Doktora Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14365/8833

Browse

Browsing Doktora Tezleri by Author "Aşıcı, Burçin"

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Thumbnail Image
    Doctoral Thesis
    Motor yatların sıfır hata odaklı üretimi için yapay zeka tabanlı bir ürün tasarım süreci önerisi
    (2026) Aşıcı, Burçin; Ergül, Mustafa Emre
    The product development process for sea vessels is complex, requiring systematic, research-based methods alongside creativity and intuition, all within cost, time, and labor constraints. Technological advances have transformed manufacturing systems and product development, enabling data-driven and predictive approaches. However, current zero-defect manufacturing (ZDM) strategies are primarily limited to production and quality control, with minimal integration into early-stage design decision-making. To address this limitation, a new product design process for motor yachts is proposed, integrating ZDM principles from conceptual design through the final product. This framework extends the Design for Excellence (DfX) approach by embedding predictive artificial intelligence (AI) as a decision-support tool that processes both qualitative and quantitative data throughout the product development process. Unlike conventional methods, the framework enables designers to proactively prevent manufacturing defects by systematically linking design choices to product, process, and stakeholder outcomes. This research employs a practice-led, mixed-methods case study in the industrial motor yacht manufacturing sector. Existing product development processes are analyzed across product, process, and stakeholder dimensions to identify recurring defects and deficiencies. Based on this analysis, a Design for Zero-Defect Manufacturing (DfZDM) framework is developed to enable early detection, prediction, and prevention of defects before physical production. The study advances industrial design research by proposing an integrated, data-informed methodology that expands designers' responsibility to include manufacturability and quality. The findings offer practical, transferable insights for improving complex, low-volume product development and establish a structured approach for embedding ZDM principles into design practice through artificial intelligence-supported workflows.