Smart Manufacturing System for Process Optimization Regarding Deviations among Material Batches
With the recent advances in digital technologies, the subtractive manufacturing industry is striving for smart machine tools, capable of data-driven self-optimization. As a building block, this work proposes an approach for incorporating awareness regarding the material and its batch-specific charac...
में बचाया:
| मुख्य लेखक: | |
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| स्वरूप: | Online |
| भाषा: | अंग्रेज़ी |
| प्रकाशित: |
FAU University Press
2025
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| विषय: | |
| ऑनलाइन पहुंच: | ONIX_20251120T103930_9783961477043_27 |
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कोई टैग नहीं, इस रिकॉर्ड को टैग करने वाले पहले व्यक्ति बनें!
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| _version_ | 1869518958032322560 |
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| author | Lutz, Benjamin Samuel |
| author_browse | Lutz, Benjamin Samuel |
| author_facet | Lutz, Benjamin Samuel |
| author_sort | Lutz, Benjamin Samuel |
| collection | Directory of Open Access Books |
| description | With the recent advances in digital technologies, the subtractive manufacturing industry is striving for smart machine tools, capable of data-driven self-optimization. As a building block, this work proposes an approach for incorporating awareness regarding the material and its batch-specific characteristics for process optimization. The proposed smart manufacturing system utilizes cutting tool images for an initial condition assessment. Methods are proposed for the semantic segmentation of the defect classes encountered in tool condition monitoring, enabling a detailed analysis regarding their presence, location, and size. Furthermore, novel methods are proposed that support the image annotation process and the adaptation of existing training data to new scenes. During machining, internal control data is used for material batch identification. The high-frequency control data is preprocessed, error-compensated, and aggregated into features. Using a novelty detection algorithm, unknown batches are identified. Subsequently, a classification algorithm is used to classify known batches, whereas a clustering approach is used to analyze unknown batches. In a final step, historic process knowledge is used to compute optimized cutting parameters, thus enabling batch-adaptive machining. Furthermore, operational routines are proposed for the automated incorporation of material batches with novel behavior, continuous model improvement, and efficient adaptation to new machining scenarios. |
| format | Online |
| id | doab-20.500.12854ir-169103 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | FAU University Press |
| publisherStr | FAU University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1691032025-11-22T05:58:39Z Smart Manufacturing System for Process Optimization Regarding Deviations among Material Batches Lutz, Benjamin Samuel Intelligente Fertigung Maschinelles Lernen Bildsegmentierung Stoffeigenschaft Prozessoptimierung thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGB Mechanical engineering thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGP Production and industrial engineering thema EDItEUR::U Computing and Information Technology::UX Applied computing::UXT Computer applications in industry and technology thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYT Image processing With the recent advances in digital technologies, the subtractive manufacturing industry is striving for smart machine tools, capable of data-driven self-optimization. As a building block, this work proposes an approach for incorporating awareness regarding the material and its batch-specific characteristics for process optimization. The proposed smart manufacturing system utilizes cutting tool images for an initial condition assessment. Methods are proposed for the semantic segmentation of the defect classes encountered in tool condition monitoring, enabling a detailed analysis regarding their presence, location, and size. Furthermore, novel methods are proposed that support the image annotation process and the adaptation of existing training data to new scenes. During machining, internal control data is used for material batch identification. The high-frequency control data is preprocessed, error-compensated, and aggregated into features. Using a novelty detection algorithm, unknown batches are identified. Subsequently, a classification algorithm is used to classify known batches, whereas a clustering approach is used to analyze unknown batches. In a final step, historic process knowledge is used to compute optimized cutting parameters, thus enabling batch-adaptive machining. Furthermore, operational routines are proposed for the automated incorporation of material batches with novel behavior, continuous model improvement, and efficient adaptation to new machining scenarios. 2025-11-21T05:14:16Z 2025-11-21T05:14:16Z 2025-11-20T09:42:32Z 2024 book ONIX_20251120T103930_9783961477043_27 https://library.oapen.org/handle/20.500.12657/108298 9783961477043 9783961477036 https://directory.doabooks.org/handle/20.500.12854/169103 eng FAU Studien aus dem Maschinenbau open access image/jpeg Attribution-NonCommercial 4.0 International https://library.oapen.org/bitstream/20.500.12657/108298/1/9783961477043.pdf FAU University Press 10.25593/978-3-96147-704-3 10.25593/978-3-96147-704-3 2c600dea-eece-4066-87be-da335e323fdb 9783961477043 9783961477036 208 Erlangen open access |
| spellingShingle | Intelligente Fertigung Maschinelles Lernen Bildsegmentierung Stoffeigenschaft Prozessoptimierung thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGB Mechanical engineering thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGP Production and industrial engineering thema EDItEUR::U Computing and Information Technology::UX Applied computing::UXT Computer applications in industry and technology thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYT Image processing Lutz, Benjamin Samuel Smart Manufacturing System for Process Optimization Regarding Deviations among Material Batches |
| title | Smart Manufacturing System for Process Optimization Regarding Deviations among Material Batches |
| title_full | Smart Manufacturing System for Process Optimization Regarding Deviations among Material Batches |
| title_fullStr | Smart Manufacturing System for Process Optimization Regarding Deviations among Material Batches |
| title_full_unstemmed | Smart Manufacturing System for Process Optimization Regarding Deviations among Material Batches |
| title_short | Smart Manufacturing System for Process Optimization Regarding Deviations among Material Batches |
| title_sort | smart manufacturing system for process optimization regarding deviations among material batches |
| topic | Intelligente Fertigung Maschinelles Lernen Bildsegmentierung Stoffeigenschaft Prozessoptimierung thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGB Mechanical engineering thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGP Production and industrial engineering thema EDItEUR::U Computing and Information Technology::UX Applied computing::UXT Computer applications in industry and technology thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYT Image processing |
| topic_facet | Intelligente Fertigung Maschinelles Lernen Bildsegmentierung Stoffeigenschaft Prozessoptimierung thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGB Mechanical engineering thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGP Production and industrial engineering thema EDItEUR::U Computing and Information Technology::UX Applied computing::UXT Computer applications in industry and technology thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYT Image processing |
| url | ONIX_20251120T103930_9783961477043_27 |
| work_keys_str_mv | AT lutzbenjaminsamuel smartmanufacturingsystemforprocessoptimizationregardingdeviationsamongmaterialbatches |