Search

Titanium is widely acknowledged as a challenging metal due to its inherent characteristics. Manufacturers often encounter challenges such as tool wear, reduced tool lifecycle, and increased cutting heat when working with Ti64. This research investigates the cutting characteristics of additively manufactured (AMed) Ti64 under dry cutting, Minimum Quantity Lubrication (MQL), Cryogenic Carbon dioxide (CO2), and a hybrid approach combining MQL and CO2 conditions. Surface roughness, flank wear, temperature, chip morphology, and microhardness were analyzed to assess the impact of cooling strategies. Results indicate that the hybrid approach outperforms individual methods, showing superior surface finish and reduced tool wear. Machined Surface roughness (Ra) measurements reveal a substantial improvement in the hybrid condition, reducing Ra values by 62.44–67.02%, 35.65–41.38%, and 18.68–27.59% compared to dry, MQL, and CO2. Tool wear assessments exhibit significantly lower flank wear values in the hybrid condition, emphasizing the synergistic benefits of lubrication and cryogenic cooling. This research provides valuable insights into tailoring cooling strategies for optimal precision in machining AMed-Ti64 material, which is crucial for achieving high-quality manufacturing outcomes.

Machine learning (ML) is a subfield of Artificial Intelligence (AI) that uses data, learns the hidden pattern from the data, and makes predictions for future instances with greater accuracy and prediction capabilities, not hallucination (overfitting) with the current data. The application of ML is quite popular in the machining sector because the ML models can be used to predict tool wear, surface roughness and other important machining aspects. With this aim, the present work evaluates the tool wear and class separation by predicting the variation of flank wear (Vb) as a test dataset. A predictive model is proposed for utilizing minimum quantity lubrication (MQL), cryogenic, and MoS2+MQL conditions. Predictions are made using several machine learning (ML) methods, including linear regression, support vector machine, random forest, and multilayer perceptron. The study's findings show that Multi-layer perceptron (MLP) is superior to other methods in classification, with a prediction accuracy of over 95% on average across both training and testing datasets. Even with limited information, the proposed method can help classify situations and recommend the best course of action for machining.

Clinching is a technique for joining metal sheets without the use of welds or screws. This method of joining uses only the energy of the joining process and no consumables to achieve local deformation of metal pieces. It is also a straightforward method for attaching metal sheets with thicknesses between about 0.5 and 3mm, and the maximum joint thickness is about 6mm. Clinching is often reserved for high-volume, low-demand applications like home appliances, HVAC parts, and automobile assemblies. This article describe clinching of cylindrical surfaces on prototype stand with different tools. Explanation of clinching and description of prototype stand for joining cylindrical surfaces. The differences od tooling is made by change of shape and size and analysis samples were made from aluminum 3003. The connection was studied by put into shear strength test and endurance test strength and check under the microscope thickness of the deformed walls.