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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Reis, A.
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (20/20 displayed)
- 2023An ethics framework for social listening and infodemic managementcitations
- 2023A Review on Direct Laser Deposition of Inconel 625 and Inconel 625-Based Composites-Challenges and Prospectscitations
- 2023Adding Value to Secondary Aluminum Casting Alloys: A Review on Trends and Achievementscitations
- 2023A Predictive Methodology for Temperature, Heat Generation and Transfer in Gigacycle Fatigue Testingcitations
- 2023Infiltration of aluminum in 3D-printed metallic inserts
- 2022Finite Element Analysis of Distortions, Residual Stresses and Residual Strains in Laser Powder Bed Fusion-Produced Components
- 2022Automation of Property Acquisition of Single Track Depositions Manufactured through Direct Energy Depositioncitations
- 2022Thermal study of a cladding layer of Inconel 625 in Directed Energy Deposition (DED) process using a phase-field modelcitations
- 2020Smart Data Visualisation as a Stepping Stone for Industry 4.0-a Case Study in Investment Casting Industrycitations
- 2020Automatic Visual Inspection of Turbo Vanes produced by Investment Casting Processcitations
- 2019Fracture characterization of a cast aluminum alloy aiming machining simulationcitations
- 2019Mechanical characterization of the AlSi9Cu3 cast alloy under distinct stress states and thermal conditionscitations
- 2017Simulation Studies of Turning of Aluminium Cast Alloy Using PCD Toolscitations
- 2017Comparison Between Cemented Carbide and PCD Tools on Machinability of a High Silicon Aluminum Alloycitations
- 2016Laboratory performance of universal adhesive systems for luting CAD/CAM restorative materials
- 2016Development of a Flexible, Light Weight Structure, Adaptable to any Space through a Shape Shifting Featurecitations
- 2016Integrated thermomechanical model for forming of glass containerscitations
- 2012Damage Prediction in Incremental Forming by Using Lemaitre Damage Modelcitations
- 2012Custom Hip Prostheses by Integrating CAD and Casting Technology
- 2002Finite-element simulation and experimental validation of a plasticity model of texture and strain-induced anisotropy
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document
Automatic Visual Inspection of Turbo Vanes produced by Investment Casting Process
Abstract
Visual inspection based systems are important tools to ensure the quality of manufactured parts in industry. This work presents an automatic visual inspection approach for defect detection in turbo vanes in the investment casting industry. The proposed method uses RANSAC for robust line and circle detection to extract relevant information to discriminate between a good part and a defected one. Then, using this data a feature vector is created serving as input to a SVM classifier that after the training phase is able to discriminate and classify between a good sample or not. To test the proposed approach a private database was created containing 650 turbo vanes (which gives 2600 different samples to train and test). On this database the proposed method achieved an average accuracy of 99.96%, an average false negative rate of 0.00% and an average false positive rate of 0.05%, using a 5-fold cross validation protocol, which demonstrates the success of the proposed method. Moreover, the proposed image processing pipeline was deployed into Raspberry Pi 4 Model B part of a visual inspection machine, and is working daily at ZCP-Zollern and Comandita Portugal, which proves the method's robustness. © 2020 Owner/Author.