Flexible automation of quality inspection in parts assembly using CNN-based machine learning

Shaloo, Masoud; Princz, Gábor; Hörbe, Roman; Erol, Selim

doi:10.1016/j.procs.2024.02.108

Procedia Computer Science

2024

DOI: 10.1016/j.procs.2024.02.108

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Flexible automation of quality inspection in parts assembly using CNN-based machine learning

Masoud Shaloo,

Gábor Princz,

Roman Hörbe

et al.

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Cited by 2 publications

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Artificial Intelligence in planning and control tasks: a study of potential use cases and perceived challenges in Austrian make-to-order companies

Hörbe,

Erol

2024

Procedia CIRP

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Artificial Intelligence in planning and control tasks: a study of potential use cases and perceived challenges in Austrian make-to-order companies

Hörbe,

Erol

2024

Procedia CIRP

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Design of Connector Assembly Equipment for the Automotive Industry

Curralo,

Campilho,

Pereira

et al. 2024

Machines

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The automotive industry is one of the most demanding sectors of all manufacturing industries due to its competitiveness. It is necessary to innovate through the implementation of automated and robotic equipment, leading to cycle time and labor cost reduction. This work aims to design semi-automatic equipment to assemble cabling connectors used in the automotive sector, replacing a manual process currently taking place in an automotive components company. In the proposed equipment, the operator places a connector in the equipment, and the components (pins and seals) are automatically inserted. A vision sensor with artificial intelligence then confirms the correct application. The equipment operation defined as Finite Element Method (FEM) was applied for structural verification; the materials and fabrication processes were detailed; the associated costs were calculated, and the equipment subsets were validated. The design was successfully accomplished, and the imposed requirements were fulfilled, with significant advantages over the current process, providing new knowledge on how semi-automatic systems can be deployed to enhance the productivity and quality of manufacturing processes. The design principles and insights gained from this work can be applied to other automation challenges, particularly where manual processes need to be replaced by more efficient semi-automatic or automatic systems. The modularity of the overall solution and the design concepts of the component inserter, component feeder, and assembly process allow for its use in different assembly scenarios beyond the automotive sector, such as electronics or aerospace, providing a contribution to increased competitiveness and survival in the global market.

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Flexible automation of quality inspection in parts assembly using CNN-based machine learning

Cited by 2 publications

References 9 publications

Artificial Intelligence in planning and control tasks: a study of potential use cases and perceived challenges in Austrian make-to-order companies

Artificial Intelligence in planning and control tasks: a study of potential use cases and perceived challenges in Austrian make-to-order companies

Design of Connector Assembly Equipment for the Automotive Industry

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