{"id":{"repo_id":"debrecen","oai_identifier":"oai:dea.lib.unideb.hu:2437/394466"},"canonical_url":"https://search.dev.ndltd.org/etd/debrecen/oai:dea.lib.unideb.hu:2437/394466","repository":{"repo_id":"debrecen","name":"University of Debrecen","base_url":"https://dea.lib.unideb.hu/server/oai/request"},"display":{"title":"HARNESSING BIG DATA FOR ENHANCED OPERATION AND MAINTENANCE IN MECHANICAL ENGINEERING","abstract":"Cubing (Meisterbock) events are vital for quality assurance in the automotive industry, using a CNC machined aluminum body to test parts fitment before production, thus preventing costly delays. However, planning these events has traditionally been labor-intensive and error-prone due to manual CAD data extraction. This thesis introduces an automated macro tool that standardizes and digitizes the planning process, aligning with Industry 4.0 practices. The macro converts CAD data into structured Excel tables, creating an error-free dataset for efficient scheduling. A key innovation is the Change Index system, which flags design changes early, allowing teams to address potential issues before they arise during cubing events. This proactive approach enhances communication among engineers and supply chain teams, transforming the cubing schedule into an early warning system for quality control. Overall, the thesis demonstrates how this macro not only streamlines planning but also embodies the principles of Industry 4.0, paving the way for a more integrated and intelligent automotive manufacturing process.","abstract_html":"Cubing (Meisterbock) events are vital for quality assurance in the automotive industry, using a CNC machined aluminum body to test parts fitment before production, thus preventing costly delays. However, planning these events has traditionally been labor-intensive and error-prone due to manual CAD data extraction. This thesis introduces an automated macro tool that standardizes and digitizes the planning process, aligning with Industry 4.0 practices. The macro converts CAD data into structured Excel tables, creating an error-free dataset for efficient scheduling. A key innovation is the Change Index system, which flags design changes early, allowing teams to address potential issues before they arise during cubing events. This proactive approach enhances communication among engineers and supply chain teams, transforming the cubing schedule into an early warning system for quality control. Overall, the thesis demonstrates how this macro not only streamlines planning but also embodies the principles of Industry 4.0, paving the way for a more integrated and intelligent automotive manufacturing process.","abstract_has_math":false,"creators":["Zaid Al-Kilani, Tariq Mahmoud Shareef"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"DE--Műszaki Kar","school":null,"contributors":[],"advisors":["Deák, Krisztián"],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-27T19:13:46Z","subjects":["Predective","Big Data","Maintenance"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2437/394466","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Deák, Krisztián"]},{"key":"dc:contributor.department","label":"Department","values":["DE--Műszaki Kar"]},{"key":"dc:creator","label":"Author","values":["Zaid Al-Kilani, Tariq Mahmoud Shareef"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-06-24T10:31:25Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-06-24T10:31:25Z"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Predective","Big Data","Maintenance"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/2437/394466"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Cubing (Meisterbock) events are vital for quality assurance in the automotive industry, using a CNC machined aluminum body to test parts fitment before production, thus preventing costly delays. However, planning these events has traditionally been labor-intensive and error-prone due to manual CAD data extraction. This thesis introduces an automated macro tool that standardizes and digitizes the planning process, aligning with Industry 4.0 practices. The macro converts CAD data into structured Excel tables, creating an error-free dataset for efficient scheduling. A key innovation is the Change Index system, which flags design changes early, allowing teams to address potential issues before they arise during cubing events. This proactive approach enhances communication among engineers and supply chain teams, transforming the cubing schedule into an early warning system for quality control. 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However, planning these events has traditionally been labor-intensive and error-prone due to manual CAD data extraction. This thesis introduces an automated macro tool that standardizes and digitizes the planning process, aligning with Industry 4.0 practices. The macro converts CAD data into structured Excel tables, creating an error-free dataset for efficient scheduling. A key innovation is the Change Index system, which flags design changes early, allowing teams to address potential issues before they arise during cubing events. This proactive approach enhances communication among engineers and supply chain teams, transforming the cubing schedule into an early warning system for quality control. 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