In this research, an experimental study is presented, which extends the usage of eddy current and load sensors in progressive stamping tools to optimize and continuously monitor the stamping process. The purpose of the research was to automatically detect material scrap before it leaves imprints on the part and based on the sensor’s readings, determine the optimal tool bottom position. The scrap thickness that needs to be detected was established in an experiment by visual evaluation of the result. To determine the optimal bottom position, a linear regression method was used, and the results were evaluated by part quality parameter. The research results consist of separate detection steps and the conclusion was made only after the serial type production. Overall results of scrap detection were influenced by the design of the existing tool. The bottom position detection consists of various readings interpretations and multi-step method descriptions. Based on the acquired results of both methods, implementing the in–die sensors was considered successful and applicable to new tools.

The manufacture of automotive parts using progressive stamping tools demands high precision and efficiency to meet industry standards. This research explores integrating quality monitoring techniques, focusing on load sensors and tonnage monitoring, to enhance the production process. Progressive tools, which perform multiple operations within a single press cycle, are critical for maintaining dimensional accuracy and minimizing defects. This research examines the correlation between real-time load sensor data and the tonnage applied during stamping, aiming to detect anomalies and deviations that may define part quality. By analyzing variations in tool loads and press tonnage, this research identifies patterns that allow the user to connect the applied force with the observed part quality and could help to determine potential issues such as instability of force or part dimensions, tool wear or improper alignment. The results of this research demonstrate that incorporating advanced monitoring systems into progressive stamping processes does not only improve part quality but also extends tool life and reduces downtime. The proposed approach provides a robust framework for ensuring reliability and efficiency in the production of automotive components, aligning with the industry demand for high-quality, cost-effective manufacturing solutions.

The study discusses the accuracy of eddy current sensors in measuring material thickness, during production process with a maximum deviation of 25,3 µm. It presents a comparison table of calibrated plates and sensor readings, highlighting certain discrepancies attributed to measurement techniques, random errors and sensor positions. The experiment's findings show that by implementing sensors it is possible to monitor process in real time and adjust parameters accordingly.

Progressive stamping is a high-efficiency sheet metal forming method in the automotive and mass production industries, where material characteristics significantly influence process stability, cutting force, tool life, and final part quality. Herein, we report the effects of the chemical composition and mechanical properties of DD11 low-carbon steel on punching force during progressive stamping. Ten DD11 material batches with varying chemical compositions and mechanical properties were subjected to experimental investigation. Material characterization involved spectroscopic chemical analysis, tensile testing in accordance with ISO 6892-1, and hardness measurement. Punching tests were performed with a Zwick BZ2-MMAG100.SH01 universal testing machine that incorporates a punch–die assembly to study force–displacement behavior under controlled conditions. The cutting curves of these materials were analyzed to determine the maximum cutting and fracture loads, which were then statistically correlated with the materials’ chemical and mechanical parameters. The results indicated that tensile strength and yield strength are the strongest statistically significant contributors to the maximum cutting load and the fracture point, and that the correlation coefficients for these measurements were +0.866 and +0.869, respectively. Carbon, chromium, and silicon showed the most positive effect on cutting resistance; whereas, titanium was negatively associated with each of the tested responses among chemical composition measures. But none of the chemical factors were statistically significant. The analysis also showed that material hardness yields the highest predictive performance for cutting force behavior (Pearson correlation coefficients up to 0.935 and a regression coefficient of R² = 0.875). Results of this study show that DD11 cutting behavior at progressive stamping is controlled primarily by strength-dependent mechanical characteristics rather than chemical composition variations.

Progressive stamping is widely used for high-volume sheet metal production, where tool condition directly affects stability and product quality. Tool wear, such as punch and die edge rounding, impacts the piercing mechanism and increases process forces and burr formation, and causes part edge deterioration. Therefore, accurate real-time wear monitoring is essential to prevent failures and reduce downtime. This study explores piezo-acoustic sensors for real-time monitoring of punch wear. Experiments on DC01 steel included static and dynamic tests with predefined tool wear. Sensor signals and force data were continuously analyzed. Results show that increased wear leads to higher peak load and delayed material fracture, indicating a shift from shear-dominated piercing to deformation-dominant material separation. Microstructural analysis confirmed that wear significantly alters fracture morphology and subsurface deformation, impacting part quality. Research shows that piezo–acoustic sensing yields the peak load value for punch wear, making it a powerful and useful measure in industry. It provides an effective solution for real-time tool-condition monitoring and predictive maintenance in progressive stamping operations.