive evaluation of pre-trained deep convolutional neural networks, and a user-friendly interface for real-time quality inspection reporting and making the information easily accessible and actionable. In comparison to existing methods, our proposed method outperforms with a higher Accuracy to Size Ratio of 7.0. This characteristic underscores its capacity to efficiently and accurately classify and detect defects across multiple classes while maintaining a low area utilization. This feature not only demonstrates its exceptional performance but also positions it as a practical solution for real-world scenarios with resource constraints.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Framework for Visual Inspection of Plastic Bottles

Kazmi,

Hafeez,

Aftab

et al. 2023

IEEE Access

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“…Nowadays, robotic arms have become more and more important in factory assembly lines [ 1 , 2 ] due to their ability to perform repetitive tasks with an accuracy of a few thousandths of an inch while being operable 24 h a day. Given the advantages, the costs of deploying robots to a product line, however, are much higher than the robots themselves.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Learning from Demonstration System for Robot Arms

Hwang

Hsu

Chou

et al. 2022

Sensors

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Robotic arms have been widely used in various industries and have the advantages of cost savings, high productivity, and efficiency. Although robotic arms are good at increasing efficiency in repetitive tasks, they still need to be re-programmed and optimized when new tasks are to be deployed, resulting in detrimental downtime and high cost. It is therefore the objective of this paper to present a learning from demonstration (LfD) robotic system to provide a more intuitive way for robots to efficiently perform tasks through learning from human demonstration on the basis of two major components: understanding through human demonstration and reproduction by robot arm. To understand human demonstration, we propose a vision-based spatial-temporal action detection method to detect human actions that focuses on meticulous hand movement in real time to establish an action base. An object trajectory inductive method is then proposed to obtain a key path for objects manipulated by the human through multiple demonstrations. In robot reproduction, we integrate the sequence of actions in the action base and the key path derived by the object trajectory inductive method for motion planning to reproduce the task demonstrated by the human user. Because of the capability of learning from demonstration, the robot can reproduce the tasks that the human demonstrated with the help of vision sensors in unseen contexts.

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