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• EARLY DETECTION OF MANUFACTURING DEFECTS IN SMALL-SCALE PRODUCTION USING NEURO-FUZZY SYSTEMS

  S.А. Prokopenko , А. V. Bobryakov

  2026-02-27

  Abstract ▼

  Problem Statement: The increasing demand for higher quality products in small-scale production and the complexity associated with the early detection of manufacturing defects necessitate the development of innovative approaches to predict and control defects at the early stages of manufacturing complex technical objects. Traditional methods applied in mass production settings are unsuitable for small-scale manufacturing due to the high variability of technological processes and the insufficient data required for conventional statistical analyses. The objective of this study is to reduce the incidence of manufacturing defects by identifying deviations at the preparatory stages of production. The proposed solution involves employing neuro-fuzzy systems capable of adaptively forecasting defects based on historical production data. Methods: To address the early detection of manufacturing defects, neuro-fuzzy components based on the fuzzy neuron proposed by Kwan–Cai were employed, integrating expert knowledge with production data. The system includes a training and fine-tuning subsystem consisting of modules for data preparation, validation and normalization, fuzzification of data, and calculation of forecasting errors. Temporal neuro-fuzzy Petri nets were used as structural forecasting elements, enabling the consideration of temporal aspects and uncertainties inherent in manufacturing processes. Novelty: The novel aspects of this research include the utilization of temporal neuro-fuzzy Petri nets and neuro-fuzzy components based on the Kwan–Cai fuzzy neuron, enabling the early detection of defects and the implementation of proactive measures. Another innovative aspect is the approach for integrating neuro-fuzzy methods into existing production management systems. Results: The implementation of the proposed methods resulted in a 15% reduction in manufacturing defects through early identification of deviations and the timely adoption of corrective actions. Developed software tools provide operational analysis of production situations and defect forecasting in near real-time. Practical Significance: The presented solution has been realized as specialized software integrated into existing production systems. It improves the effectiveness of quality management, reduces the costs associated with defect correction, and can be adapted to various small-scale production environments, significantly enhancing their operational performance.