<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>1933</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>613</pageFirst>
    <pageLast>618</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>98</volume>
    <type>article</type>
    <publisherName>Elsevir</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2021-09-20</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Sustainable Aspects of a Metal Printing Process Chain with Laser Powder Bed Fusion (LPBF)</title>
    <abstract language="eng">Production companies are getting more and more aware of the relevancy of energy costs and the environmental impact of their manufactured products. Hence, the knowledge about the energy intensity of new process technologies as metal printing becomes increasingly crucial. Therefore, data about the energy intensity of entire process chains allow a detailed assessment of the life cycle costs and environmental impact of metal printed parts. As metal printing with Laser Powder Bed Fusion (LPBF) is applied from rapid prototyping to serial manufacturing processes more and more, sustainability data are useful to support a valid scale-up scenario and energetic improvements of the 3D-printing machinery as well as peripheral aggregates used in the process chain. The contribution aims to increase the transparency of the LPBF process chain in terms of its energy consumption. Therefore a generalized model to assess sustainability aspects of metal printed parts is derived. For this purpose, the LPBF process chain with the essential pre-, main- and post-processes is evaluated regarding its energy intensity. Here, the sub-processes, for example wet and dry cleaning of the printer, sieving of the metal powder or sand-blasting of the part are analyzed as well as the main printing process. Based on the derived experimental data from an installed, industry-like process chain, a model is created, which tends to generalize the experimental findings to evaluate other metal printed parts and process chain variants in terms of their energy intensity.</abstract>
    <parentTitle language="eng">Procedia CIRP</parentTitle>
    <identifier type="url">10.1016/j.procir.2021.01.163</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Dennis Ochs</author>
    <author>Kira-Kristin Wehnert</author>
    <author>Jürgen Hartmann</author>
    <author>Andreas Schiffler</author>
    <author>Jan Schmitt</author>
    <collection role="institutes" number="fang">Fakultät für angewandte Natur- und Geisteswissenschaften</collection>
    <collection role="institutes" number="fe">Fakultät Elektrotechnik</collection>
    <collection role="institutes" number="fm">Fakultät Maschinenbau</collection>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="ddc" number="671">Metallverarbeitung und Rohprodukte aus Metall</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <thesisPublisher>Hochschule für Angewandte Wissenschaften Würzburg-Schweinfurt</thesisPublisher>
  </doc>
  <doc>
    <id>5739</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>535</pageFirst>
    <pageLast>543</pageLast>
    <pageNumber>9</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>bookpart</type>
    <publisherName>Springer Nature Switzerland</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Peak Shaving Approach in Manufacturing Combining Machine Learning and Job Shop Scheduling</title>
    <abstract language="eng">Computerized Numerical Control (CNC) plays an important role in highly autonomous manufacturing systems with multiple machine tools. The necessary Numerical Control (NC) programs to manufacture the parts are mostly written in standardized G-code. An a priori evaluation of the energy demand of CNC-based machine processes opens up the possibility of scheduling multiple jobs according to balanced energy consumption over a production period. Due to this, we present a combined Machine Learning (ML) and Job-Shop-Scheduling (JSS) approach to evaluate G-code for a CNC-milling process with respect to the energy demand of each G-command. The ML model training data are derived by the Latin hypercube sampling (LHS) method facing the main G-code operations G00, G01, and G02. The resulting energy demand for each job enhances a JSS algorithm to smooth the energy demand for multiple jobs, as peak power consumption needs to be avoided due to its expense.</abstract>
    <parentTitle language="eng">Lecture Notes in Mechanical Engineering</parentTitle>
    <identifier type="isbn">9783031774287</identifier>
    <identifier type="issn">2195-4356</identifier>
    <identifier type="doi">10.1007/978-3-031-77429-4_59</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T15:40:15Z","timestamp":1736178015423,"version":"3.32.0"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031774287"},{"type":"electronic","value":"9783031774294"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"abstract":"&lt;jats:title&gt;Abstract&lt;\/jats:title&gt;&lt;jats:p&gt;Computerized Numerical Control (CNC) plays an important role in highly autonomous manufacturing systems with multiple machine tools. The necessary Numerical Control (NC) programs to manufacture the parts are mostly written in standardized G-code. An a priori evaluation of the energy demand of CNC-based machine processes opens up the possibility of scheduling multiple jobs according to balanced energy consumption over a production period. Due to this, we present a combined Machine Learning (ML) and Job-Shop-Scheduling (JSS) approach to evaluate G-code for a CNC-milling process with respect to the energy demand of each G-command. The ML model training data are derived by the Latin hypercube sampling (LHS) method facing the main G-code operations G00, G01, and G02. The resulting energy demand for each job enhances a JSS algorithm to smooth the energy demand for multiple jobs, as peak power consumption needs to be avoided due to its expense.&lt;\/jats:p&gt;","DOI":"10.1007\/978-3-031-77429-4_59","type":"book-chapter","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T14:04:11Z","timestamp":1736172251000},"page":"535-543","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Peak Shaving Approach in\u00a0Manufacturing Combining Machine Learning and\u00a0Job Shop Scheduling"],"prefix":"10.1007","author":[{"given":"Eddi","family":"Miller","sequence":"first","affiliation":[]},{"given":"Anna-Maria","family":"Schmitt","sequence":"additional","affiliation":[]},{"given":"Tobias","family":"Kaupp","sequence":"additional","affiliation":[]},{"given":"Rafael","family":"Batres","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Schiffler","sequence":"additional","affiliation":[]},{"given":"Jan","family":"Schmitt","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,7]]},"reference":[{"key":"59_CR1","doi-asserted-by":"crossref","unstructured":"Negi PK, Ram M, Yadav OP (2018) 1 CNC machine and its importance. In: Basics of CNC Programming. River Publishers, pp 1\u201316","DOI":"10.1201\/9781003337317-1"},{"key":"59_CR2","unstructured":"Anderberg S, Kara S (2009) Energy and cost efficiency in CNC machining. In: The 7th CIRP Conference on Sustainable Manufacturing"},{"key":"59_CR3","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.1016\/j.jclepro.2018.10.289","volume":"209","author":"X Gong","year":"2019","unstructured":"Gong X, De Pessemier T, Martens L, Joseph W (2019) Energy-and labor-aware flexible job shop scheduling under dynamic electricity pricing: a many-objective optimization investigation. J Clean Prod 209:1078\u20131094","journal-title":"J Clean Prod"},{"key":"59_CR4","doi-asserted-by":"crossref","unstructured":"Borgia S, Pellegrinelli S, Bianchi G, Leonesio M (2014) A reduced model for energy consumption analysis in milling. Proced CIRP 17:529\u2013534. Variety Management in Manufacturing","DOI":"10.1016\/j.procir.2014.01.105"},{"key":"59_CR5","doi-asserted-by":"crossref","unstructured":"Bhinge R, Park J, Law KH, Dornfeld DA, Helu M, Rachuri S (2016) Toward a generalized energy prediction model for machine tools. J Manufact Sci Eng 139(4)","DOI":"10.1115\/1.4034933"},{"key":"59_CR6","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jclepro.2017.05.013","volume":"161","author":"SJ Shin","year":"2017","unstructured":"Shin SJ, Woo J, Rachuri S (2017) Energy efficiency of milling machining: component modeling and online optimization of cutting parameters. J Clean Prod 161:12\u201329","journal-title":"J Clean Prod"},{"key":"59_CR7","doi-asserted-by":"publisher","first-page":"3864","DOI":"10.1016\/j.jclepro.2015.07.040","volume":"112","author":"J Lv","year":"2016","unstructured":"Lv J, Tang R, Jia S, Liu Y (2016) Experimental study on energy consumption of computer numerical control machine tools. J Clean Prod 112:3864\u20133874","journal-title":"J Clean Prod"},{"key":"59_CR8","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.jclepro.2017.04.096","volume":"157","author":"IF Edem","year":"2017","unstructured":"Edem IF, Mativenga PT (2017) Modelling of energy demand from computer numerical control (CNC) toolpaths. J Clean Prod 157:310\u2013321","journal-title":"J Clean Prod"},{"issue":"2","key":"59_CR9","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1177\/0954405411417673","volume":"226","author":"Y He","year":"2012","unstructured":"He Y, Liu F, Wu T, Zhong F, Peng B (2012) Analysis and estimation of energy consumption for numerical control machining. Proceed Institut Mech Eng, Part B: J Eng Manuf 226(2):255\u2013266","journal-title":"Proceed Institut Mech Eng, Part B: J Eng Manuf"},{"issue":"3","key":"59_CR10","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/0378-3758(94)00035-T","volume":"43","author":"MD Morris","year":"1995","unstructured":"Morris MD, Mitchell TJ (1995) Exploratory designs for computational experiments. J Statis Plann Infer 43(3):381\u2013402","journal-title":"J Statis Plann Infer"},{"issue":"10","key":"59_CR11","doi-asserted-by":"publisher","first-page":"1780","DOI":"10.1080\/00207721.2013.835003","volume":"46","author":"N Pholdee","year":"2015","unstructured":"Pholdee N, Bureerat S (2015) An efficient optimum Latin hypercube sampling technique based on sequencing optimisation using simulated annealing. Int J Syst Sci 46(10):1780\u20131789","journal-title":"Int J Syst Sci"},{"key":"59_CR12","doi-asserted-by":"crossref","unstructured":"Miller E, Kaupp T, Schmitt J (2022) Cascaded scheduling for highly autonomous production cells with agvs. In: Global Conference on Sustainable Manufacturing, pp 383\u2013390. Springer","DOI":"10.1007\/978-3-031-28839-5_43"},{"key":"59_CR13","doi-asserted-by":"crossref","unstructured":"Solano-Rojas BJ, Villal\u00f3n-Fonseca R, Batres R (2023) Micro evolutionary particle swarm optimization (MEPSO): a new modified metaheuristic. Syst Soft Comp, 200057","DOI":"10.1016\/j.sasc.2023.200057"}],"container-title":["Lecture Notes in Mechanical Engineering","Sustainable Manufacturing as a Driver for Growth"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-77429-4_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T15:09:16Z","timestamp":1736176156000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-77429-4_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031774287","9783031774294"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-77429-4_59","relation":{},"ISSN":["2195-4356","2195-4364"],"issn-type":[{"type":"print","value":"2195-4356"},{"type":"electronic","value":"2195-4364"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"7 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"GCSM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Global Conference on Sustainable Manufacturing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Buenos Aires","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Argentina","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"gcsm2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/gcsm.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}</enrichment>
    <enrichment key="local_crossrefDocumentType">book-chapter</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PublisherName,PublisherPlace,TitleMain_1,Language,TitleAbstract_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,IdentifierIsbn,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">Global Conference on Sustainable Manufacturing</enrichment>
    <enrichment key="conference_place">Buenos Aires, Argentinien</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <author>Eddi Miller</author>
    <author>Anna-Maria Schmitt</author>
    <author>Tobias Kaupp</author>
    <author>Rafael Batres</author>
    <author>Andreas Schiffler</author>
    <author>Jan Schmitt</author>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
  </doc>
  <doc>
    <id>5831</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>340-347</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Energy Prediction for CNC Machines Using G-Code Evaluation, Machine Learning and a Real-World Training Part</title>
    <parentTitle language="eng">2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE)</parentTitle>
    <identifier type="doi">10.1109/ICMRE64970.2025.10976308</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Anna-Maria Schmitt</author>
    <author>Eddi Miller</author>
    <author>Andreas Schiffler</author>
    <author>Jan Schmitt</author>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <thesisPublisher>Technische Hochschule Würzburg-Schweinfurt</thesisPublisher>
  </doc>
  <doc>
    <id>5832</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Deep Reinforcement Learning for Adaptive Job Shop Scheduling in Robotic Cells: Handling Disruptions Effectively</title>
    <parentTitle language="eng">2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE)</parentTitle>
    <identifier type="url">10.1109/ICMRE64970.2025.10976238</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <author>Eddi Miller</author>
    <author>Anna-Maria Schmitt</author>
    <author>Tobias Kaupp</author>
    <author>Andreas Schiffler</author>
    <author>Jan Schmitt</author>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <thesisPublisher>Technische Hochschule Würzburg-Schweinfurt</thesisPublisher>
  </doc>
  <doc>
    <id>2994</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>8</pageLast>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Industry 4.0 and International Collaborative Online Learning in a Higher Education Course on Machine Learning</title>
    <parentTitle language="eng">2021 Machine Learning-Driven Digital Technologies for Educational Innovation Workshop</parentTitle>
    <enrichment key="opus.import.data">@inproceedingsmiller2021industry, title=Industry 4.0 and International Collaborative Online Learning in a Higher Education Course on Machine Learning, author=Miller, Eddi and Ceballos, Hector and Engelmann, Bastian and Schiffler, Andreas and Batres, Rafael and Schmitt, Jan, booktitle=2021 Machine Learning-Driven Digital Technologies for Educational Innovation Workshop, pages=1–8, year=2021, organization=IEEE</enrichment>
    <enrichment key="opus.import.dataHash">md5:6e88787dd5868d6c97b77905304b3530</enrichment>
    <enrichment key="opus.import.date">2023-06-13T12:32:02+00:00</enrichment>
    <enrichment key="opus.import.file">/tmp/phpzI1Tx1</enrichment>
    <enrichment key="opus.import.format">bibtex</enrichment>
    <enrichment key="opus.import.id">648861c2a3cdd4.16940320</enrichment>
    <author>Eddi Miller</author>
    <author>Hector Ceballos</author>
    <author>Bastian Engelmann</author>
    <author>Andreas Schiffler</author>
    <author>Rafael Batres</author>
    <author>Jan Schmitt</author>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
  </doc>
  <doc>
    <id>2988</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>298</pageFirst>
    <pageLast>299</pageLast>
    <pageNumber>2</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">C7. 4 Application of Laser Line Scanners for Quality Control during Selective Laser Melting (SLM)</title>
    <parentTitle language="eng">SMSI 2021-System of Units and Metreological Infrastructure</parentTitle>
    <enrichment key="opus.import.data">@articlewehnert2021c7, title=C7. 4 Application of Laser Line Scanners for Quality Control during Selective Laser Melting (SLM), author=Wehnert, K and Schäfer, S and Schmitt, J and Schiffler, A, journal=SMSI 2021-System of Units and Metreological Infrastructure, pages=298–299, year=2021</enrichment>
    <enrichment key="opus.import.dataHash">md5:e423b232255b74ca08e8bd8b5ad1e9b9</enrichment>
    <enrichment key="opus.import.date">2023-06-13T12:32:02+00:00</enrichment>
    <enrichment key="opus.import.file">/tmp/phpzI1Tx1</enrichment>
    <enrichment key="opus.import.format">bibtex</enrichment>
    <enrichment key="opus.import.id">648861c2a3cdd4.16940320</enrichment>
    <author>Kira-Kristin Wehnert</author>
    <author>S Schäfer</author>
    <author>Jan Schmitt</author>
    <author>Andreas Schiffler</author>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
  </doc>
  <doc>
    <id>5629</id>
    <completedYear>2024</completedYear>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>13</volume>
    <type>article</type>
    <publisherName>Elsevier BV</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A simplified machine learning product carbon footprint evaluation tool</title>
    <abstract language="eng">On the way to climate neutrality manufacturing companies need to assess the Carbon dioxide (CO2) emissions of their products as a basis for emission reduction measures. The evaluate this so-called Product Carbon Footprint (PCF) life cycle analysis as a comprehensive method is applicable, but means great effort and requires interdisciplinary knowledge. Nevertheless, assumptions must still be made to assess the entire supply chain. To lower these burdens and provide a digital tool to estimate the PCF with less input parameter and data, we make use of machine learning techniques and develop an editorial framework called MINDFUL. This contribution shows its realization by providing the software architecture, underlying CO2 factors, calculations and Machine Learning approach as well as the principles of its user experience. Our tool is validated within an industrial case study.</abstract>
    <parentTitle language="eng">Cleaner Environmental Systems</parentTitle>
    <identifier type="issn">2666-7894</identifier>
    <identifier type="doi">10.1016/j.cesys.2024.100187</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T02:16:13Z","timestamp":1713406573861},"reference-count":33,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Cleaner Environmental Systems"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1016\/j.cesys.2024.100187","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T02:27:57Z","timestamp":1713234477000},"page":"100187","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["A simplified machine learning product carbon footprint evaluation tool"],"prefix":"10.1016","volume":"13","author":[{"given":"Silvio","family":"Lang","sequence":"first","affiliation":[]},{"given":"Bastian","family":"Engelmann","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1447-7331","authenticated-orcid":false,"given":"Andreas","family":"Schiffler","sequence":"additional","affiliation":[]},{"given":"Jan","family":"Schmitt","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.cesys.2024.100187_bib1","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2019.119661","article-title":"Sustainability assessment and modeling based on su- pervised machine learning techniques: the case for food consumption","volume":"251","author":"Abdella","year":"2020","journal-title":"J. Clean. Prod."},{"issue":"32","key":"10.1016\/j.cesys.2024.100187_bib2","doi-asserted-by":"crossref","first-page":"48424","DOI":"10.1007\/s11356-022-18711-3","article-title":"Influencing factors of carbon emissions and their trends in China and India: a machine learning method","volume":"29","author":"Ahmed","year":"2022","journal-title":"Environ. Sci. Pollut. Control Ser."},{"key":"10.1016\/j.cesys.2024.100187_bib3","series-title":"Data Science Applied to Sustainability Analysis","article-title":"Machine learning in life cycle assessment","author":"Algren","year":"2021"},{"key":"10.1016\/j.cesys.2024.100187_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.resconrec.2021.105810","article-title":"A review of lca assumptions impacting whether landfilling or incineration results in less greenhouse gas emissions","volume":"174","author":"Anshassi","year":"2021","journal-title":"Resour. Conserv. Recycl."},{"key":"10.1016\/j.cesys.2024.100187_bib5","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.jclepro.2017.02.059","article-title":"Is there a simplified lca tool suitable for the agri-food indus- try? an assessment of selected tools","volume":"149","author":"Arzoumanidis","year":"2017","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.cesys.2024.100187_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2020.139407","article-title":"Calculating the carbon footprint in ports by using a standardized tool","volume":"734","author":"Azarkamand","year":"2020","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.cesys.2024.100187_bib7","doi-asserted-by":"crossref","first-page":"2154","DOI":"10.1007\/s11367-020-01821-w","article-title":"Ways to get work done: a review and systematisation of simplification practices in the lca literature","volume":"25","author":"Beemsterboer","year":"2020","journal-title":"Int. J. Life Cycle Assess."},{"key":"10.1016\/j.cesys.2024.100187_bib8","series-title":"Data Analytics in SMEs: Trends and Policies","author":"Bianchini","year":"2019"},{"issue":"2","key":"10.1016\/j.cesys.2024.100187_bib9","doi-asserted-by":"crossref","first-page":"707","DOI":"10.3390\/su13020707","article-title":"Climbing ropes\u2014environmental hotspots in their life cycle and potentials for optimization","volume":"13","author":"Bradford","year":"2021","journal-title":"Sustainability"},{"key":"10.1016\/j.cesys.2024.100187_bib10","series-title":"Informationsblatt CO2- Faktoren. Bundesamt f\u00fcr Wirtschaft und Energie","year":"2023"},{"key":"10.1016\/j.cesys.2024.100187_bib11","series-title":"WSTP South End Plant Process Selection Report","year":"2012"},{"issue":"5","key":"10.1016\/j.cesys.2024.100187_bib12","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.3390\/su16051905","article-title":"Developing a tool for calculating the carbon footprint in smes","volume":"16","author":"Eleftheriadis","year":"2024","journal-title":"Sustainability"},{"issue":"1","key":"10.1016\/j.cesys.2024.100187_bib13","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.idairyj.2013.07.016","article-title":"Method to assess the carbon footprint at product level in the dairy industry","volume":"34","author":"Flysj\u00f6","year":"2014","journal-title":"Int. Dairy J."},{"issue":"2","key":"10.1016\/j.cesys.2024.100187_bib14","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/j.ijpe.2011.12.002","article-title":"Assessing the environmental footprint of manufactured products: a survey of current literature","volume":"146","author":"Gaussin","year":"2013","journal-title":"Int. J. Prod. Econ."},{"issue":"3","key":"10.1016\/j.cesys.2024.100187_bib15","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/s11367-022-02030-3","article-title":"Advances in application of machine learning to life cycle assessment: a literature review","volume":"27","author":"Ghoroghi","year":"2022","journal-title":"Int. J. Life Cycle Assess."},{"key":"10.1016\/j.cesys.2024.100187_bib16","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/s11367-020-01843-4","article-title":"The common understanding of simplification approaches in published lca studies\u2014a review and mapping","volume":"26","author":"Gradin","year":"2021","journal-title":"Int. J. Life Cycle Assess."},{"key":"10.1016\/j.cesys.2024.100187_bib17","series-title":"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow","author":"G\u00e9ron","year":"2022"},{"issue":"1","key":"10.1016\/j.cesys.2024.100187_bib18","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.cirp.2010.03.008","article-title":"Carbon footprint as environmental performance indicator for the manufacturing industry","volume":"59","author":"Laurent","year":"2010","journal-title":"CIRP annals"},{"key":"10.1016\/j.cesys.2024.100187_bib19","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.apenergy.2018.09.182","article-title":"Random forest re- gression for online capacity estimation of lithium-ion batteries","volume":"232","author":"Li","year":"2018","journal-title":"Appl. Energy"},{"key":"10.1016\/j.cesys.2024.100187_bib20","article-title":"An open- source tool to assess the carbon footprint of research","volume":"2","author":"Mariette","year":"2022","journal-title":"Environ. Res.: Infrastruct.Sustain."},{"key":"10.1016\/j.cesys.2024.100187_bib21","series-title":"International Conference on Artificial Intelligence and Soft Computing","first-page":"369","article-title":"Machine learning application in energy con- sumption calculation and assessment in food processing industry","author":"Milczarski","year":"2020"},{"key":"10.1016\/j.cesys.2024.100187_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.scs.2020.102526","article-title":"Machine learning for geographically differentiated climate change mitigation in urban areas","volume":"64","author":"Milojevic-Dupont","year":"2021","journal-title":"Sustain. Cities Soc."},{"issue":"4\u20135","key":"10.1016\/j.cesys.2024.100187_bib23","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1002\/pts.2484","article-title":"A simplified environmental eval- uation tool for food packaging to support decision-making in packaging development","volume":"33","author":"Molina-Besch","year":"2020","journal-title":"Packag. Technol. Sci."},{"key":"10.1016\/j.cesys.2024.100187_bib24","series-title":"Introduction to Machine Learning with Python: A Guide for Data Scientists","author":"M\u00fcller","year":"2018"},{"key":"10.1016\/j.cesys.2024.100187_bib26","series-title":"Proceedings of ICMLCI 2019","article-title":"Advances in machine learning and computational intelligence","author":"Patnaik","year":"2021"},{"key":"10.1016\/j.cesys.2024.100187_bib27","series-title":"Carbon Footprint - Teilgutachten \"Monitoring f\u00fcr den CO2-Aussto\u00df in der Logistikkette\" -. \u00d6ko-Inst. e.V","author":"Schmied","year":"2011"},{"issue":"4","key":"10.1016\/j.cesys.2024.100187_bib28","doi-asserted-by":"crossref","first-page":"2406","DOI":"10.3390\/su13042406","article-title":"Challenges of applying simplified lca tools in sustainable design pedagogy","volume":"13","author":"Suppipat","year":"2021","journal-title":"Sustainability"},{"key":"10.1016\/j.cesys.2024.100187_bib29","series-title":"Probas - prozessorientierte basisdaten f\u00fcr umweltmanagementsysteme","year":"2023"},{"key":"10.1016\/j.cesys.2024.100187_bib30","series-title":"Der carbon fu\u00dfabdruck des \u00f6sterreichischen gesund- heitssektors. Endbericht, Klima- und Energiefonds","author":"Weisz","year":"2019"},{"key":"10.1016\/j.cesys.2024.100187_bib31","article-title":"Quantifying the impact of sustainable product design decisions in the early design phase through machine learning","volume":"vol. 50145","author":"Wisthoff","year":"2016"},{"key":"10.1016\/j.cesys.2024.100187_bib32","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2019.109519","article-title":"Com- parison of regression models for estimation of carbon emissions during building's lifecycle using designing factors: a case study of residential buildings in tianjin, China","volume":"204","author":"Xikai","year":"2019","journal-title":"Energy Build."},{"key":"10.1016\/j.cesys.2024.100187_bib33","article-title":"A review of machine learning applications in life cycle assessment studies","volume":"912","author":"Xue","year":"2024","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.cesys.2024.100187_bib34","series-title":"Carbon Footprint Analysis of Printed Circuit Board","author":"Yung","year":"2018"}],"container-title":["Cleaner Environmental Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666789424000254?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666789424000254?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T03:20:01Z","timestamp":1713324001000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666789424000254"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":33,"alternative-id":["S2666789424000254"],"URL":"http:\/\/dx.doi.org\/10.1016\/j.cesys.2024.100187","relation":{},"ISSN":["2666-7894"],"issn-type":[{"value":"2666-7894","type":"print"}],"subject":["Management, Monitoring, Policy and Law","Environmental Science (miscellaneous)","Renewable Energy, Sustainability and the Environment","Environmental Engineering"],"published":{"date-parts":[[2024,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A simplified machine learning product carbon footprint evaluation tool","name":"articletitle","label":"Article Title"},{"value":"Cleaner Environmental Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cesys.2024.100187","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"100187"}}</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://www.elsevier.com/tdm/userlicense/1.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">SubjectUncontrolled_1,SubjectUncontrolled_2,SubjectUncontrolled_3,SubjectUncontrolled_4,PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PublisherName,TitleMain_1,Language,TitleParent_1,ArticleNumber,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <author>Silvio Lang</author>
    <author>Bastian Engelmann</author>
    <author>Andreas Schiffler</author>
    <author>Jan Schmitt</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Management, Monitoring, Policy and Law</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environmental Science (miscellaneous)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Renewable Energy, Sustainability and the Environment</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environmental Engineering</value>
    </subject>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <collection role="oa-colour" number="">Gefördert (Gold)</collection>
  </doc>
  <doc>
    <id>2003</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>143</pageFirst>
    <pageLast>146</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>5</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2022-03-22</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Internationalisierung in Pandemiezeiten, technische Möglichkeiten, Lehr- und Forschungskonzepte mal anders gedacht</title>
    <abstract language="deu">Eines der zentralen strategischen Ziele unserer Hochschule ist die Internationalisierung, sowie der »internationalisation@home«. Als die weltweite Corona-Pandemie die Präsenzlehre und -forschung ebenso wie den internationalen Austausch von Studierenden und Forschenden zu Beginn 2020 quasi zum Erliegen brachte wurden die Rufe nach digitalen Angeboten im internationalen Bereich schnell laut. Vor diesem Hintergrund reagierte der »Deutsche Akademische Auslandsdienst (DAAD)« mit der kurzfristig ins Leben gerufenen Förderlinie »International Virtual Academic Collaboration« (IVAC), um internationale Hochschulkooperationen und weltweite Mobilität unter digitalen Vorzeichen strategisch zu gestalten und auszubauen [1].</abstract>
    <parentTitle language="deu">FHWS Science Journal</parentTitle>
    <identifier type="url">https://nbn-resolving.org/urn:nbn:de:bvb:863-opus-19389</identifier>
    <identifier type="issn">2196-6095</identifier>
    <identifier type="urn">urn:nbn:de:bvb:863-opus-20035</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="review.accepted_by">2</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Eddi Miller</author>
    <author>Christine Barthelme</author>
    <author>Andreas Schiffler</author>
    <author>Bastian Engelmann</author>
    <author>Jan Schmitt</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>internationalisierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>covid</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>corona</value>
    </subject>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <thesisPublisher>Hochschule für Angewandte Wissenschaften Würzburg-Schweinfurt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-fhws/files/2003/SJ21.2_internationalisierung_pandemie.pdf</file>
  </doc>
  <doc>
    <id>1934</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>684</pageFirst>
    <pageLast>689</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>98</volume>
    <type>article</type>
    <publisherName>Elsevir</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2021-09-20</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Reducing Lifecycle Costs due to Profile Scanning of the Powder Bed in Metal Printing</title>
    <abstract language="eng">First time right is one major goal in powder based 3D metal printing. Reaching this goal is driven by reducing life cycle costs for quality measures, to minimize scrap rate and to increase productivity under optimal resource efficiency. Therefore, monitoring the state of the powder bed for each printed layer is state of the art in selective laser melting. In the most modern approaches the quality monitoring is done by computer vision systems working with an interference on trained neural networks with images taken after exposure and after recoating. There are two drawbacks of this monitoring method: First, the sensor signals - the image of the powder bed - give no direct height information. Second, the application of this method needs to be trained and labeled with reference images for several cases. The novel approach presented in this paper uses a laser line scanner attached to the recoating machine. With this new concept, a direct threshold measure can be applied during the recoating process to detect deviations in height level without prior knowledge. The evaluation can be done online during recoating and feedback to the controller to monitor each individual layer. Hence, in case of deviations the location in the printing plane is an inherent measurement and will be used to decide which severity of error is reported. The signal is used to control the process, either by starting the recoating process again or stopping the printing process. With this approach, the sources of error for each layer can be evaluated with deep information to evaluate the cause of the error. This allows a reduction of failure in the future, which saves material costs, reduces running time of the machine life cycle phase in serial production and results in less rework for manufactured parts. Also a shorter throughput time per print job results, which means that the employee can spent more time to other print jobs and making efficient use of the employee’s work force. In summary, this novel approach will not only reduce material costs but also operating costs and thus optimize the entire life cycle cost structure. The paper presents a first feasibility and application of the described approach for test workpieces in comparison to conventional monitoring systems on an EOS M290 machine.</abstract>
    <parentTitle language="eng">Procedia CIRP 98</parentTitle>
    <identifier type="url">10.1016/j.procir.2021.01.175</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Kira-Kristin Wehnert</author>
    <author>Dennis Ochs</author>
    <author>Jan Schmitt</author>
    <author>Jürgen Hartmann</author>
    <author>Andreas Schiffler</author>
    <collection role="institutes" number="fang">Fakultät für angewandte Natur- und Geisteswissenschaften</collection>
    <collection role="institutes" number="fe">Fakultät Elektrotechnik</collection>
    <collection role="institutes" number="fm">Fakultät Maschinenbau</collection>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="Regensburger_Klassifikation" number="U">Physik</collection>
    <collection role="ddc" number="671">Metallverarbeitung und Rohprodukte aus Metall</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <thesisPublisher>Hochschule für Angewandte Wissenschaften Würzburg-Schweinfurt</thesisPublisher>
  </doc>
</export-example>
