This paper examines the implementation of Industry 4.0 elements in enhancing the quality of cables and connectors in the automotive industry, with a focus on meeting ISO 16949 requirements. Modern quality control solutions are presented, including smart sensors, digital twins, and predictive analytics. Special emphasis is placed on multi-stage testing methods and process digitalization for quality monitoring. Through a case study from the company Leoni, the impact of QRQC, Q4.0, and Q-Loop systems on defect reduction in the production of BMW components is analyzed. The paper demonstrates how the integration of Industry 4.0 technologies enhances reliability, efficiency, and compliance with automotive industry standards.

The functional performance and in-service quality of products are strongly influenced by surface roughness, which is a direct outcome of material removal processes. In general, surface roughness is function by the input parameters of the machining process and the extent of tool wear, the increase of which leads to an increase cutting forces, torque, acoustic emission level, vibrations, and temperature. Finding the dependence between machining parameters, tool wear indicators, and surface roughness parameters enables real-time prediction of surface quality and contributes to appropriate processing quality. In this study, based on data obtained through experiment conducted using the Taguchi design of experiment, predictive models were developed using multiple regression analysis and artificial neural networks (ANN). These models establish a relationship between input drilling parameters, axial drilling force, and the maximum height of the surface roughness profile.

This paper presents a model of dependence between the parameters of surface roughness and the parameters of cylindricity and eccentricity in drilling operation for the enhancing steel EN 42CrMo4, hardness 28 HRC, with twist drills DIN 338 made of high-speed steel EN HS6-5-2, with normal blade. The quality of machining, besides the accuracy of measures, completely determined with the values of the parameters of the surface roughness and the parameters of form and location, hence this paper is oriented to the creating models between parameters of the quality of a machined surface and parameters of deviations from form and position. By the developing models based on artificial neural networks and using experimental results, it is possible to analyse the quality of machining on the basis of parameters of a surface roughness.

Given the application of a multiple regression and artificial neural networks (ANNs), this paper describes development of models for predicting surface roughness, linking an arithmetic mean deviation of a surface roughness to a torque as an input variable, in the process of drilling enhancement steel EN 42CrMo4, thermally treated to the hardness level of 28 HRC, using cruciform blade twist drills made of high speed steel with hardness level of 64–68 HRC. The model was developed using process parameters (nominal diameters of twist drills, speed, feed, and angle of installation of work pieces) as input variables varied at three levels by Taguchi design of experiment and measured experimental data for a torque and arithmetic mean deviation of a surface roughness for different values of flank wear of twist drills. The comparative analysis of the models results and the experimental data, acquired for the inputs at the moment when a wear span reaches a limit value corresponding to a moment of the drills blunting, demonstrates that the neural network model gives better results than the results obtained in the application of multiple linear and nonlinear regression models.