This study introduces an artificial neural network (ANN) model for optimizing process parameters to reduce the chances of delamination in carbon fiber-reinforced polymer (CFRP) materials during abrasive water jet (AWJ) piercing. AWJ is a proper method for cutting CFRP. The initial step in this process is AWJ piercing, which creates entry holes in the material to facilitate further cutting operations. However, AWJ piercing is particularly challenging due to the high energy applied to the material. If it is not properly controlled, this high-energy impact can cause material delamination. Avoiding CFRP delamination is a critical aspect when expensive parts are processed with AWJ, particularly in the aerospace and automotive industries. This can compromise the CFRP workpiece, and this induces extra costs for rework. The ANN model was trained using backpropagation to predict delamination. It features a feed-forward architecture that balances model complexity and performance. Validation showed that the ANN model effectively predicted optimal process parameters, eliminating delamination in machined CFRP parts. This study underscores the potential of ANNs in enhancing AWJ piercing processes and provides a robust and reliable method of improving the quality of CFRP parts.

This paper presents a solution aimed at enhancing the accuracy of abrasive waterjet cutting (AWJC) for the processing of carbon-fiber-reinforced polymers (CFRP). Processing CFRP with high accuracy and good surface quality in a short processing time is a difficult task. One crucial problem is the occurrence of shape errors, overcuts, at the cut-in and cut-out point during the cutting process. Shape errors have the potential to create mechanical stress concentrators, which can result in structural failures and compromise the integrity and reliability of components. The primary objective of this study was to gain a comprehensive understanding of the formation mechanism underlying the shape error. The observed shape error is closely associated with both the lead-in/lead-out strategies employed and the process parameters selected. The experimental investigation focused on two commonly used strategies for CFRP cutting: lead-in/lead-out in arc and lead-in/lead-out in line. In order to minimize shape errors, this study proposed a correction method that offers a set of recommendations for selecting the appropriate lead-in/out strategy and a suitable combination of process parameters. Additionally, a mathematical model has been developed to determine the depth of the shape error. The conclusions drawn from this study have been successfully validated through industrial applications.

There are many strategies that the operator can use for hole finishing, depending on the equipment available to him. The issue at hand is choosing the optimal strategy when equipment is available for more than one possible manufacturing method. The present work analyses four types of finishing strategies for holes and determines the advantages and disadvantages of each, from a quality perspective.

Noise vibration harshness (NVH) defines, as a whole, that specific field within automotive industry, that studies mostly the noise and vibrations for different assemblies (such as chassis or drivetrain – gearbox) or complete vehicles, particularly cars and trucks. Gear quality parameters have been studied and it has been experienced that these parameters have an important relevance for NVH topic. Therefore, this paper introduces a case-study, as to highlight the influence of two of these parameters, profile angle deviation (fHα) and tooth trace angle deviation (fHβ), on run-off cycle on test benches, for high-performance automatic transmission, designed for passenger vehicles. The demand for high accuracy is mandatory, so fine adjustments are required, as could be further observed, in order to accomplish the requirements for a lower NVH run-off rate, while the whole life-time.