Multiplier case: AI-supported optimisation of machining processes<\/h2><\/div>Learning scenario and demonstrator with the potential to integrate machine data<\/h4><\/div><\/div><\/div><\/div><\/div>Summary<\/h3><\/div><\/div><\/div>The machining demonstrator showcases the use of AI for the visual analysis of chips during the turning process. The analysis results provide specific insights into how cutting parameters, such as rotational speed or feed rate, can be optimised. This improves the quality of the process and reduces tool wear. The demonstrator also offers the potential to integrate machine data into the analysis, thereby enabling real-time analysis during the process.<\/p>\n<\/div><\/div><\/div>
![](\"https:\/\/humaine.info\/wp-content\/uploads\/2025\/04\/Bildschirmfoto-2025-04-22-um-10.53.25.png\" "\\"Bildschirmfoto")
<\/span><\/div><\/div><\/div>Description of the Approach<\/h3><\/div>The approach to brainstorming and developing the demonstrator was based on a practical problem relating to process monitoring in machining. Implementation was carried out using the CRISP-DM approach. As part of this, relevant chip shapes were identified, produced and photographed in order to train an AI model for visual chip analysis. The analysis results are incorporated into a user interface that provides targeted recommendations for optimising the cutting parameters..<\/p>\n<\/div><\/div><\/div>
Goals & Results<\/h3><\/div>The demonstrator aims to improve the quality and efficiency of turning processes through AI-supported visual machining analysis. It offers the added benefit of enabling the targeted optimisation of cutting parameters such as rotational speed and feed rate, in order to reduce tool wear and stabilise production processes. Furthermore, the demonstrator promotes an understanding of data-driven process optimisation and machine data analysis, for example in workplace training.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Scientific Publications<\/span><\/a><\/h4><\/div>\nArnold, D., B\u00fclow, F., & Kuhlenk\u00f6tter, B. (2025): Referenzarchitektur f\u00fcr Demonstratoren des Maschinellen Sehens: Zur Unterst\u00fctzung einer humanzentrierten Einf\u00fchrung von KI in Industrieunternehmen. W. Kersten, H. Koller, P. Kuhlang & H. L\u00f6dding (Hrsg.). In: Industry 4.0 Science (Jg. 41, Nr. 5, S. 152\u2013160). GITO mbH. <\/span>https:\/\/doi.org\/10.30844\/I4SD.25.5.152
\n <\/a><\/p>\nArnold, D., B\u00fclow, F., & Kuhlenk\u00f6tter, B. (2025): Towards human-centered industrial AI adoption: A reference architecture for machine vision demonstrators. In W. Kersten, H. Koller, P. Kuhlang & H. L\u00f6dding (Hrsg.). In: Industry 4.0 Science (Jg. 41, Nr. 5, S. 152\u2013160). GITO mbH. <\/span>https:\/\/doi.org\/10.30844\/I4SE.25.5.146
\n <\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>
Learning scenario and demonstrator with the potential to integrate machine data<\/h4><\/div><\/div><\/div><\/div><\/div>Summary<\/h3><\/div><\/div><\/div>The machining demonstrator showcases the use of AI for the visual analysis of chips during the turning process. The analysis results provide specific insights into how cutting parameters, such as rotational speed or feed rate, can be optimised. This improves the quality of the process and reduces tool wear. The demonstrator also offers the potential to integrate machine data into the analysis, thereby enabling real-time analysis during the process.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>Description of the Approach<\/h3><\/div>The approach to brainstorming and developing the demonstrator was based on a practical problem relating to process monitoring in machining. Implementation was carried out using the CRISP-DM approach. As part of this, relevant chip shapes were identified, produced and photographed in order to train an AI model for visual chip analysis. The analysis results are incorporated into a user interface that provides targeted recommendations for optimising the cutting parameters..<\/p>\n<\/div><\/div><\/div>
Goals & Results<\/h3><\/div>The demonstrator aims to improve the quality and efficiency of turning processes through AI-supported visual machining analysis. It offers the added benefit of enabling the targeted optimisation of cutting parameters such as rotational speed and feed rate, in order to reduce tool wear and stabilise production processes. Furthermore, the demonstrator promotes an understanding of data-driven process optimisation and machine data analysis, for example in workplace training.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Scientific Publications<\/span><\/a><\/h4><\/div>\nArnold, D., B\u00fclow, F., & Kuhlenk\u00f6tter, B. (2025): Referenzarchitektur f\u00fcr Demonstratoren des Maschinellen Sehens: Zur Unterst\u00fctzung einer humanzentrierten Einf\u00fchrung von KI in Industrieunternehmen. W. Kersten, H. Koller, P. Kuhlang & H. L\u00f6dding (Hrsg.). In: Industry 4.0 Science (Jg. 41, Nr. 5, S. 152\u2013160). GITO mbH. <\/span>https:\/\/doi.org\/10.30844\/I4SD.25.5.152
\n <\/a><\/p>\nArnold, D., B\u00fclow, F., & Kuhlenk\u00f6tter, B. (2025): Towards human-centered industrial AI adoption: A reference architecture for machine vision demonstrators. In W. Kersten, H. Koller, P. Kuhlang & H. L\u00f6dding (Hrsg.). In: Industry 4.0 Science (Jg. 41, Nr. 5, S. 152\u2013160). GITO mbH. <\/span>https:\/\/doi.org\/10.30844\/I4SE.25.5.146
\n <\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>
Summary<\/h3><\/div><\/div><\/div>The machining demonstrator showcases the use of AI for the visual analysis of chips during the turning process. The analysis results provide specific insights into how cutting parameters, such as rotational speed or feed rate, can be optimised. This improves the quality of the process and reduces tool wear. The demonstrator also offers the potential to integrate machine data into the analysis, thereby enabling real-time analysis during the process.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>Description of the Approach<\/h3><\/div>The approach to brainstorming and developing the demonstrator was based on a practical problem relating to process monitoring in machining. Implementation was carried out using the CRISP-DM approach. As part of this, relevant chip shapes were identified, produced and photographed in order to train an AI model for visual chip analysis. The analysis results are incorporated into a user interface that provides targeted recommendations for optimising the cutting parameters..<\/p>\n<\/div><\/div><\/div>
Goals & Results<\/h3><\/div>The demonstrator aims to improve the quality and efficiency of turning processes through AI-supported visual machining analysis. It offers the added benefit of enabling the targeted optimisation of cutting parameters such as rotational speed and feed rate, in order to reduce tool wear and stabilise production processes. Furthermore, the demonstrator promotes an understanding of data-driven process optimisation and machine data analysis, for example in workplace training.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Scientific Publications<\/span><\/a><\/h4><\/div>\nArnold, D., B\u00fclow, F., & Kuhlenk\u00f6tter, B. (2025): Referenzarchitektur f\u00fcr Demonstratoren des Maschinellen Sehens: Zur Unterst\u00fctzung einer humanzentrierten Einf\u00fchrung von KI in Industrieunternehmen. W. Kersten, H. Koller, P. Kuhlang & H. L\u00f6dding (Hrsg.). In: Industry 4.0 Science (Jg. 41, Nr. 5, S. 152\u2013160). GITO mbH. <\/span>https:\/\/doi.org\/10.30844\/I4SD.25.5.152
\n <\/a><\/p>\nArnold, D., B\u00fclow, F., & Kuhlenk\u00f6tter, B. (2025): Towards human-centered industrial AI adoption: A reference architecture for machine vision demonstrators. In W. Kersten, H. Koller, P. Kuhlang & H. L\u00f6dding (Hrsg.). In: Industry 4.0 Science (Jg. 41, Nr. 5, S. 152\u2013160). GITO mbH. <\/span>https:\/\/doi.org\/10.30844\/I4SE.25.5.146
\n <\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>
The machining demonstrator showcases the use of AI for the visual analysis of chips during the turning process. The analysis results provide specific insights into how cutting parameters, such as rotational speed or feed rate, can be optimised. This improves the quality of the process and reduces tool wear. The demonstrator also offers the potential to integrate machine data into the analysis, thereby enabling real-time analysis during the process.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>Description of the Approach<\/h3><\/div>The approach to brainstorming and developing the demonstrator was based on a practical problem relating to process monitoring in machining. Implementation was carried out using the CRISP-DM approach. As part of this, relevant chip shapes were identified, produced and photographed in order to train an AI model for visual chip analysis. The analysis results are incorporated into a user interface that provides targeted recommendations for optimising the cutting parameters..<\/p>\n<\/div><\/div><\/div>
Goals & Results<\/h3><\/div>The demonstrator aims to improve the quality and efficiency of turning processes through AI-supported visual machining analysis. It offers the added benefit of enabling the targeted optimisation of cutting parameters such as rotational speed and feed rate, in order to reduce tool wear and stabilise production processes. Furthermore, the demonstrator promotes an understanding of data-driven process optimisation and machine data analysis, for example in workplace training.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Scientific Publications<\/span><\/a><\/h4><\/div>\nArnold, D., B\u00fclow, F., & Kuhlenk\u00f6tter, B. (2025): Referenzarchitektur f\u00fcr Demonstratoren des Maschinellen Sehens: Zur Unterst\u00fctzung einer humanzentrierten Einf\u00fchrung von KI in Industrieunternehmen. W. Kersten, H. Koller, P. Kuhlang & H. L\u00f6dding (Hrsg.). In: Industry 4.0 Science (Jg. 41, Nr. 5, S. 152\u2013160). GITO mbH. <\/span>https:\/\/doi.org\/10.30844\/I4SD.25.5.152
\n <\/a><\/p>\nArnold, D., B\u00fclow, F., & Kuhlenk\u00f6tter, B. (2025): Towards human-centered industrial AI adoption: A reference architecture for machine vision demonstrators. In W. Kersten, H. Koller, P. Kuhlang & H. L\u00f6dding (Hrsg.). In: Industry 4.0 Science (Jg. 41, Nr. 5, S. 152\u2013160). GITO mbH. <\/span>https:\/\/doi.org\/10.30844\/I4SE.25.5.146
\n <\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>
The approach to brainstorming and developing the demonstrator was based on a practical problem relating to process monitoring in machining. Implementation was carried out using the CRISP-DM approach. As part of this, relevant chip shapes were identified, produced and photographed in order to train an AI model for visual chip analysis. The analysis results are incorporated into a user interface that provides targeted recommendations for optimising the cutting parameters..<\/p>\n<\/div><\/div><\/div>
Goals & Results<\/h3><\/div>The demonstrator aims to improve the quality and efficiency of turning processes through AI-supported visual machining analysis. It offers the added benefit of enabling the targeted optimisation of cutting parameters such as rotational speed and feed rate, in order to reduce tool wear and stabilise production processes. Furthermore, the demonstrator promotes an understanding of data-driven process optimisation and machine data analysis, for example in workplace training.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Scientific Publications<\/span><\/a><\/h4><\/div>\nArnold, D., B\u00fclow, F., & Kuhlenk\u00f6tter, B. (2025): Referenzarchitektur f\u00fcr Demonstratoren des Maschinellen Sehens: Zur Unterst\u00fctzung einer humanzentrierten Einf\u00fchrung von KI in Industrieunternehmen. W. Kersten, H. Koller, P. Kuhlang & H. L\u00f6dding (Hrsg.). In: Industry 4.0 Science (Jg. 41, Nr. 5, S. 152\u2013160). GITO mbH. <\/span>https:\/\/doi.org\/10.30844\/I4SD.25.5.152
\n <\/a><\/p>\nArnold, D., B\u00fclow, F., & Kuhlenk\u00f6tter, B. (2025): Towards human-centered industrial AI adoption: A reference architecture for machine vision demonstrators. In W. Kersten, H. Koller, P. Kuhlang & H. L\u00f6dding (Hrsg.). In: Industry 4.0 Science (Jg. 41, Nr. 5, S. 152\u2013160). GITO mbH. <\/span>https:\/\/doi.org\/10.30844\/I4SE.25.5.146
\n <\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>
The demonstrator aims to improve the quality and efficiency of turning processes through AI-supported visual machining analysis. It offers the added benefit of enabling the targeted optimisation of cutting parameters such as rotational speed and feed rate, in order to reduce tool wear and stabilise production processes. Furthermore, the demonstrator promotes an understanding of data-driven process optimisation and machine data analysis, for example in workplace training.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Scientific Publications<\/span><\/a><\/h4><\/div>\nArnold, D., B\u00fclow, F., & Kuhlenk\u00f6tter, B. (2025): Referenzarchitektur f\u00fcr Demonstratoren des Maschinellen Sehens: Zur Unterst\u00fctzung einer humanzentrierten Einf\u00fchrung von KI in Industrieunternehmen. W. Kersten, H. Koller, P. Kuhlang & H. L\u00f6dding (Hrsg.). In: Industry 4.0 Science (Jg. 41, Nr. 5, S. 152\u2013160). GITO mbH. <\/span>https:\/\/doi.org\/10.30844\/I4SD.25.5.152
\n <\/a><\/p>\nArnold, D., B\u00fclow, F., & Kuhlenk\u00f6tter, B. (2025): Towards human-centered industrial AI adoption: A reference architecture for machine vision demonstrators. In W. Kersten, H. Koller, P. Kuhlang & H. L\u00f6dding (Hrsg.). In: Industry 4.0 Science (Jg. 41, Nr. 5, S. 152\u2013160). GITO mbH. <\/span>https:\/\/doi.org\/10.30844\/I4SE.25.5.146
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Arnold, D., B\u00fclow, F., & Kuhlenk\u00f6tter, B. (2025): Referenzarchitektur f\u00fcr Demonstratoren des Maschinellen Sehens: Zur Unterst\u00fctzung einer humanzentrierten Einf\u00fchrung von KI in Industrieunternehmen. W. Kersten, H. Koller, P. Kuhlang & H. L\u00f6dding (Hrsg.). In: Industry 4.0 Science (Jg. 41, Nr. 5, S. 152\u2013160). GITO mbH. <\/span>https:\/\/doi.org\/10.30844\/I4SD.25.5.152 Arnold, D., B\u00fclow, F., & Kuhlenk\u00f6tter, B. (2025): Towards human-centered industrial AI adoption: A reference architecture for machine vision demonstrators. In W. Kersten, H. Koller, P. Kuhlang & H. L\u00f6dding (Hrsg.). In: Industry 4.0 Science (Jg. 41, Nr. 5, S. 152\u2013160). GITO mbH. <\/span>https:\/\/doi.org\/10.30844\/I4SE.25.5.146
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