In recent years, cutting edge preparation became a topic of high interest in the manufacturing industry because of the important role it plays in the performance of the cutting tool. This paper describes the use of the drag finishing DF cutting edge preparation process on the cutting tool for the broaching process. The main process parameters were manipulated and analyzed, as well as their influence on the cutting edge rounding, material remove rate MRR, and surface quality/roughness (Ra, Rz). In parallel, a repeatability and reproducibility R&R analysis and cutting edge radius r_e prediction were performed using machine learning by an artificial neural network ANN. The results achieved indicate that the influencing factors on r_e, MRR, and roughness, in order of importance, are drag depth, drag time, mixing percentage, and grain size, respectively. The reproducibility accuracy of r_e is reliable compared to traditional processes, such as brushing and blasting. The prediction accuracy of the r_e of preparation with ANN is observed in the low training and prediction errors 1.22% and 0.77%, respectively, evidencing the effectiveness of the algorithm. Finally, it is demonstrated that the DF has reliable feasibility in the application of edge preparation on broaching tools under controlled conditions.

This work describes a machine vision system workflow to automatically estimate the broaching tool wear. The proposed system offers the possibility to evaluate the evolution of wear under different machining conditions and to decide when a tool should be replaced, guaranteeing the quality of the machined part and avoiding catastrophic tool breakage. In addition, the paper discusses the advantages of the proposed method over the traditional and widely used ISO 3685:1993 based methods, which are highly influenced by the operator. The proposed method uses a novel wear area segmentation technique based on Machine Learning artificial intelligence, generating highly reproducible values, saving technicians labor-intensive tasks, and obtaining values with high accuracy. The results show a strong relationship between the values obtained by the proposed automatic method and the experimental ones, with errors below 0.17% and 2.88% corresponding to the MSE and MAE respectively.

Current machine-tool users require more unattended machine centres due to the high competition in the global market of manufacturing. As a result, the control and monitoring systems became of utmost importance to detect the different events that could happen during the machining process. For example, broaching process is critical for firtree machining in turbine and automotive components. Tight tolerances and high productivity are the two main requirements for this kind of operation; therefore, it is essential to capture any anomaly during the process, that could damage the piece. This work proposes an alternative way of monitoring broaching process, based on machine internal signals, instead of the use of expensive force sensors for cutting force measurement. This paper presents an indirect monitoring procedure and its application in AISI 1045 high-speed broaching. The results showed the correlation between cutting force and servomotor power consumption, is as a good parameter in the monitoring process of vertical high-speed broaching forces, capable of capture even changes in the cutting forces range of 500N.

Dimensional tolerances and surface finish are two fundamental elements that must be carefully controlled during the manufacturing of industrial parts. The study of signals obtained from the machining of parts is useful for the control and monitoring of the process. For this reason, correlating these signals and information on the surface quality of the machined part is essential for the early detection of breaches in the quality levels of the parts. Therefore, the objective of this work is to develop an intelligent strategy capable of estimating the artificial geometric error induced in the part during machining. In order train these strategies, the experimental signals of the process and the three-dimensional scanned geometry of the machined part are used as starting data. This development aims to reduce both defects and production time, increasing the efficiency, productive capacity and environmental sustainability of the industrial process.

Sektore aeronautikoan erabiltzen diren super-aleazio termorresistenteak lantzea erronka zaila da, ebaketa-erremintak azkar higatzen baititu. Horregatik, erreminta horien errendimendua hobetzeko, fabrikatzaileek, ebaketa geometriaren eragina ulertzea funtsezkoa dela uste dute. Hori dela eta, artikulu honetan erremintaren ebaketa sorbatzeko erradioak eta jaulkitze- zein azpijan-angeluek daukaten eragina aztertzen da. Abiapuntu gisa, diseinu esperimental (DOE) bat burutu da, non aurrean aipatutako hiru parametroen eragina kuantifikatzen den, Elementu Finituen Metodoaren (EF) bitartez. Ondoren, proba esperimentalei ekin zaie, EF softwaretik lortutako emaitza teorikoak balioztatzeko. Emaitzek erakusten dute ebaketa sorbatzearen erradioak erremintaren higaduran eragin zuzena daukala eta balio egokia aukeratuz erremintaren bizitza erabilgarria areagotzeko gai dela.