Modal Analysis Of CNC Milling Machine Bed Made Of Different Alloys Using Finite Element Modeling

Selama bertahun-tahun, ada keperluan kuat pada prediksi perilaku dinamis dari struktur alat mesin dengan mengidentifikasi parameter modal. Machine bed adalah salah satu komponen terpenting dalam mesin frais CNC karena fungsinya secara signifikan memengaruhi kualitas produk akhir. Bed berfungsi sebagai fondasi alat berat, menopang semua komponen penting alat berat. Ini juga digunakan untuk menghindari deformasi yang disebabkan oleh beban statis dan dinamis. Penelitian ini bertujuan untuk membandingkan parameter modal dari beberapa paduan yang berfungsi sebagai bahan alas, seperti baja karbon, baja paduan, dan baja tahan karat, dengan bahan yang digunakan pada umumnya. Model tiga dimensi dan analisis modal machine bed dilakukan menggunakan perangkat lunak Autodesk Inventor. Baja paduan menunjukkan nilai frekuensi alami tertinggi dibandingkan dengan bahan lain karena modulus Young yang tinggi dan kerapatan yang rendah. Bentuk mode serupa dengan besaran bervariasi teramati di semua bahan. Menurut temuan tersebut, baja paduan bisa menjadi alternatif yang layak untuk besi tuang dalam struktur tempat tidur peralatan mesin. Over the years, there has been a strong emphasis on predicting the dynamic behavior of machine tool structures by identifying modal parameters. The machine bed is one of the most significant components in a CNC milling machine because its functionality significantly affects the final product's quality. The bed acts as the machine's foundation, supporting all of the machine's critical components. It is also utilized to avoid deformation caused by static and dynamic loads. This study aims to compare the modal parameters of several alloys used as bed materials, such as carbon steel, alloy steel, and stainless steel, to those of a common material. A three-dimensional model and modal analysis of the bed were conducted in Autodesk Inventor software. Alloy steel shows the highest values of natural frequencies compared to other materials due to its high Young’s modulus and low density. Similar mode shape is observed with varied magnitude in all the materials. According to the findings, alloy steel could be a viable alternative to cast iron in machine tools bed's structur

Static and Dynamic Analyses of Spindle Collet Made of Different Materials Using Finite Element Modeling

The spindle collet, a critical component in various machine tools, plays a pivotal role in determining the success of machining operations. This paper aims to study the static and dynamic parameters of collet structures made from three different materials using the finite element method. A three-dimensional model and computer simulation were conducted in Autodesk Inventor software. Simulations are performed using identical boundary conditions and mesh size. Static analysis is performed with varied applied forces where total deformation and Von Mises stress are measured. For the dynamic analysis, the natural frequencies and mode shapes are measured up to the first five modes. The variations in stress is minimal when the material is altered. The magnitude of deformation varies significantly with changes in material. The relative deformation values demonstrate that carbon steel deforms more than alloy steel by almost 3%, while stainless steel deforms more than alloy steel by 6%. Materials with higher Young's modulus and lower density have been found to increase the natural frequencies, reducing total deformation and von Misses stress. The use of alloy steel in the industry offers an advantage over the other two materials. The results provide improved insight into the appropriate materials for the collet

Hydrological Analysis of Batu Dam, Malaysia in the Urban Area: Flood and Failure Analysis Preparing for Climate Change

Extensive hydrological analysis is carried out to estimate floods for the Batu Dam, a hydropower dam located in the urban area upstream of Kuala Lumpur, Malaysia. The study demonstrates the operational state and reliability of the dam structure based on hydrologic assessment of the dam. The surrounding area is affected by heavy rainfall and climate change every year, which increases the probability of flooding and threatens a dense population downstream of the dam. This study evaluates the adequacy of dam spillways by considering the latest Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) values of the concerned dams. In this study, the PMP estimations are applied using comparison of both statistical method by Hershfield and National Hydraulic Research Institute of Malaysia (NAHRIM) Envelope Curve as input for PMF establishments. Since the PMF is derived from the PMP values, the highest design flood standard can be applied to any dam, ensuring inflow into the reservoirs and limiting the risk of dam structural failure. Hydrologic modeling using HEC-HMS provides PMF values for the Batu dam. Based on the results, Batu Dam is found to have 200.6 m3/s spillway discharge capacities. Under PMF conditions, the Batu dam will not face overtopping since the peak outflow of the reservoir level is still below the crest level of the dam

Comparative assessment of rainfall-based water level prediction using machine learning (ML) techniques

Machine learning (ML) techniques are rapidly emerging as effective tools in predicting complex hydrological processes. The present study aims to comparatively assess the efficacy of four machine learning algorithms – Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Random Forest (RF) – in predicting water levels using rainfall data at the Batu Dam, Malaysia. Situated about 16 km from Kuala Lumpur city center, the Batu Dam plays a crucial role in flood mitigation and water supply. Utilizing a statistical approach, the models were evaluated based on key performance metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2). Preliminary results accentuated the superior predictive prowess of the MLP model, especially for challenging forecasting scenarios with longer lag intervals. This investigation not only accentuates the potential of data-driven methodologies in hydrology but also offers valuable insights for water resource management in the region. When all scenarios for the MLP model are considered, it is observed that the 3-day scenario performed the best within MLP, with the lowest RMSE (at 0.0072) and MAE (at 0.005), and the highest R2 score (at 0.9972). Furthermore, within the MLP model. Due to its exceptionally high performance, the MLP-3 model proved to be an excellent choice for our modeling purposes. Furthermore, it was observed that MLP-3 yields a high R2 score of 0.994, and its predictions aligned closely with the actual water level values. This indicates that the model fits very well to the modeling problem. On the other hand, the SVR-30 model had an R2 score of 0.83, and its predictions were quite scattered with respect to the actual water levels. Four different input scenarios were investigated, considering correlation analysis. Generally, the comparison of four ML model indicated that the MLP model offered better accuracy in predicting daily water levels with respect to different assessment criteria. The findings of this study depicted the accomplishment of MLP model in capturing the changes in the water level of a dam thus paving the way for which the model can be used in works to mitigate potential risk that may occur in the future from natural events