Impact of Cutting Parameters on Cutting Force of AISI 410 and AISI 420 MSS during CNC Dry Milling

A CNC dry milling experiment was conducted for the machining parameter optimization of two grades of Martensitic Stainless steel (MSS). Optimization is done by employing Taguchi method (S/N ratio and ANOVA). The specimens used are MSS grades 410 and 420.The experiments were designed by employing L9 orthogonal array for 3 levels of feed and spindle speeds. The impact of these parameters on cutting force was analyzed. The analysis reveals that spindle speed constitute the maximum impact on cutting force for both MSS grades. Optimum cutting parameters are obtained at 30 mm/min (feed rate) and 1500 rpm (spindle speed). Due to higher Chromium and Carbon content in AISI 420 MSS resulted higher cutting force values compared with AISI 410 MSS. Optimum values of cutting parameters are estimated for improving productivity and quality. The predicted values at optimal conditions are estimated. The results indicate a good conformity with the outcome of experiment.

A Data-driven Minimum Stiffness Prediction Method for Machining Regions of Aircraft Structural Parts

Large thin-walled structural parts have been widely used in aircrafts for the purpose of weight reduction. These parts usually contain various thin-walled complex structures with weak local stiffness, which are easy to deform during machining if improper cutting parameters are selected. Thus, local stiffness has to be seriously considered during the machining parameter planning. Existing stiffness calculation methods mainly include mechanics calculation methods, empirical formula methods, finite element methods, and surrogate-based methods. However, due to the structural complexity, these methods are either inaccurate or time consuming. To address this issue, this paper proposes a data-driven method for stiffness prediction of aircraft structural parts. First, machining regions of aircraft structural part finishing are classified into bottom, sidewall, rib and corner to further define the minimum stiffness of machining regions. Then, by representing the part geometry with attribute graph as the input feature, while computing the minimum stiffness using FEM as the output label, stiffness prediction is turned to a graph learning task. Thus, a graph neural network (GNN) is designed and trained to map the attribute graph of a machining region to its minimum stiffness. In the case study, a dataset of aircraft structural parts is used to train four GNN models to predict the minimum stiffness of the defined four types of machining regions. Compared with FEM results, the average percentage errors on the test set are 6.717%, 7.367%, 7.432% and 5.962% respectively. In addition, the data driven model once trained, can greatly reduce the time in predicting the stiffness of a new part compared with FEM, which indicates that the proposed method can meet the engineering requirements in both accuracy and computational efficiency.

Research on machining parameter optimization in finishing milling with multiple constraints

High efficiency and high precision milling, as the eternal goal of CNC machining, needs to balance many constraints for selecting the most reasonable processing parameters. This paper presents an efficient machining parameter optimization method for finishing milling operation with multiple constraints. Firstly, under the multiple constraints of parameter feasible region, milling force, milling stability, roughness, and machining contour accuracy, a multi-variable parameter optimization model with machining efficiency as the objective is established. A four level cycle optimization strategy has been detailly described for solving the optimization problem, in which the feed per tooth is optimized by using the golden section method, and with the aid of the random vector search method, the spindle speed, radial, and axial depth cuts are both numerically iterated. The optimal machining parameter combination of the tooth number, feed per tooth, spindle speed, radial, and axial depth of cuts are achieved at last. Finally, the experimental verification results show that the proposed method can greatly improve the machining efficiency under chatter free condition and achieve an efficient finishing milling with consideration of the multiple constraints.

Optimization of micro-EDM drilling on titanium alloy (Ti-6AL-4V) using RSM and Neural Network

In modern manufacturing industries, micro machining technology is widely used to machine micro parts for various applications such as in MEMS, die and tool industries, etc. Micro electric discharge machining (Micro-EDM) is widely used in die and tool making. This paper investigates three different input machining parameters such as pulse on time, pulse off time, and servo voltage of micro electric discharge machining performances of tool wear (TW) and Diametrical accuracy (DA) of a hole on titanium alloy (Ti-6Al-4V) using copper micro electrodes of ϕ 400μm. The experiments ate conducted out with the Box-Behnken design of Response Surface Methodology (RSM). The neural network is used for the optimization of multi response by fitting the regression model. ANOVA is also performed to find the significant contribution of the machining parameter. The predicted optimal machining values with the maximum error of 12.72% for tool wear and 8.78% for diametrical accuracy was achieved on comparing with experimental results.

Optimization of Laser Parameters and Dimple Geometry Using PCA-Coupled GRG

Stainless steel (SS316L) is applied in numerous fields due to its intrinsic properties. In this study, micro-dimples were fabricated on SS316L. The effects of laser process parameters, such as frequency, average power, and pulse duration, on the average dimple diameter, dimple distance, and dimple depth were studied using an L9 orthogonal array. The analysis of variance (ANOVA) and multi-objective optimization technique, principal-component-analysis-coupled grey relational grade (GRG), was used to optimize laser process parameters on output responses. The optimal machining parameter settings obtained for the highest GRG peak value of 0.2642 are 15 kHz (frequency), 12 W (average power), and 1500 ns (pulse duration). The ANOVA results showed that average power is the most influential factor, contributing 86.40 % to performance measures (average dimple diameter (φ), dimple distance (d), and depth (l). Moreover, the effect of process parameters was studied using mean effect plots, and the micro-dimple quality was analysed using SEM micrographs.

Experimental investigation and optimization of machining performance characteristics during WEDM of inconel 718: On evaluation of wire electrodes and advanced parameter techniques

Wire Electrical-Discharge-Machining (WEDM) is a well-known unconventional machining process to produce intricate shapes. However, obtaining a satisfactory WEDM cutting performance is indeed a challenging task during precision cutting. Hence, this investigation aims to attempt a favorable machining parameter setting in order to corner-cutting during WEDM for In-718. Here, machining performance characteristics have been considered based on corner deviation (CD) along with Material Removal Rate (MRR) and surface roughness (SR). Taguchi’s experiment design technique (L16) has been considered to run the experiments. The controllable process parameters are considered as Spark-on-time (Son), flushing-pressure (Fp), wire-tension (Tw), and discharge-current (Id). The aforesaid machining performance characteristics have been achieved through the two most popular wire electrodes, i.e., Zinc-coated brass electrode (Zn-BE) and Brass Wire Electrode (BWE), and compared the results. The comparison of performances by the wire electrodes on CD, MRR, and SR varied from 0.0286 mm to 0.0844 mm, 0.0045 g/min to 0.0214 g/min and 3.12 µm to 4.80 µm for BWE and 0.0218 mm to 0.0783 mm, 0.0090 g/min to 0.0342 g/min and 2.58 µm to 4.40 µm for Zn-BE respectively. However, machining with Zn-WE yields reduced CD, SR, and increased MRR value and shows less defect on the WEDMed surfaces than its counterpart. The present study developed the mathematical model based on non-linear regression for correlating the machining parameters with the machining responses. The next step of this study is that a unique optimization strategy, namely grey relation analysis (GRA) integrated with Teaching Learning-Based Optimization (TLBO), has been implemented for achieving optimal parametric setting. The satisfactory machining setting obtained from GRA-TLBO has been compared with GRA-JAYA and GRA-genetic algorithm (GA). The proposed methodology appears more fruitful in terms of computational time and effort.

Multi-Performance Optimization in Electrical Discharge Machining of Al2O3 Ceramics Using Taguchi Base AHP Weighted TOPSIS Method

The main application of electrical discharge machining in ceramic processing is limited to conductive ceramics. However, the most commonly used non-conductive potteries in modern industry, such as aluminum oxide (Al2O3), also reveal the limitations of choosing a suitable process. In this study, Taguchi based TOPSIS coupled with AHP weight method to optimize the machining parameters of EDM on Al2O3 leads to better multi-performance. The results showed that the technique is suitable for tackling multi-performance machining parameter optimization. The adhesive foil had a significant impact on material removal rate, electrode wear rate, and surface roughness, according to the findings. In addition, the response graph of relative closeness is used to determine the optimal combination levels of machining parameters. A confirmation test revealed a good agreement between predicted and experimental preference values at an optimum combination of the input parameters. The suggested experimental and statistical technique is a simple, practical, and reliable methodology for optimizing EDM process parameters on Al2O3 ceramics. This approach might be utilized to optimize and improve additional process parameters in the future.

Optimization of electrical discharge machining processing for ceramics using grey-taguchi system

Electrical discharge machining (EDM) is one of the importantly non-traditional processing technologies employed for ceramics’ surface processing. Modeling and optimization of the EDM process are essentially applied to find and obtain the optimal values of the responses for materials having smaller surface roughness, higher removing rate of materials, lower electrode wear rate. In this study, the Grey-Taguchi system with AHP weighting was applied in order to optimize the multi-responses of the EDM processing for ceramics. When the EDM processing was used in the ZrO2 ceramics for adhesive metal foils, the multi-response gray relational grade for the optimal level of machining parameter was 0.2685, which was higher than those using the initial experimental conditions. This study has proven that using the Grey-Taguchi system method with AHP weighting to find a model with a highly efficient standard for optimizing differently advanced machining processes is profitable.