HARNESSING BIG DATA FOR ENHANCED OPERATION AND MAINTENANCE IN MECHANICAL ENGINEERING

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.

Department dc:contributor.department

DE--Műszaki Kar

Author dc:creator

• Zaid Al-Kilani, Tariq Mahmoud Shareef

Advisor dc:contributor.advisor

• Deák, Krisztián

• Predective
• Big Data
• Maintenance

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/2437/394466

OAI identifier oai:identifier

oai:dea.lib.unideb.hu:2437/394466