Search Results (32)

Wire electrical discharge machining (WEDM) is a standard micro-manufacturing technology. In WEDM, surface roughness (SR), deviation dimension (DD), and machining time (MT) are critical requirements that impact machining quality and are affected by various input parameters. The workpiece often performs multiple machining steps (roughing, semi-finishing, and finishing) to achieve high accuracy. Each machining step directly affects the accuracy and machining time, and the preceding machining step influences the subsequent machining step parameters. Many input control parameters regulate WEDM’s performance. Thus, optimizing process control parameters at each step is essential to achieve optimal results. This study investigates the influence of input parameters, including pulse on time (Ton), pulse off time (Toff), and servo voltage (SV), on SR, DD, and MT when machining AISI D2 mold steel through rough, semi-finish, and finish cutting. Taguchi and Analysis of Variance (ANOVA) are applied to analyze and optimize this WEDM process. The results display that the optimal surface roughness values for rough, semi-finish, and finish-cut stages are 2.03 µm, 1.77 µm, and 0.57 µm, corresponding to the parameter set of Ton = 6 μs, Toff = 10 μs, and SV = 30 V; Ton = 3 μs, Toff = 15 μs, and SV = 60 V; and Ton = 21 μs, Toff = 45 μs, and SV = 60 V, respectively. In addition, in the finish-cut stage, the parameters for optimal DD of 0.001 mm (0.04%) are Ton = 3 μs, Toff = 15 μs, and SV = 40 V. In contrast, those values for optimal MT of 218 s are Ton = 3 μs, Toff = 30 μs, and SV = 40 V. All optimal input values are confirmed by the manufacturing mold and die parts. Full article

With the aim of improving the machined surface quality of die steel, this paper takes Cr12MoV quenched die steel as the research object and proposes a ball head milling surface morphology prediction model that comprehensively considers influencing factors, including tool vibration, eccentricity, as well as deformation. By setting key parameters, such as line spacing, feed per tooth, cutting depth, and phase difference, the system analyzed the influence of each parameter on the residual height and surface roughness of the machined surface. High-speed milling experiments were conducted, and the surface morphology of the samples was observed and measured under a microscope. The simulation results show good agreement with the experimental data, with errors within 7%~15%, proving the accuracy of the model. This study can provide theoretical support and methodological guidance for surface quality control and processing parameter optimization in complex mold surface machining. Full article

The main aim of this Special Issue was to present the current state of the research on the subjects of theory, modeling, monitoring, and control of the operation of technology systems and processes, along with research and diagnostics of manufacturing systems and processes operation. The contributions have focused on manufacturing research, operation reliability, and diagnostics of machines; inspection, measurements, evaluation, and diagnostics of production quality in technologies of standard and progressive machining; reversible engineering; 3D printing; pressure die casting; injection molding; EDM; AWJ cutting; etc., which are used for advanced processing of materials and various kinds of technological applications. Full article

In hard milling, there has been a significant surge in demand for sustainable machining techniques. Research indicates that the Minimum Quantity Lubrication (MQL) method is a promising approach to achieving sustainability in milling processes due to its eco-friendly characteristics, as well as its cost-effectiveness and improved cooling efficiency compared to conventional flood cooling. This study investigates the end milling of AISI H11 die steel, utilizing a cooling system that involves a mixture of graphene nanoparticles (Gnps) and sesame oil for MQL. The experimental framework is based on a Taguchi L36 orthogonal array, with key parameters including feed rate, cutting speed, cooling condition, and air pressure. The resulting outcomes for cutting zone temperature and surface roughness were analyzed using the Taguchi Signal-to-Noise ratio and Analysis of Variance (ANOVA). Additionally, an Adaptive Neuro-Fuzzy Inference System (ANFIS) prediction model was developed to assess the impact of process parameters on cutting temperature and surface quality. The optimal cutting parameters were found to be a cutting speed of 40 m/min, a feed rate of 0.01 mm/rev, a jet pressure of 4 bar, and a nano-based MQL cooling environment. The adoption of these optimal parameters resulted in a substantial 62.5% reduction in cutting temperature and a 68.6% decrease in surface roughness. Furthermore, the ANFIS models demonstrated high accuracy, with 97.4% accuracy in predicting cutting temperature and 92.6% accuracy in predicting surface roughness, highlighting their effectiveness in providing precise forecasts for the machining process. Full article

Electrical erosion molding (EDM) is an unconventional machining technology widely used in the manufacture of injection molds for plastics injection molding for the creation of complex cavities and geometries. However, EDM productivity can be challenging, directly influencing mold manufacturing time and cost. This work aims to improve EDM productivity in the context of mold manufacturing for plastics injection molding. The research focuses on the optimization of processing parameters and strategies to reduce manufacturing time and increase process efficiency. Through a rigorous experimental approach, this work demonstrates that the optimization of EDM parameters and strategies can lead to significant productivity gains in the manufacture of plastic injection molds without compromising part quality and accuracy. This research involved a series of controlled experiments on a Mitsubishi EA28V Advance die-sinking EDM machine. Different combinations of pre-cutting parameters and processing strategies were investigated using copper electrodes on a heat-treated steel plate. Productivity was evaluated by measuring the volume of material removed, and geometrical accuracy was checked on a coordinate measuring machine. The experimental results showed a significant increase in productivity (up to 61%) by using the “processing speed priority” function of the EDM machine, with minimal impact on geometric accuracy. Furthermore, the optimized parameters led to an average reduction of 12% in dimensional deviations, indicating improved geometric accuracy of the machined parts. This paper also provides practical recommendations on the selection of optimal EDM processing parameters and strategies, depending on the specific requirements of plastic injection mold manufacturing. Full article

The selection of the right tool path trajectory and the corresponding machining parameters for end milling is a challenge in mold and die industries. Subsequently, the selection of appropriate tool path parameters can reduce overall machining time, improve the surface finish of the workpiece, extend tool life, reduce overall cost, and improve productivity. This work aims to establish the performance of end milling process parameters and the impact of trochoidal toolpath parameters on the surface finish of AISI D3 steel. It especially focuses on the effect of the tool tip nose radius deviation on the surface quality using precision measurement techniques. The experimental design was carried out in a systematic manner using a face-centered central composite design (FCCD) within the framework of response surface methodology (RSM). Twenty different experiment trials were conducted by changing the independent variables, such as cutting speed, feed rate, and trochoidal pitch distance. The main effects and the interactions of these parameters were determined using analysis of variance (ANOVA). The optimal conditions were identified using a multiple objective optimization method based on desirability function analysis (DFA). The developed empirical models showed statistical significance with the best process parameters, which include a feed rate of 0.05 m/tooth, a trochoidal pitch distance of 1.8 mm, and a cutting speed of 78 m/min. Further, as the trochoidal pitch distance increased, variations in the tool tip cutting edge were observed on the machined surface due to peeling off of the coating layer. The flaws on the tool tip, which alter the edge micro-geometry after machining, resulted in up to 33.83% variation in the initial nose radius. Deviations of 4.25% and 5.31% were noted between actual and predicted values of surface roughness and the nose radius, respectively. Full article

In the die and mold industry, tempered JIS SKD11 steel is selected to manufacture cold-forming dies that require an optimum balance of toughness, strength, and wear resistance. Therefore, the machinability of tempered JIS SKD11 in the milling machining process is challenging. The use of eco-friendly machining settings is intended to diminish tool wear and enhance the quality of the machined surface as well as the accuracy of the machined components. Adapting to the aforementioned factors for cold-forming dies is a pivotal issue. In this study, the machinability of tempered JIS SKD11 steel was analyzed under dry, MQL, cryogenic cooling with liquid nitrogen (LN₂), and liquid carbon dioxide (LCO₂) machining settings during open slot milling operations with varying input parameters, including cutting speeds and cutting feeds. An in-depth evaluation of output responses, including tool wear, surface roughness, cutting temperature in the cutting zone, and microhardness of the machined surface, was also conducted. The findings unveiled that the flank wear of the cutters and surface roughness of the machined surfaces obtained minimum values of 0.22 mm and 0.197 µm, respectively, during open slot milling operations at a cutting speed of 100 m/min and a cutting feed of 204 mm/min under cryogenic cooling with liquid carbon dioxide (LCO₂). The findings from this study suggest that employing cryogenic cooling with LCO₂ could serve as a viable substitute for dry, MQL, and cryogenic cooling with LN₂ methods to enhance the machinability of hardened JIS SKD11 steel. Full article

Wire electric discharge machine (WEDM) is a process used popularly in microsystems, tool and die industries, medicine, transportation, and spacecrafts to create intricate portions with high dimensional accuracy and surface finish. It is employed to process superalloys and materials made of composites which are conductive and strong materials. From the literature, an analysis of the WEDM process on different materials revealed that there were many variables involved and that each process parameter influences the different response variables. The removal process of a spark discharge for an inclined angle during the cutting of 3D profiles has different applications. Also, types of dielectric fluid, and the influence of wire material, diameter and pressure, wire tension, feed, Ton, Toff, current, and voltage on machining characteristics—like kerf, MRR, wire wear, surface finish and its characteristics, dimensional deviations, and corner errors—and on a variety of materials like Inconel, nickel, titanium, WC, steels, and other superalloys and composites (MMCs and CMCs) during taper WEDM were reviewed. Full article

The most effective and cutting-edge method for achieving a 0.004 mm precision on a typical material is to employ die-sinking electrical discharge machining (EDM). The material removal rate (MRR), tool wear rate (TWR), residual stresses, and crater depth were analyzed in the current study in an effort to increase the productivity and comprehension of the die-sinking EDM process. A parametric design was employed to construct a two-dimensional model, and the accuracy of the findings was verified by comparing them to prior research. Experiments were conducted utilizing the EDM machine, and the outcomes were assessed in relation to numerical simulations of the MRR and TWR. A significant temperature disparity that arises among different sections of the workpiece may result in the formation of residual strains throughout. As a consequence, a structural model was developed in order to examine the impacts of various stress responses. The primary innovations of this paper are its parametric investigation of residual stresses and its use of Haynes 25, a workpiece material that has received limited attention despite its numerous benefits and variety of applications. In order to accurately forecast the output parameters, a deep neural network model, more precisely, a multilayer perceptron (MLP) regressor, was utilized. In order to improve the precision of the outcomes and guarantee stability during convergence, the L-BFGS solver, an adaptive learning rate, and the Rectified Linear Unit (ReLU) activation function were integrated. Extensive parametric studies allowed us to determine the connection between key inputs, including the discharge current, voltage, and spark-on time, and the output parameters, namely, the MRR, TWR, and crater depth. Full article

The increasing demand for customized products is a core driver of novel automation concepts in Industry 4.0. For the case of machining complex free-form workpieces, e.g., in die making and mold making, individualized manufacturing is already the industrial practice. The varying process conditions and demanding machining processes lead to a high relevance of machining domain experts and a low degree of manufacturing flow automation. In order to increase the degree of automation, online process monitoring and the prediction of the quality-related remaining cutting tool life is indispensable. However, the varying process conditions complicate this as the correlation between the sensor signals and tool condition is not directly apparent. Furthermore, machine learning (ML) knowledge is limited on the shop floor, preventing a manual adaption of the models to changing conditions. Therefore, this paper introduces a new method for remaining tool life prediction in individualized production using automated machine learning (AutoML). The method enables the incorporation of machining expert knowledge via the model inputs and outputs. It automatically creates end-to-end ML pipelines based on optimized ensembles of regression and forecasting models. An explainability algorithm visualizes the relevance of the model inputs for the decision making. The method is analyzed and compared to a manual state-of-the-art approach for series production in a comprehensive evaluation using a new milling dataset. The dataset represents gradual tool wear under changing workpieces and process parameters. Our AutoML method outperforms the state-of-the-art approach and the evaluation indicates that a transfer of methods designed for series production to variable process conditions is not easily possible. Overall, the new method optimizes individualized production economically and in terms of resources. Machining experts with limited ML knowledge can leverage their domain knowledge to develop, validate and adapt tool life models. Full article

A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Brain cells gradually die because of interruptions in blood supply and other nutrients to the brain, resulting in disabilities, depending on the affected region. Early recognition and detection of symptoms can aid in the rapid treatment of strokes and result in better health by reducing the severity of a stroke episode. In this paper, the Random Forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LightGBM) were used as machine learning (ML) algorithms for predicting the likelihood of a cerebral stroke by applying an open-access stroke prediction dataset. The stroke prediction dataset was pre-processed by handling missing values using the KNN imputer technique, eliminating outliers, applying the one-hot encoding method, and normalizing the features with different ranges of values. After data splitting, synthetic minority oversampling (SMO) was applied to balance the stroke samples and no-stroke classes. Furthermore, to fine-tune the hyper-parameters of the ML algorithm, we employed a random search technique that could achieve the best parameter values. After applying the tuning process, we stacked the parameters to a tuning ensemble RXLM that was analyzed and compared with traditional classifiers. The performance metrics after tuning the hyper-parameters achieved promising results with all ML algorithms. Full article

In order to solve the problems of excessive elastic deformation and excessive inertia force existed in the drive mechanism of traditional die-cutting machine, a lightweight drive rod with full symmetrical structure is proposed as the main force bearing component of the drive mechanism based on the kinematics analysis. The elastic deformation and inertia force of the lightweight drive rod are verified by static simulation analysis, and show that the weight of the drive rod is significantly reduced under the same deformation conditions, the traditional one. Further compared with, taking the minimum elastic deformation and lightweight as the optimization objectives, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to optimize the structural parameters of the drive rod. The results show that under the working conditions of 350 T die-cutting force and 125 r/min rotating speed, the elastic deformation of lightweight drive rod after structural optimization is smaller (the maximum deformation is 0.00988 mm) and the weight is lighter (27% less). The research data presented in this paper can be used as the theoretical basis for future research on die-cutting mechanism. The lightweight drive rod proposed in this study can be used in die-cutting devices with high die-cutting speed and high die-cutting accuracy. Full article

Since the inception of electric discharge machining (EDM), it has facilitated the production industries, for instance, die & mold, automotive, aerospace, etc., by providing an effective solution for machining hard-to-cut materials and intricate geometries. However, achieving high machining rates and a fine surface finish is an inherent issue with the traditional EDM process. The emergence of the powder mixed electric discharge machining (PMEDM) process has not only provided the opportunity for enhancing productivity and surface finish but also opened a window for its potential application in surface modification/coating of biomaterials. The process incorporates simultaneous machining and coating of bioimplants, i.e., lacking in the already available chemical and physical coating methods while requiring costly post-treatment procedures. This study comprehends the influence of powder characteristics and EDM process parameters on the performance parameters. The impact of tool electrodes and additive powders on the machined and coated surface of commonly used biomaterials. Furthermore, the study depicts the most frequently used methods for optimizing the PMEDM process, future research directions, challenges, and research trends over the past decade. Full article

“Alzheimer’s disease” (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. “Dementia” is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person’s ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. “Deep learning” (DL) is a type of “machine learning” (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered “neural network” architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field. Full article

The electrical discharge machining (EDM) process is one of the most efficient non-conventional precise material removal processes. It is a smart process used to intricately shape hard metals by creating spark erosion in electroconductive materials. Sparking occurs in the gap between the tool and workpiece. This erosion removes the material from the workpiece by melting and vaporizing the metal in the presence of dielectric fluid. In recent years, EDM has evolved widely on the basis of its electrical and non-electrical parameters. Recent research has sought to investigate the optimal machining parameters for EDM in order to make intricate shapes with greater accuracy and better finishes. Every method employed in the EDM process has intended to enhance the capability of machining performance by adopting better working conditions and developing techniques to machine new materials with more refinement. This new research aims to optimize EDM’s electrical parameters on the basis of multi-shaped electrodes in order to obtain a good surface finish and high dimensional accuracy and to improve the post-machining hardness in order to improve the overall quality of the machined profile. The optimization of electrical parameters, i.e., spark voltage, current, pulse-on time and depth of cut, has been achieved by conducting the experimentation on die steel D2 with a specifically designed multi-shaped copper electrode. An experimental design is generated using a statistical tool, and actual machining is performed to observe the surface roughness, variations on the surface hardness and dimensional stability. A full factorial design of experiment (DOE) approach has been followed (as there are more than two process parameters) to prepare the samples via EDM. Regression analysis and analysis of variance (ANOVA) for the interpretation and optimization of results has been carried out using Minitab as a statistical tool. The validation of experimental findings with statistical ones confirms the significance of each operating parameter on the output parameters. Hence, the most optimized relationships were found and presented in the current study. Full article