This study explores the optimization of leather defect detection through the advanced YOLOv11 model, addressing long-standing challenges in quality control within the leather industry. Traditional inspection methods, reliant on human accuracy ranging between 70% and 85%, have limited leather utilization rates and contributed to substantial material waste. To overcome these limitations, we developed an automated solution leveraging controlled environmental conditions within a custom-designed light chamber. This research specifically targets common defects in leather, such as insect larvae damage and removal cuts, by analyzing both the grain and flesh sides of the material. The results reveal a notable improvement in detection accuracy on the flesh side, achieving 93.5% for grubs and 91.8% for suckout, compared to 85.8% and 87.1% on the grain side. Classification accuracy further demonstrates the advantage of dual-side analysis, with the flesh side reaching 98.2% for grubs and 97.6% for suckout, significantly outperforming the grain side. The dual-side methodology, combined with YOLOv11’s enhanced capabilities, enables the precise identification of subtle defects and offers a transformative approach to leather defect detection. By integrating cutting-edge AI models with standardized digitization environments, this research presents a scalable, highly efficient solution that reduces human error, optimizes leather utilization, and supports industrial sustainability.

This paper conducts an analysis of the application of FMEA in companies operating in the automotive industry within the territory of the Republic of Serbia, focusing on the current situation and future perspectives. The basic characteristics of implementing this analysis are presented, such as the number of employees involved in the implementation process and the prevalence of different types of FMEA in the considered companies. Additionally, the study presents and analyses the needs for methodological improvements and the adoption of modern tools in conducting FMEA. The aim of this research is to present the current situation and challenges in the application of FMEA in the automotive industry in the Republic of Serbia, as well as to identify possibilities for future improvements.

According to the international industry standards, the Process Failure Mode and Effect Analysis (PFMEA) is an obligatory tool for the risk assessment in the automotive industry. Due to FMEA transition in the automotive industry (2019th), PFMEA has been subjected to a series of transformations and improvements, where the substantial change is the risk estimation. However, although it is an essential tool for the reliability assessment, the formal PFMEA is still not sufficiently compatible with the ongoing Industry 4.0 trends. Consequently, practitioners have increasing needs for guides that could support them to implement PFMEA toward Industry 4.0 in their companies. This study aims to ease this shift by proposing a novel model for the assessment of PFMEA maturity level, which considers four PFMEA maturity levels; where the first level corresponds to the manual handling of reports; while the level-four corresponds to the fully automated PFMEA. The overall maturity level was calculated by accounting the eight significant influential factors. Since each of the influential factors has a different degree of influence, their importance can be calculated by applying some Multi-Criteria Decision-Making method or by direct assessment. Finally, the usage of the proposed maturity model was demonstrated by the case study, and obtained results proved that the proposed model is suitable and applicable in a real production environment in the automotive industry, with the emphasis that it could be extended for other industries.

The paper presents a study on optimizing leather defect detection using the YOLOv11 model. Traditional leather processing has faced challenges in quality control, where human inspection accuracy ranges between 70% to 85%, affecting leather utilization rates and resulting in significant material waste. This research introduces an automated solution to improve defect classification and detection accuracy. The study focuses on defects commonly found in leather, such as insect larvae damage and removal cuts, using a specialized light chamber to control environmental variables. By analyzing both grain and flesh sides of the leather, the researchers demonstrated a significant increase in defect detection accuracy, with the flesh side achieving a higher classification rate. YOLOv11's dual-side analysis allowed for clearer identification of subtle defects, leading to a more efficient classification process. The results suggest that integrating advanced AI models like YOLOv11 with controlled digitization environments could drastically reduce human error and improve leather utilization, providing a scalable solution for the leather industry.