TiN thin films are widely used as protective and decorative coatings for tools in industry. Previous studies have focused on the deposition of TiN coatings on substrates by reactive magnetron sputtering, whereas the use of TiN targets avoids problems such as ‘nitrogen contamination’ and ‘target poisoning’. TiN coatings were grown on silicon wafers and cemented carbide substrates by varying the parameters of the magnetron sputtering plasma source, operating Ar pressure and deposition temperature. The experimental results show the better mechanical properties of ceramic materials deposited using radio frequency (RF) magnetron sputtering. During RF magnetron sputtering, the hardness of the coating increased significantly to 17 Gpa when the deposition working pressure was reduced from 1.5 Pa to 0.5 Pa. The coefficient of friction tends to decrease as the deposition temperature increases, and at 400 °C the coefficient of friction between the deposited film and the friction pair made of Al₂O₃ material is only 0.36. The nano-scratch experimental tests concluded that the TiN coatings deposited at 300 °C conditions had the best adhesion to the substrate at an Ar pressure of 0.5 Pa under an RF source.

In the context of a low-carbon economy, automotive parts supply chains face multifaceted risks, making an effective supply chain risk assessment model a crucial means of ensuring supply chain stability. Traditional evaluation methods struggle to comprehensively and accurately identify all influencing factors and their interrelationships in automotive parts supply chains. This article constructs an evaluation model based on the principle of symmetry. The “structural symmetry” is determined by the ratio of the completeness of risk dimension coverage in the indicator system to the precision of indicators, while “fusion symmetry” refers to the degree of equilibrium in information contribution during the fusion of subjective and objective weights. First, Fault Tree Analysis (FTA) and the Delphi method are adopted to establish a risk evaluation index system, whereby structural symmetry is ensured by the equilibrium between the completeness of risk dimension coverage and the accuracy of indicators in the index system. Second, drawing on the symmetric fusion principle, this study proposes a hybrid evaluation approach integrating hesitant fuzzy DEMATEL with entropy weight-coefficient of variation (HDEC), and the fusion symmetry is guaranteed by the balanced integration of subjective and objective weight information. Finally, a case study of an automotive parts supply chain enterprise quantitatively assesses and ranks risk factors, with corresponding countermeasures proposed. The symmetry-guided HDEC method achieves high accuracy, identifying indicator–causal relationships. Compared with the traditional entropy-weighted AHP algorithm, the Pearson correlation coefficient is 0.8566, and Spearman’s rank correlation coefficient is 0.88, indicating strong weight correlation and robust stability. The integration of mathematical symmetry enhances the model’s theoretical rigor, which aligns with symmetry-oriented optimization research.

The automotive industry is a key pillar of many national economies, and automotive body manufacturing is among the most complex production processes. In the automotive body manufacturing process, quality control and safety assurance are of paramount importance, directly influencing the overall safety performance, structural reliability, and comfort of vehicles. Therefore, it is crucial to analyze the primary factors that influence quality and safety during the car body manufacturing process. The study first focuses on four key processes of car body manufacturing—stamping, welding, painting, and assembly—using the man, machine, material, method, environment (4M1E) framework to analyze the factors affecting quality and safety. Subsequently, a quality and safety early-warning indicator system is established for the automotive body manufacturing process, followed by a comprehensive analysis of the constructed system. To address the issue of subjectivity in traditional technique for order of preference by similarity to an ideal solution (TOPSIS) evaluation methods, this paper employs the coefficient of variation method for objective analysis of criterion-level indicators, the trapezoidal fuzzy number method for subjective analysis of criterion-level indicators, and establishes a model for optimizing target weight that balances subjective and objective approaches. Furthermore, a relative entropy-based method is applied to comprehensively evaluate criterion-level indicators. This approach reduces the information loss associated with separate weighting schemes and overcomes a known limitation of traditional TOPSIS—its inability to distinguish alternatives that lie equidistant from ideal solutions. Finally, an evaluation model for quality and safety influencing factors in body manufacturing is developed and validated through a case study, demonstrating its feasibility. The results show that the proposed model can effectively identify the key quality and safety influencing factors in the automobile body manufacturing process, guarantee quality control and safety assurance in the body manufacturing process, and thus ensure that the automobile production process meets the quality and safety requirements.

On-machine measurement (OMM) enables real-time dimensional feedback in production, yet accuracy is often degraded by thermal drift, sensor noise, and environmental disturbances. This motivates intelligent error-prediction methods to ensure reliable, high-precision machining. This study proposes a hybrid deep learning model integrating a Convolutional Neural Network (CNN), a Bidirectional Long Short-Term Memory (Bi-LSTM) network, and a Whale Optimization Algorithm (WOA) for precise OMM error prediction. Initially, raw measurement data underwent preprocessing to remove noise and outliers. Subsequently, we use a CNN to extract features and a Bi-LSTM to model time-dependent patterns. Finally, WOA optimizes model hyperparameters globally, further boosting predictive accuracy. Comparative experiments show that the proposed model reduces RMSE, MAE, and MAPE by approximately 53.58%, 54.96%, and 57.65%, respectively, while improving the R² score by about 11.17% over baseline methods. Results confirm the method’s superior nonlinear prediction capabilities, significantly enhancing machining accuracy and production efficiency, and demonstrating promising industrial application potential.