[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123010-en":3,"doc-seo-123010-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123010,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Machine Learning for Costing Gas-Turbine Components - Article","Machine Learning (ML), a core enabling technology within Industry 4.0, is presented as an effective and scalable approach for estimating the cost of mechanical components during early design. The work addresses the limited application of cost estimation techniques reported in literature to real industrial settings, such as engineered-to-order products. A method is proposed that builds ML-based parametric cost models by generating training datasets through an analytical automatic cost estimation tool and processing them with CRISP-DM. Two gas-turbine disk cost models are produced and validated using Gradient Boosted Trees, then applied by design engineers in configuring gas-turbine crosssections.","UNIVERSITÀ POLITECNICA DELLE MARCHE Repository ISTITUZIONALE  \nMachine Learning for Costing Gas-Turbine Components  \nThis is the peer reviewd version of the followng article:  \nOriginal  \nMachine Learning for Costing Gas-Turbine Components / Mandolini, Marco; Manuguerra, Luca; Sartini, Mikhailo; Pescatori, Francesco; Lo Presti, Giulio Marcello; Germani, Michele. - (2024), pp. 67-74. ( 3rd International Conference of the Italian Association of Design Methods and Tools for Industrial Engineering, ADM 2023 Florence, Italy 6 September 2023through 8 September 2023) [10.1007/978-3-031-58094-9_ 8] .  \nAvailability:  \nThis version is available at: 11566/329560 since: 2024-05-11T15:27:10Z  \nPublisher:  \nSpringer, Cham  \nPublished  \nDOI:10.1007/978-3-031-58094-9_8  \nTerms of use:  \nThe terms and conditions for the reuse of this version of the manuscript are specified in the publishing policy. The use of copyrighted works requires the consent of the rights’ holder (author or publisher) . Works made available under a Creative Commons license or a Publisher's custom-made license can be used according to the terms and conditions contained therein. See editor’s website for further information and terms and conditions.  \nThis item was downloaded from IRIS Università Politecnica delle Marche ([https://iris.univpm.it](https://iris.univpm.it)) . When citing, please refer to the published version.  \n(Article begins on next page)  \n10 March 2026  \nMachine Learning for Costing Gas-Turbine Components  \nMarco Mandolini1, Luca Manuguerra1, Mikhailo Sartini1, Francesco Pescatori2, Giulio Marcello Lo Presti2, and Michele Germani1  \n1 Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy [m.mandolini@staff.univpm.com](m.mandolini@staff.univpm.com)  \n2 Baker Hughes, Via Felice Matteucci 2, 50127 Firenze, Italy  \nAbstract. Machine Learning (ML), part of Artificial Intelligence, is one of the enabling technologies of Industry 4.0. ML appears to be an effective, affordable, accurate and scalable technique to cost mechanical parts in the early stage of the design process. Despite the cost estimation methods proposed in the literature, their application in specific real industrial contexts (e.g., engineered-to-order products) is minimal.  \nThis paper presents an innovative method for developing ML-based paramet-ric cost models. The training data set is generated thanks to an analytical and automatic software tool for cost estimation. The data is subsequently processed using the Cross Industry Standard Process for Data Mining – CRISP-DM method. CRISP-DM is a process model for data science and representation. It provides an overview of the data mining life cycle. Its flexibility and easy customisation allow the creation of a data mining model that fits the goal of this work.  \nThe proposed method was employed to develop two cost models (semi-finishing and finishing phases) for components (disks) of a gas turbine. Gra-dient Boosted Trees turned out to be the best-performing prediction algorithm. Design engineers successfully used the generated cost models while configuring the gas-turbine crosssection.  \nKeywords: Design to Cost · Cost Estimation · Conceptual Design · Machine Learning · Industry 4.0  \n1 Introduction  \nIn the early stages of developing complex products and technologies, employing decision-making models is a well-established industrial practice. Engineers rely on wellestablished statistical and mathematical models (e.g., data mining, machine learning, and artificial intelligence) to investigate the design space and select the best configurations.  \nNumerous scientific studies targeted at applying and assessing the effectiveness of Machine Learning (ML) approaches for cost estimation during the preliminary design phases characterise the scientific literature on parametric methods [1] . Scientific literature focuses on creating cost models where cost formulas are made using regression  \nanalysis and neural networks [2]. Deep","cbCaioPtzDQdS6vq","https://ap.wps.com/l/cbCaioPtzDQdS6vq","pdf",470239,1,9,"English","en",105,"# Abstract\n# Introduction\n## Background and motivation\n## Related work in parametric cost modelling\n## Machine learning approaches for early-phase cost estimation\n# Proposed method\n## Dataset generation with analytical tooling\n## CRISP-DM workflow\n## ML model training and selection","[{\"question\":\"What problem does the paper target in cost estimation?\",\"answer\":\"The paper targets the limited use of published cost estimation methods in specific real industrial contexts, particularly early-stage conceptual design for engineered-to-order products.\"},{\"question\":\"How are the training datasets generated for the ML cost models?\",\"answer\":\"Training data are generated using an analytical and automatic software tool for cost estimation, then processed through the CRISP-DM data mining workflow.\"},{\"question\":\"Which algorithm performed best for predicting gas-turbine component costs?\",\"answer\":\"Gradient Boosted Trees produced the best-performing predictions among the considered options.\"}]","Machine Learning for Costing Gas-Turbine Components - 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