Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The aim of this thesis was to develop and optimize deep learning models specifically designed for the identification of tool wear on microscopic images of cutting tools and cutting tool edges. Cutting tool wear has an impact on dimensional accuracy and surface quality of parts, ultimately affecting the costs associated with meeting part quality criteria.To accomplish this objective, the creation of a tool wear model based on empirical tool life trials was conducted. An outcome of the trials was the generation of a dataset of images, which were then utilized to develop a deep learning model capable of segmenting cutting tool flank wear. To ensure the effectiveness of the deep learning model, a screening analysis was conducted to investigate various dataset properties and model hyperparameters that could influence the quality of predictions. The screening analysis helped identify the key factors that significantly impacted the performance of the model. Building upon the insights gained from the screening analysis, the thesis proceeded with an in-depth investigation of the most influential factors. This investigation led to the development of a decision model that could guide the selection of dataset-specific hyperparameters for optimal performance. To validate the effectiveness of the model optimization strategy, a machine tool integrated measurement setup was employed, utilizing a microscope as well as a camera. These use cases provided a practical assessment of the developed deep learning model and its ability to identify and assess tool wear in a real-world manufacturing scenario.By developing and refining deep learning models for tool wear identification on microscopic images, this thesis contributes to enhancing the understanding and management of tool wear in the manufacturing industry. The optimized models have the potential to facilitate timely maintenance interventions, minimize production errors, and reduce costs associated with part quality deviations. Moreover, the decision model for dataset-specific hyperparameter selection provides a valuable framework for researchers and practitioners working on similar image-based classification problems. 182 pp. Englisch. N� de r�f. du vendeur 9783985552610

Contacter le vendeur

Acheter neuf

EUR 54

Autre devise

Frais de port : EUR 23

De Allemagne vers Etats-Unis

Destinations, frais et d�lais

Quantit� disponible : 2 disponible(s)

Ajouter au panier

Image fournie par le vendeur

Automated Flank Wear Segmentation and Measurement with Deep Learning Image Processing

Carsten Holst

Edit� par Apprimus Verlag Feb 2025, 2025

ISBN 10 : 3985552614 ISBN 13 : 9783985552610

Neuf Taschenbuch

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. Neuware -This thesis optimizes AI models for identifying tool wear on microscopic images of cutting tools. It creates a tool wear model from empirical trials to generate a dataset and conducts screening analysis to find key factors affecting AI performance. A decision model for dataset-specific hyperparameters is developed. The model is validated with practical use cases. The work enhances tool wear management in manufacturing, enabling timely maintenance, reducing scrap, and lowering costs.Books on Demand GmbH, �berseering 33, 22297 Hamburg 182 pp. Englisch. N� de r�f. du vendeur 9783985552610

Contacter le vendeur

Acheter neuf

EUR 54

Autre devise

Frais de port : EUR 60

De Allemagne vers Etats-Unis

Destinations, frais et d�lais

Quantit� disponible : 2 disponible(s)

Ajouter au panier

Image d'archives

Automated Flank Wear Segmentation and Measurement with Deep Learning Image Processing : DE

Carsten Holst

Edit� par Apprimus Verlag, 2025

ISBN 10 : 3985552614 ISBN 13 : 9783985552610

Neuf Taschenbuch

impression � la demande

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The aim of this thesis was to develop and optimize deep learning models specifically designed for the identification of tool wear on microscopic images of cutting tools and cutting tool edges. Cutting tool wear has an impact on dimensional accuracy and surface quality of parts, ultimately affecting the costs associated with meeting part quality criteria.To accomplish this objective, the creation of a tool wear model based on empirical tool life trials was conducted. An outcome of the trials was the generation of a dataset of images, which were then utilized to develop a deep learning model capable of segmenting cutting tool flank wear. To ensure the effectiveness of the deep learning model, a screening analysis was conducted to investigate various dataset properties and model hyperparameters that could influence the quality of predictions. The screening analysis helped identify the key factors that significantly impacted the performance of the model. Building upon the insights gained from the screening analysis, the thesis proceeded with an in-depth investigation of the most influential factors. This investigation led to the development of a decision model that could guide the selection of dataset-specific hyperparameters for optimal performance. To validate the effectiveness of the model optimization strategy, a machine tool integrated measurement setup was employed, utilizing a microscope as well as a camera. These use cases provided a practical assessment of the developed deep learning model and its ability to identify and assess tool wear in a real-world manufacturing scenario.By developing and refining deep learning models for tool wear identification on microscopic images, this thesis contributes to enhancing the understanding and management of tool wear in the manufacturing industry. The optimized models have the potential to facilitate timely maintenance interventions, minimize production errors, and reduce costs associated with part quality deviations. Moreover, the decision model for dataset-specific hyperparameter selection provides a valuable framework for researchers and practitioners working on similar image-based classification problems. N� de r�f. du vendeur 9783985552610

Contacter le vendeur