EEGDepressionNet: A Novel Self Attention-Based Gated DenseNet With Hybrid Heuristic Adopted Mental Depression Detection Model Using EEG Signals.

World Health Organization (WHO) has identified depression as a significant contributor to global disability, creating a complex thread in both public and private health. Electroencephalogram (EEG) can accurately reveal the working condition of the human brain, and it is considered an effective tool for analyzing depression. However, manual depression detection using EEG signals is time-consuming and tedious. To address this, fully automatic depression identification models have been designed using EEG signals to assist clinicians. In this study, we propose a novel automated deep learning-based depression detection system using EEG signals. The required EEG signals are gathered from publicly available databases, and three sets of features are extracted from the original EEG signal. Firstly, spectrogram images are generated from the original EEG signal, and 3-dimensional Convolutional Neural Networks (3D-CNN) are employed to extract deep features. Secondly, 1D-CNN is utilized to extract deep features from the collected EEG signal. Thirdly, spectral features are extracted from the collected EEG signal. Following feature extraction, optimal weights are fused with the three sets of features. The selection of optimal features is carried out using the developed Chaotic Owl Invasive Weed Search Optimization (COIWSO) algorithm. Subsequently, the fused features undergo analysis using the Self-Attention-based Gated Densenet (SA-GDensenet) for depression detection. The parameters within the detection network are optimized with the assistance of the same COIWSO. Finally, implementation results are analyzed in comparison to existing detection models. The experimentation findings of the developed model show 96% of accuracy. Throughout the empirical result, the findings of the developed model show better performance than traditional approaches.

Assessment of consolidative multi-criteria decision making (C-MCDM) algorithms for optimal mapping of polymer materials in additive manufacturing: A case study of orthotic application.

Objective: The objectives of this research are twofold. The primary goal is to introduce, investigate, and contrast consolidative multi-criteria decision-making (C-MCDM) approaches. The second objective is the investigation of five alternative additive manufacturing materials. Methods: It integrates the subjective and objective weights using the Bayes hypothesis in conjunction with a normal method. Chang's Extent Analysis Method under fuzzy logic is used to estimate subjective weights and the CRITIC approach is used for assessing objective weights. Ranking techniques, including the simple ranking process (SRP), multi-objective optimization based on ratio analysis (MOORA), measurement alternatives and ranking according to compromise solution (MARCOS), and technique for order preference by similarity to ideal solution (TOPSIS) are applied. It also encompasses sensitivity analysis based on Kendall's coefficient of concordance and rank reversal phenomenon analysis. Spearman's rank correlation coefficient, a weighted rank measure of correlation, and rank similarity coefficient are among the metrics used to evaluate agreement between different approaches. It entails gathering expert opinions regarding the importance of various criteria as well as conducting extensive experiments. Results: The findings of the study indicate that polylactic acid is the best material to use for orthoses. When compared to the other MCDM approaches being discussed, SRP is the most reliable approach. It is also demonstrated that the SRP, MARCOS, and TOPSIS methods are rank reversal-free. Furthermore, SRP exhibits a very poor association with the TOPSIS technique but a strong correlation with the MOORA and MARCOS approaches. Conclusions: To ensure results reliability, it is necessary to consider both the subjectivity and objectivity of weights as well as apply multiple MCDM methodologies in addition to sensitivity analysis.

Predicting Mechanical Properties of Polymer Materials Using Rate-Dependent Material Models: Finite Element Analysis of Bespoke Upper Limb Orthoses.

Three-dimensional printing-especially with fused deposition modeling (FDM)-is widely used in the medical field as it enables customization. FDM is versatile owing to the availability of various materials, but selecting the appropriate material for a certain application can be challenging. Understanding materials' mechanical behaviors, particularly those of polymeric materials, is vital to determining their suitability for a given application. Physical testing with universal testing machines is the most used method for determining the mechanical behaviors of polymers. This method is resource-intensive and requires cylinders for compression testing and unique dumbbell-shaped specimens for tensile testing. Thus, a specialized fixture must be designed to conduct mechanical testing for the customized orthosis, which is costly and time-consuming. Finite element (FE) analysis using an appropriate material model must be performed to identify the mechanical behaviors of a customized shape (e.g., an orthosis). This study analyzed three material models, namely the Bergström-Boyce (BB), three-network (TN), and three-network viscoplastic (TNV) models, to determine the mechanical behaviors of polymer materials for personalized upper limb orthoses and examined three polymer materials: PLA, ABS, and PETG. The models were first calibrated for each material using experimental data. Once the models were calibrated and found to fit the data appropriately, they were employed to examine the customized orthosis's mechanical behaviors through FE analysis. This approach is innovative in that it predicts the mechanical characteristics of a personalized orthosis by combining theoretical and experimental investigations.

An Insight into the Characteristics of 3D Printed Polymer Materials for Orthoses Applications: Experimental Study.

Knee orthoses assist patients with impaired gait through the amendment of knee abnormalities, restoration of mobility, alleviation of pain, shielding, and immobilization. The inevitable issues with laborious traditional plaster molding procedures for orthoses can be resolved with 3D printing. However, a number of challenges have limited the adoption of 3D printing, the most significant of which is the proper material selection for orthoses. This is so because the material used to make an orthosis affects its strength, adaptability, longevity, weight, moisture response, etc. This study intends to examine the mechanical, physical, and dimensional characteristics of three-dimensional (3D) printing materials (PLA, ABS, PETG, TPU, and PP). The aim of this investigation is to gain knowledge about these materials' potential for usage as knee orthosis materials. Tensile testing, Olympus microscope imaging, water absorption studies, and coordinate measuring machine-based dimension analysis are used to characterize the various 3D printing materials. Based on the investigation, PLA outperforms all other materials in terms of yield strength (25.98 MPa), tensile strength (30.89 MPa), and shrinkage (0.46%). PP is the least water absorbent (0.15%) and most flexible (407.99%); however, it is the most difficult to fabricate using 3D printing. When producing knee orthoses with 3D printing, PLA can be used for the orthosis frame and other structural elements, PLA or ABS for moving parts like hinges, PP for padding, and TPU or PP for the straps. This study provides useful information for scientists and medical professionals who are intrigued about various polymer materials for 3D printing and their effective utilization to fabricate knee orthoses.

Polyether-Ether-Ketone (PEEK) and Its 3D-Printed Quantitate Assessment in Cranial Reconstruction.

Three-dimensional (3D) printing, medical imaging, and implant design have all advanced significantly in recent years, and these developments may change how modern craniomaxillofacial surgeons use patient data to create tailored treatments. Polyether-ether-ketone (PEEK) is often seen as an attractive option over metal biomaterials in medical uses, but a solid PEEK implant often leads to poor osseointegration and clinical failure. Therefore, the objective of this study is to demonstrate the quantitative assessment of a custom porous PEEK implant for cranial reconstruction and to evaluate its fitting accuracy. The research proposes an efficient process for designing, fabricating, simulating, and inspecting a customized porous PEEK implant. In this study, a CT scan is utilized in conjunction with a mirrored reconstruction technique to produce a skull implant. In order to foster cell proliferation, the implant is modified into a porous structure. The implant's strength and stability are examined using finite element analysis. Fused filament fabrication (FFF) is utilized to fabricate the porous PEEK implants, and 3D scanning is used to test its fitting accuracy. The results of the biomechanical analysis indicate that the highest stress observed was approximately 61.92 MPa, which is comparatively low when compared with the yield strength and tensile strength of the material. The implant fitting analysis demonstrates that the implant's variance from the normal skull is less than 0.4436 mm, which is rather low given the delicate anatomy of the area. The results of the study demonstrate the implant's endurance while also increasing the patient's cosmetic value.

Finite Element Analysis of Upper Limb Splint Designs and Materials for 3D Printing.

Three-dimensional (3D) printed splints must be lightweight and adequately ventilated to maximize the patient's convenience while maintaining requisite strength. The ensuing loss of strength has a substantial impact on the transformation of a solid splint model into a perforated or porous model. Thus, two methods for making perforations-standard approach and topological optimization-are investigated in this study. The objective of this research is to ascertain the impact of different perforation shapes and their distribution as well as topology optimization on the customized splint model. The solid splint models made of various materials have been transformed into porous designs to evaluate their strength by utilizing Finite Element (FE) simulation. This study will have a substantial effect on the designing concept for medical devices as well as other industries such as automobiles and aerospace. The novelty of the research refers to creating the perforations as well as applying topology optimization and 3D printing in practice. According to the comparison of the various materials, PLA had the least amount of deformation and the highest safety factor for all loading directions. Additionally, it was shown that all perforation shapes behave similarly, implying that the perforation shape's effect is not notably pronounced. However, square perforations seemed to perform the best out of all the perforation shape types. It was also obvious that the topology-optimized hand splint outperformed that with square perforations. The topology-optimized hand splint weighs 26% less than the solid splint, whereas the square-perforated hand splint weighs roughly 12% less. Nevertheless, the user must choose which strategy (standard perforations or topology optimization) to employ based on the available tools and prerequisites.

Design, Fabrication, and Optimization of a Printed Ag Nanoparticle-Based Flexible Capacitive Sensor for Automotive IVI Bezel Display Applications.

Since printed capacitive sensors provide better sensing performance, they can be used in automotive bezel applications. It is necessary to fabricate such sensors and apply an optimization approach for choosing the optimal sensor pattern. In the present work, an effort was made to formulate interdigitated pattern-printed Silver (Ag) electrode flexible sensors and adopt the Taguchi Grey Relational (TGR)-based optimization approach to enhance the flexible sensor's panel for enhanced automobile infotainment applications. The optimization technique was performed to derive better design considerations and analyze the influence of the sensor's parameters on change in capacitance when touched and production cost. The fabricated flexible printed sensors can provide better sensing properties. A design pattern which integrates an overlap of 15 mm, an electrode line width of 0.8 mm, and an electrode gap 0.8 mm can produce a higher change in capacitance and achieve a lower weight. The overlap has a greater influence on sensor performance owing to its optimization of spatial interpolation.

Performance Evaluation of Input Power of Diode Laser on Machined Leather Specimen in Laser Beam Cutting Process.

Numerous industries, including footwear, handicrafts, and the automobile industry, utilize leather materials. The main goal of this study was to investigate the effect of input power of the diode laser in laser cutting on vegetable chrome tanned buffalo leather to enhance the cutting process. In the present investigation, carbonization, kerf width, and material removal rate (MRR) were taken as performance measures. The diode-based laser beam machining was designed and fabricated with 2.5 W, 5.5 W, and 20 W diode laser to cut vegetable chrome tanned leather. The high-intensity 20 W diode laser produced lower carbonization, lower kerf width, and higher material removal rate compared with the 2.5 W and 5.5 W diodes. This improved performance was due to the adjustable features associated with this diode laser actuation in the form of circular shape with adjustable diameter. A high power with a lower spot size under pulsed mode can produce higher power density. Since a higher power density can establish less interaction time, it produces lower carbonization. Due to the ability of the 20 W diode laser driver to control the beam shape and size, it could produce a lower kerf width and higher MRR. The optimal parameters for cutting chrome vegetable tanned cow leather were a standoff distance of 18 mm, feed rate of 200 mm/min, and duty cycle of 70%.

Design, Analysis, and 3D Printing of a Patient-Specific Polyetheretherketone Implant for the Reconstruction of Zygomatic Deformities.

The reconstruction of craniomaxillofacial deformities, especially zygomatic bone repair, can be exigent due to the complex anatomical structure and the sensitivity of the crucial organs involved. The need to reconstruct the zygomatic bone in the most precise way is of crucial importance for enhancing the patient outcomes and health care-related quality of life (HRQL). Autogenous bone grafts, despite being the gold standard, do not match bone forms, have limited donor sites and bone volume, and can induce substantial surgical site morbidity, which may lead to adverse outcomes. The goal of this study is to provide an integrated approach that includes various processes, from patient scanning to implant manufacture, for the restoration of zygomatic bone abnormalities utilizing Polyetheretherketone (PEEK) material, while retaining adequate aesthetic and facial symmetry. This study takes an integrated approach, including computer-aided implant design using the mirror reconstruction technique, investigating the biomechanical behavior of the implant under loading conditions, and carrying out a fitting accuracy analysis of the PEEK implant fabricated using state-of-the-art additive manufacturing technology. The findings of the biomechanical analysis results reveal the largest stress of approximately 0.89 MPa, which is relatively low in contrast to the material's yield strength and tensile strength. A high degree of sturdiness in the implant design is provided by the maximum value of strain and deformation, which is also relatively low at roughly 2.2 × 10-4 and 14 µm. This emphasizes the implant's capability for load resistance and safety under heavy loading. The 3D-printed PEEK implant observed a maximum deviation of 0.4810 mm in the outside direction, suggesting that the aesthetic result or the fitting precision is adequate. The 3D-printed PEEK implant has the potential to supplant the zygoma bone in cases of severe zygomatic reconstructive deformities, while improving the fit, stability, and strength of the implant.

Self-Propelled Rotary Tools in Hard Turning: Analysis and Optimization via Finite Element Models.

This study investigates self-propelled rotary tool (SPRT) performance in hard turning using 3D finite element (FE) models. The FE models developed in this study are based on coupled temperature-displacement analysis using an explicit time-integration scheme. The developed FE models can predict chip morphology, cutting forces, tool and workpiece stresses and temperatures. For model verification, hard turning experiments were conducted using an SPRT on AISI 4340 bars. Cutting forces and maximum tool-chip interface temperatures were recorded and compared with the model findings. The effects of different process parameters were analyzed and discussed using the developed FE models. The FE models were run with a central composite design (CCD-25) matrix with four input variables, i.e., the cutting speed, the feed rate, the depth of the cut and the inclination angle. Response surfaces based on the Gaussian process were generated for each performance variable in order to predict design points not available in the original design of the experiment matrix. An optimization study was carried out to minimize tool stress and temperature while setting limits for the material removal rate (MRR) and specific cutting energy for the process. Optimized processes were found with moderate cutting speeds and feed rates and high depths of cut and inclination angles.

A Multi-Part Orientation Planning Schema for Fabrication of Non-Related Components Using Additive Manufacturing.

Additive manufacturing (AM) is a technique that progressively deposits material in layer-by-layer manner (or in additive fashion) for producing a three-dimensional (3D) object, starting from the computer-aided design (CAD) model. This approach allows for the printing of complicated shaped objects and is quickly gaining traction in the aerospace, medical implant, jewelry, footwear, automotive, and fashion industries. AM, which was formerly used for single part customization, is currently being considered for mass customization of parts because of its positive impacts. However, part quality and build time are two main impediments to the deployment of AM for mass production. The optimal part orientation is fundamental for maximizing the part's quality as well as being critical for reducing the fabrication time. This research provides a new method for multi-part AM production that improves quality while reducing overall build time. The automatic setup planning or orientation approach described in this paper employs two objective functions: the quality of the build component and the build time. To tackle the given problem, it introduces a three-step genetic algorithm (GA)-based solution. A feature-based technique is utilized to generate a collection of finite alternative orientations for each component within a specific part group to ensure each part's individual build quality. Then, a GA was utilized to find the best combination of part build orientations at a global optimal level to reduce material consumption and build time. A case study of orienting nine components concurrently inside a given building chamber was provided for illustration. The findings suggest that the developed technique can increase quality, reduce support waste, and shorten overall production time. When components are positioned optimally rather than in random orientations, build time and support volume are reduced by approximately 7% and 16%, respectively.

Multiobjective Optimization of Heat-Treated Copper Tool Electrode on EMM Process Using Artificial Bee Colony (ABC) Algorithm.

Electrochemical micromachining (EMM) is a plausible method for manufacturing high accuracy and precision microscale components in a broad range of materials. EMM is commonly utilized to manufacture turbine blades for automobiles and aircrafts. In this present study, the EMM process was performed with a heat-treated copper tool electrode on aluminum 8011 alloy. The process parameters such as voltage, concentration of electrolyte, frequency, and duty factor were varied to analyze the effect of a heat-treated electrode on material removal rate (MRR), overcut, conicity, and circularity. It was observed that high MRR was obtained with lower overcut with an annealed electrode. The better conicity and circularity were obtained with a quenched electrode compared to other heat-treated and untreated tool electrodes. The artificial bee's colony (ABC) algorithm was used to identify the optimum parameters and, finally, the confirmation test was carried out to evaluate the error difference on the machining process. The optimum combination of input process parameters found using TOPSIS and ABC algorithm for the EMM process are voltage (14 V), electrolyte concentration (30 g/L), frequency (60 Hz), and duty cycle (33%) for the annealed tool electrode and voltage (14 V), electrolyte concentration (20 g/L), frequency (70 Hz), and duty cycle (33%) for the quenched tool electrode. It was confirmed that 95% of accurate response values were proven under the optimum parameter combination.

Tool Wear Prediction When Machining with Self-Propelled Rotary Tools.

The performance of a self-propelled rotary carbide tool when cutting hardened steel is evaluated in this study. Although various models for evaluating tool wear in traditional (fixed) tools have been introduced and deployed, there have been no efforts in the existing literature to predict the progression of tool wear while employing self-propelled rotary tools. The work-tool geometric relationship and the empirical function are used to build a flank wear model for self-propelled rotary cutting tools. Cutting experiments are conducted on AISI 4340 steel, which has a hardness of 54-56 HRC, at various cutting speeds and feeds. The rate of tool wear is measured at various intervals of time. The constant in the proposed model is obtained using genetic programming. When experimental and predicted flank wear are examined, the established model is found to be competent in estimating the rate of rotary tool flank wear progression.

Adaptive Mechanism for Designing a Personalized Cranial Implant and Its 3D Printing Using PEEK.

The rehabilitation of the skull's bones is a difficult process that poses a challenge to the surgical team. Due to the range of design methods and the availability of materials, the main concerns are the implant design and material selection. Mirror-image reconstruction is one of the widely used implant reconstruction techniques, but it is not a feasible option in asymmetrical regions. The ideal design approach and material should result in an implant outcome that is compact, easy to fit, resilient, and provides the perfect aesthetic and functional outcomes irrespective of the location. The design technique for the making of the personalized implant must be easy to use and independent of the defect's position on the skull. As a result, this article proposes a hybrid system that incorporates computer tomography acquisition, an adaptive design (or modeling) scheme, computational analysis, and accuracy assessment. The newly developed hybrid approach aims to obtain ideal cranial implants that are unique to each patient and defect. Polyetheretherketone (PEEK) is chosen to fabricate the implant because it is a viable alternative to titanium implants for personalized implants, and because it is simpler to use, lighter, and sturdy enough to shield the brain. The aesthetic result or the fitting accuracy is adequate, with a maximum deviation of 0.59 mm in the outside direction. The results of the biomechanical analysis demonstrate that the maximum Von Mises stress (8.15 MPa), Von Mises strain (0.002), and deformation (0.18 mm) are all extremely low, and the factor of safety is reasonably high, highlighting the implant's load resistance potential and safety under high loading. Moreover, the time it takes to develop an implant model for any cranial defect using the proposed modeling scheme is very fast, at around one hour. This study illustrates that the utilized 3D reconstruction method and PEEK material would minimize time-consuming alterations while also improving the implant's fit, stability, and strength.

Influence of Adaptive Gap Control Mechanism and Tool Electrodes on Machining Titanium (Ti-6Al-4V) Alloy in EDM Process.

Titanium alloy is widely used for orthodontic technology and easily machined using the EDM process. In the EDM process, the workpiece and tool electrode must be separated by a continuous air gap during the machining operation to generate discharge energy in this method. In the present study, an endeavor was made to analyze the effects of a servo feed air gap control and tool electrode in the EDM process. The developed mechanical setup consists of a linear action movement with zero backlash along the X-axis, which can be controlled up to 0.03 mm. It was observed that the suggested air gap control scheme can enhance the servo feed mechanism on a machining titanium alloy. A tungsten carbide electrode can enhance the surface measures owing to its ability to produce tiny craters with uniform distribution. Since it produces a little crater and has a higher melting point, a tungsten carbide electrode can create lesser surface roughness than a copper tool and brass tool electrode.

Friction Stir Welding of Thick Plates of 4Y3Gd Mg Alloy: An Investigation of Microstructure and Mechanical Properties.

Applications of non-ferrous light metal alloys are especially popular in the field of aerospace. Hence it is important to investigate their properties in joining processes such as welding. Solid state joining process such as friction stir welding (FSW) is quite efficient for joining non-ferrous alloys, but with thick plates, challenges increase. In this study, Mg alloy plates of thickness 11.5 mm were successfully welded via single-pass FSW. Due to the dynamic recrystallization, grain size in the stir zone was reduced to 16 µm which is ≈15 times smaller than the parent material. The optimized rotational speed and traverse speed for optimum weld integrity were found to be 710 rpm and 100 mm/min, respectively. A sound weld with 98.96% joint efficiency, having an Ultimate Tensile Strength (UTS) of 161.8 MPa and elongation of 27.83%, was accomplished. Microhardness of the nugget was increased by 14.3%.

Multi-Response Optimization of Processing Parameters for Micro-Pockets on Alumina Bioceramic Using Rotary Ultrasonic Machining.

The machining of ceramic materials is challenging and often impossible to realize with conventional machining tools. In various manufacturing applications, rotary ultrasonic milling (RUM) shows strengths, in particular for the development of high-quality micro-features in ceramic materials. The main variables that influence the performance and price of the product are surface roughness, edge chipping (EC), and material removal rate (MRR) during the processing of ceramics. RUM has been considered in this research for the milling of micro-pockets in bioceramic alumina (Al2O3). Response surface methodology in the context of a central composite design (CCD) is being used to plan the experiments. The impacts of important RUM input parameters concerning cutting speed, feed rate, depth of cut, frequency, and amplitude have been explored on the surface roughness in terms of arithmetic mean value (Ra), the EC, and the MRR of the machined pockets. The main effect and the interaction effect of the implemented RUM parameters show that by providing a lower feed rate and cutting depth levels and elevated frequency and cutting speed, the Ra and the EC can be minimized. At greater levels of feed rate and cutting depth, higher MRR can be obtained. The influence of RUM input parameters on the surface morphology was also recorded and analyzed using scanning electron microscopic (SEM) images. The study of the energy dispersive spectroscopy (EDS) shows that there is no modification in the alumina bioceramic material. Additionally, a multi-response optimization method has been applied by employing a desirability approach with the core objectives of minimizing the EC and Ra and maximizing the MRR of the milled pockets. The obtained experimental values for Ra, EC, and MRR at an optimized parametric setting were 0.301 µm, 12.45 µm, and 0.873 mm3/min respectively with a combined desirability index value of 0.73.

Evaluation of Self-Propelled Rotary Tool in the Machining of Hardened Steel Using Finite Element Models.

This paper presents a model for assessing the performance of self-propelled rotary tool during the processing of hardened steel. A finite element (FE) model has been proposed in this analysis to study the hard turning of AISI 51200 hardened steel using a self-propelled rotary cutting tool. The model is developed by utilizing the explicit coupled temperature displacement analysis in the presence of realistic boundary conditions. This model does not take into account any assumptions regarding the heat partitioning and the tool-workpiece contact area. The model can predict the cutting forces, chip flow, induced stresses, and the generated temperature on the cutting tool and the workpiece. The nodal temperatures and heat flux data from the chip formation analysis are used to achieve steady-state temperatures on the cutting tool in the heat transfer analysis. The model outcomes are compared with reported experimental data and a good agreement has been found.

Achieving the Minimum Roughness of Laser Milled Micro-Impressions on Ti 6Al 4V, Inconel 718, and Duralumin.

Titanium-aluminium-vanadium (Ti 6Al 4V) alloys, nickel alloys (Inconel 718), and duraluminum alloys (AA 2000 series) are widely used materials in numerous engineering applications wherein machined features are required to having good surface finish. In this research, micro-impressions of 12 µm depth are milled on these materials though laser milling. Response surface methodology based design of experiment is followed resulting in 54 experiments per work material. Five laser parameters are considered naming lamp current intensity (I), pulse frequency (f), scanning speed (V), layer thickness (LT), and track displacement (TD). Process performance is evaluated and compared in terms of surface roughness through several statistical and microscopic analysis. The significance, strength, and direction of each of the five laser parametric effects are deeply investigated for the said alloys. Optimized laser parameters are proposed to achieve minimum surface roughness. For the optimized combination of laser parameters to achieve minimum surface roughness (Ra) in the titanium alloy, the said alloy consists of I = 85%, f = 20 kHz, V = 250 mm/s, TD = 11 µm, and LT = 3 µm. Similarly, optimized parameters for nickel alloy are as follows: I = 85%, f = 20 kHz, V = 256 mm/s, TD = 8 µm, and LT = 1 µm. Minimum roughness (Ra) on the surface of aluminum alloys can be achieved under the following optimized parameters: I = 75%, f = 20 kHz, V = 200 mm/s, TD = 12 µm, and LT = 3 µm. Micro-impressions produced under optimized parameters have surface roughness of 0.56 µm, 2.46 µm, and 0.54 µm on titanium alloy, nickel alloy, and duralumin, respectively. Some engineering applications need to have high surface roughness (e.g., in case of biomedical implants) or some desired level of roughness. Therefore, validated statistical models are presented to estimate the desired level of roughness against any laser parametric settings.

Micromachining of Biolox Forte Ceramic Utilizing Combined Laser/Ultrasonic Processes.

Micromachining has gained considerable interest across a wide range of applications. It ensures the production of microfeatures such as microchannels, micropockets, etc. Typically, the manufacturing of microchannels in bioceramics is a demanding task. The ubiquitous technologies, laser beam machining (LBM) and rotary ultrasonic machining (RUM), have tremendous potential. However, again, these machining methods do have inherent problems. LBM has issues concerning thermal damage, high surface roughness, and vulnerable dimensional accuracy. Likewise, RUM is associated with high machining costs and low material-removal rates. To overcome their limits, a synthesis of LBM and RUM processes known as laser rotary ultrasonic machining (LRUM) has been conceived. The bioceramic known as biolox forte was utilized in this investigation. The approach encompasses the exploratory study of the effects of fundamental input process parameters of LBM and RUM on the surface quality, machining time, and dimensional accuracy of the manufactured microchannels. The performance of LRUM was analyzed and the mechanism of LRUM tool wear was also investigated. The results revealed that the surface roughness, depth error, and width error is decreased by 88%, 70%, and 80% respectively in the LRUM process. Moreover, the machining time of LRUM is reduced by 85%.