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 re prediction were performed using machine learning by an artificial neural network ANN. The results achieved indicate that the influencing factors on re, MRR, and roughness, in order of importance, are drag depth, drag time, mixing percentage, and grain size, respectively. The reproducibility accuracy of re is reliable compared to traditional processes, such as brushing and blasting. The prediction accuracy of the re 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.
Additive manufacturing by selective laser melting (SLM) allows significant flexibility in obtaining components with complex morphologies, which usually require finishing to correct the geometric distortions and roughness inherent in the process. This paper investigated surface quality by milling and reaming deep holes into ducts obtained by SLM technology. For this purpose, tools with different characteristics were tested, with a reaming stage necessary to obtain roughness levels of less than one μm. Dimensional distortions in SLMed ducts led to substantial variability in axial cutting forces. The helix angle of the endmill had a significant influence on the axial cutting force and roughness.
Manufacturing improvements in terms of manufacturing-chain costs and times reduction are becoming a real need in the industry, especially for roughing operations. To satisfy these industrial requirements, more efficient machining processes have been developed, such as High Efficiency Milling (HEM). According to the above mentioned direction, trochoidal milling appears as an efficient method for roughing operations of full slots. This work presents an analysis in terms of production time between conventional, plunge and trochoidal milling to determine which process configuration is more productive.
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.
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 esperimentalekin ekin da, EF softwaretik lortutako emaitza teorikoak balioztatzeko. Emaitzek erakusten dute ebaketa sorbatzaren erradioak erremintaren higaduran eragin zuzena daukala eta balio egokia aukeratuz erremintaren bizitza erabilgarria areagotzeko gai dela.
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.
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