{"id":113965,"date":"2026-06-10T13:44:20","date_gmt":"2026-06-10T11:44:20","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=113965"},"modified":"2026-06-10T16:45:29","modified_gmt":"2026-06-10T14:45:29","slug":"ai-lubrication-thread-forming","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/ai-lubrication-thread-forming\/","title":{"rendered":"AI-Powered Lubrication Strategies for Thread Forming"},"content":{"rendered":"

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Thread forming is widely established in manufacturing as a cost-effective and reliable process for producing internal threads. In contrast to thread cutting, thread forming is a chipless process in which the material is plastically deformed rather than removed. This results in smooth thread flanks, increased strength, and improved surface quality [1, 2]. However, the high contact pressure between the tool and the workpiece poses a challenge for process control. A suitable process-specific lubrication strategy is crucial for minimizing friction and heat generation, reducing tool wear, and ensuring process stability [3]. The current state of the art comprises …<\/div>\n

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\n\n\n\n<\/thead>\n\n\n\n\n\n\n\n\n<\/tbody>\n<\/table>\n

Subscription<\/th>\nincluded<\/th>\nPurchase<\/th>\n
without<\/td>\n−<\/td>\n29,00 \u20ac<\/td>\n
Digital<\/td>\n−<\/td>\n27,55 \u20ac<\/td>\n
Expert<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
Professional<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
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\t\t

Interoperable Data Access in Automotive Body Manufacturing<\/h4>\n
Deterministic integration of structured target parameters into tact-time production<\/div>
Tim Richter<\/a> \n \"ORCID<\/a>, Robert Weidner<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n AI has long been capable of analyzing production processes, yet why is it still so difficult to bring its insights back into production without intermediate steps? In automotive body-in-white mass production, the challenge is less a lack of data than the absence of holistic integration concepts that extend all the way to the machines. This paper demonstrates why bidirectionally communicative information systems are critical to addressing this challenge and identifies the design principles required to effectively integrate AI-generated results into production processes in the future. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 34-42 | DOI 10.30844\/I4SE.26.5.4<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Explaining\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Explaining AI in Industrial Production in an Accessible Way<\/h4>\n
Requirements for AI demonstrators to promote acceptance<\/div>
Jennifer Link<\/a> \n \"ORCID<\/a>, Markus Harlacher<\/a> \n \"ORCID<\/a>, Colin Srebny<\/a>, Sascha Stowasser<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Artificial intelligence (AI) offers a wide range of possibilities in industrial production, but it also presents challenges regarding employee acceptance. AI demonstrators are therefore of central importance, as they enable hands-on experience with AI. However, there has been a lack of systematically identified requirements for demonstrators that specifically promote acceptance and address negative emotions. Using a multi-stage research design, 69 requirements were identified, structured into functional requirements, quality requirements, and boundary conditions. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 6-14 | DOI 10.30844\/I4SE.26.5.1<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"AI-Based\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

AI-Based Building Inspection for Large Structures<\/h4>\n
A new approach to construction progress monitoring<\/div>
Jan Sender<\/a> \n \"ORCID<\/a>, Konrad Jagusch<\/a> \n \"ORCID<\/a>, Michael Geist<\/a> \n \"ORCID<\/a>, David Jericho<\/a> \n \"ORCID<\/a>, Christian Scharr<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Monitoring construction progress, as required in the one-off production of large structures, is very time- and labor-intensive due to a high level of complexity and individuality. The goal of this article is to develop a sensor-based approach for capturing and evaluating multiple inspection characteristics. The use of machine learning models to detect objects and derive relevant information forms the basis for linking current condition to construction schedule. This enables a significant increase in efficiency during construction progress monitoring and a well-founded assessment of progress. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 78-84 | DOI 10.30844\/I4SE.26.5.9<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Experiential\n <\/picture>\n <\/div>\n
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\t\t

Experiential Knowledge Powered by AI\u00a0<\/h4>\n
Practical insights from industry<\/div>
Martin Schmauder<\/a> \n \"ORCID<\/a>, Gritt Ott<\/a> \n \"ORCID<\/a>, Bianca Windisch<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The use of experiential knowledge is a key success factor for companies. Based on four corporate case studies, this article analyzes the technical, organizational, and personnel challenges associated with the use of retrieval-augmented generation (RAG) systems. The results show that the success of such systems depends on the strategic development and maintenance of the knowledge base, as well as on employee engagement. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 128-135 | DOI 10.30844\/I4SE.26.5.15<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Inclusive\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Inclusive Work System Design<\/h4>\n
Automation, standardization, and adaptability<\/div>
Sebastian Schlund<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The design of inclusive work systems is gaining importance due to demographic change and the increasing digital penetration of value creation processes. While traditional ergonomic approaches are based primarily on percentile logic and thus address only a portion of the user population, the integration of digital technologies opens up new possibilities for the dynamic and individualized adaptation of work systems. This article presents a conceptual framework for inclusive work system design that integrates standardization, automation, and adaptability. Methodologically, the article is based on a conceptual analysis of existing approaches from ergonomics and human-centered design. The article makes a theoretical contribution to the systematization of inclusive work system design and identifies areas of focus for further research and industrial practice. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 70-76 | DOI 10.30844\/I4SE.26.5.8<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Work\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Work Design in the Use of Autonomous Systems<\/h4>\n
Addressing the shortage of skilled workers<\/div>
Tim Jeske<\/a> \n \"ORCID<\/a>, Sascha Stowasser<\/a> \n \"ORCID<\/a>, Nicole Ottersb\u00f6ck<\/a>, Sebastian Terstegen<\/a>, Rasmus Adler<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Companies are increasingly challenged to address shortages of skilled workers while meeting rising demands for productivity, flexibility, and innovation. Because labor supply can only be expanded to a limited extent, there is a growing focus on designing work systems with productivity in mind. Autonomous systems offer significant potential in this regard. Their implementation requires not only technical adjustments but, above all, changes in organization, skills, and work design. This article analyzes empirically grounded change requirements in existing work systems as well as associated economic potential. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 44-50 | DOI 10.30844\/I4SE.26.5.5<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

Thread forming requires precise lubricant application because high contact pressures and process temperatures strongly influence tool loading, friction, and process stability. Although minimum quantity lubrication (MQL) systems are widely used, current spray-based approaches can still suffer from spray losses, insufficient wetting of the thread grooves, and unstable droplet transport. This article presents a concept for adaptive precision lubrication in thread forming based on computational fluid dynamics (CFD)-supported flow analysis, experimental validation, and artificial intelligence (AI)-assisted optimization. The focus is on droplet size, spray jet geometry, nozzle position, ambient flow conditions, and their influence on wetting intensity. Preliminary simulation-based investigations indicate that data-driven optimization can help identify wetting deficiencies and support the development of future control strategies for resource-efficient lubricant application.<\/p>\n","protected":false},"featured_media":113808,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[85878,85876,85873,85875,85874,84325,79303,85877,68785],"product_cat":[79304],"topic":[79333,67701],"technology":[67790,67634],"knowhow":[],"industry":[84636,79494],"writer":[85690,85848,85691],"content-type":[83932],"potential":[67894],"solution":[],"glossary":[],"class_list":["post-113965","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-translate-en","category-typeset","tag-ai-based-control","tag-droplet-dynamics","tag-fluid-mechanics","tag-lubricant-application","tag-process-stability","tag-resource-efficiency-en","tag-ressourceneffizienz-en","tag-thread-forming","tag-tool-wear","product_cat-articles","topic-process-optimization","topic-production-system","technology-artificial-intelligence","technology-tools","industry-design","industry-manufacturing-en","writer-christian-donhauser","writer-marco-susic","writer-reinhard-schmied","content-type-article","potential-innovation-en","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff.webp",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-150x150.webp",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-666x375.webp",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-768x432.webp",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-1024x576.webp",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-1032x320.webp",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-764x376.webp",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-392x320.webp",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-608x496.webp",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-640x325.webp",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-274x376.webp",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-514x292.webp",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-320x440.webp",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-514x289.webp",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-196x180.webp",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff.webp",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff.webp",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-510x510.webp",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-510x287.webp",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-100x100.webp",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-64x36.webp",64,36,true]},"uagb_author_info":{"display_name":"Florian Goldmann","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Thread forming requires precise lubricant application because high contact pressures and process temperatures strongly influence tool loading, friction, and process stability. Although minimum quantity lubrication (MQL) systems are widely used, current spray-based approaches can still suffer from spray losses, insufficient wetting of the thread grooves, and unstable droplet transport. This article presents a concept for…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/113965","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/article"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media\/113808"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=113965"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=113965"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=113965"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=113965"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=113965"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=113965"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=113965"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=113965"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=113965"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=113965"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=113965"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=113965"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=113965"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}