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Industrial pressure relief valves must function reliably and effectively to protect pressurized systems from excessive pressure conditions. These valves are essential safety devices that act as cushions to protect piping systems, equipment, and vessels from the risks of high pressure, which can cause damage or even explosions. The objectives of this study were to minimize valve failures, decrease the number of rejected valves in the production line, and enhance the overall quality of pressure relief valves. This work introduces an integrated quality improvement methodology known as the hybrid multi-criteria decision-making (MCDM)—failure mode and effects analysis (FMEA) approach. This approach is based on prioritizing crucial factors for any failure modes in the industrial setting. The presented case study demonstrates the application of a hybrid approach for identifying the fundamental causes of industrial pressure relief valve failure modes and malfunctions. This investigation highlights the applicability of FMEA as a methodology for determining causes and executing remedial actions to keep failures from happening again. FMEA helps uncover the underlying causes of industrial pressure relief valve failures, while the integration of the hybrid MCDM methodology enables the application of four integrated MCDM methods to identify crucial factors. The adopted model addresses the shortcomings of the conventional FMEA by accurately analyzing the relationships between the risk factors and by utilizing several MCDM methods to rank failure modes. Following the application of the adopted methodology, it was discovered that the high-risk failure modes for the pressure relief valve included misalignment of wire, normal wear/aging, rejection of machined parts, mismatch of mating parts, and corrosion. Therefore, risk managers should prioritize developing improvement strategies for these five failure modes. Similarly, failures comprising debris, delayed valve opening, internal leakage, premature valve opening, and burr foreign particles were determined as second essential groups for improvement.

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.

Coordinate measuring machines (CMMs) are utilized to acquire coordinate data from manufactured surfaces for inspection reasons. These data are employed to gauge the geometric form errors associated with the surface. An optimization procedure of fitting a substitute surface to the measured points is applied to assess the form error. Since the traditional least-squares approach is susceptible to overestimation, it leads to unreasonable rejections. This paper implements a modified differential evolution (DE) algorithm to estimate the minimum zone femoral head sphericity. In this algorithm, opposition-based learning is considered for population initialization, and an adaptive scheme is enacted for scaling factor and crossover probability. The coefficients of the correlation factor and the uncertainty propagation are also measured so that the result’s uncertainty can be determined. Undoubtedly, the credibility and plausibility of inspection outcomes are strengthened by evaluating measurement uncertainty. Several data sets are used to corroborate the outcome of the DE algorithm. CMM validation shows that the modified DE algorithm can measure sphericity with high precision and consistency. This algorithm allows for an adequate initial solution and adaptability to address a wide range of industrial problems. It ensures a proper balance between exploitation and exploration capabilities. Thus, the suggested methodology, based on the computational results, is feasible for the online deployment of the sphericity evaluation. The adopted DE strategy is simple to use, has few controlling variables, and is computationally less expensive. It guarantees a robust solution and can be used to compute different form errors.

Human life has become smarter by utilizing big data, telecommunication technologies, and wearable sensors over pervasive computing to give better healthcare services. Big data is built with the possibility to improve the healthcare industry. Big data makes the interconnection between patients, wearable sensors, healthcare caregivers, and providers through the utilization of Information and Communication Technology (ICT) and software. Most of the economic challenges in developing countries are caused by the healthcare sector, which occurs predominantly due to the increasing population requiring more quality of care concerning older people. Older people need great attention and care as they lead with irreparable damages when a minor accident or insignificant disease occurs. Therefore, the necessity of implementing new technologies and tools has arisen to support senior citizens regarding their healthcare. Various advancements in wireless technology, miniaturization, computing power, and processing made diverse healthcare innovations that led to developing the connected medical devices. Hence, this proposal develops a new healthcare monitoring system for tracking the activities of elderly people, where the Hadoop MapReduce technique for parallel processing the large-sized data. The data collected as mentioned in the available datasets is performed by using the numerous wearable sensors fixed on the “subject’s left ankle, right arm, and chest” that are transformed to the cloud platform and also to the data analytics layer according to the Internet of Medical Things (IoMT) devices. The given input undergoes data splitting to produce tiny chunks. These small chunks of the input files are then considered as Map tasks. Here, in the map phase, the features are optimally selected by the Hybrid Dingo Coyote Optimization (HDCO). The combiner phase classifies the physical activities using the developed Deep Ensemble Learning (DEL) consisting of classifiers such as “Extreme Learning Machine (ELM), deep Convolutional Neural Network (CNN), Long short-term memory (LSTM), Deep Belief Network (DBN), and Deep Neural Network (DNN)”. The parameter tuning in these classifiers is done by the same HDCO. The reducer phase extracts data from different chunks by merging the same classes. The developed HDCO-DEL has secured 13.66%, 16.01%, 17.33%, 13.6%, and 14.01% better accuracy than ELM, CNN, LSTM, DBN, DNN, and HealthFog, respectively on second dataset. The comparison with existing methods shows its better performance and also predicts physical activities with overall high accuracy.

Recently, hospital care and other services have become increasingly important for patient satisfaction. Better hospital care and assistance improve patients’ medical conditions, management trust, and financial success. In this regard, monitoring and measuring hospital service quality is necessary to improve patient satisfaction and wellness. However, the evaluation of healthcare service quality is a complex and critical task due to its intangible nature. Existing methodologies often struggle to effectively incorporate multiple criteria and address uncertainties inherent in healthcare evaluations. To address these challenges, this research work seeks to develop a comprehensive and robust approach for evaluating hospital service quality to improve decision making and resource allocation for service enhancement. This study aims to evaluate multi-faceted healthcare service quality by combining many criteria and uncertainties into a single index. The model is constructed methodically utilizing fuzzy logic and decision modeling. A dataset collected from diverse healthcare facilities covering various medical specialties and regions is employed to validate and refine the model. Numerous criteria, factors, and dimensions are examined and embedded into the development of the model. Fuzzy logic is used to capture and manage healthcare evaluations’ inherent vagueness and imprecision, yielding more accurate and comprehensive outcomes. The model’s outcome is the hospital service quality fuzzy index (HSQFI), an easy-to-understand single performance measure. A graphical user interface (GUI) is developed for collecting data, and then it shows the results in the form of barriers and recommendations. Based on the findings, recommendations in terms of barriers (service criteria) to enhance the hospital’s service quality have been made. This approach can be a tool for managers or other stakeholders to quickly realize the success of their service plans and pinpoint areas that may need improvement in the future.

Efficient and effective operation of an emergency department is necessary. Since patients can visit the emergency department without making an appointment, the emergency department always treats a lot of critical patients. Moreover, the severity of the ailment determines which patients should be prioritized. Therefore, the patients are greatly impacted as a consequence of longer waiting times caused primarily by incorrect resource allocation. It frequently happens that patients leave the hospital or waiting area without treatment. Certainly, the emergency department’s operation can be made more effective and efficient by examining its work and making modifications to the number of resources and their allocation. This study, therefore, investigates the emergency department of a public hospital to improve its functioning. The goal of this research is to model and simulate an emergency department to minimize patient wait times and also minimize the number of patients leaving the hospital without service. A comprehensive simulation model is developed using the Arena simulation platform and goal programming is undertaken to conduct simulation optimization and resource allocation analysis. Hospital management should realize that all resources must be prioritized rather than just focusing on one or two of them. The case scenario (S3) in this study that implements goal programming with variable weights yields the most favorable results. For example, it is observed in this instance that the number of patients leaving the system without service drops by 61.7%, and there is also a substantial drop in waiting times for various types of patients.

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.

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.

The selection of manufacturing processes for a given application is a complex problem of multicriteria decision-making although there have been several different approaches that can be utilized to select a suitable alternative. However, identifying appropriate multicriteria decision-making approach from the list of available methods for a given application is a difficult task. This work suggests a methodology to assess different selection approaches, which are the technique for order of preference by similarity to ideal solution (TOPSIS), analytic hierarchy process (AHP), and VIKOR: stepwise procedure. This valuation was done depending on the following factors: number of alternative processes and criteria, agility through the process of decision-making, computational complexity, adequacy in supporting a group decision, and addition or removal of a criterion. A case study in this study was presented to analyse the evaluation methodology. The criteria used to evaluate and identify the best manufacturing process were categorized into productivity, accuracy, complexity, flexibility, material utilization, quality, and operation cost. Five manufacturing processes were considered, including gravity die casting, investment casting, pressure die casting, sand casting, and additive manufacturing. The results showed that each approach was suitable for the problems of manufacturing process selection, in particular toward the support of group decision-making and uncertainty modelling. Manufacturing processes were ranked based on their respective weights for AHP, TOPSIS, and VIKOR, and sand casting is the best. In terms of computational complexity, the VIKOR method performed better than TOPSIS and AHP. Moreover, the VIKOR and TOPSIS methods were better convenient to the selection of manufacturing processes for agility during the process of decision-making, the number of alternative processes and criteria, adequacy in supporting a group decision, and addition or removal of a criterion.

The hybrid process, which integrates two or more different processes such as additive manufacturing and subtractive manufacturing, has gained appreciable considerations in recent years. This process exploits the benefits of individual processes while overcoming their limitations. Lately, the combination of additive, subtractive, and inspection methods is a valuable conglomeration, considering its potential to produce complicated components precisely. Certainly, computer-aided process plan (CAPP) provides a crucial link among different processes and is essential to avail the benefits of hybridization. However, a valuable process plan can only be achieved through the optimization of its different elements. Therefore, the objective of this work is the accomplishment of an optimized CAPP to fabricate parts in the shortest time employing the hybrid additive, subtractive, and inspection processes. In this work, mathematical models have been developed to optimize part orientation as well as minimize additive and subtractive times. Additionally, the genetic algorithm has been employed to obtain the best path with minimum inspection time. The feasibility and capability of the proposed approach as well as the optimized CAPP for the hybrid process have been demonstrated through a case study.

Implants are the most popular option for restoring the facial anatomy in the event of a mishap. The commercially available craniofacial implants are of standard shapes, which need to be tailored and shaped to accurately fit the patient’s anatomy. The manual shaping of the implant to match the bone contours is conducted during surgical operation, and is a cumbersome and inaccurate process. Recent breakthroughs in computer-aided design, analysis, and additive manufacturing (AM) have allowed the precise and rapid manufacture of bespoke scaffolds for difficult anatomical restoration. The goal of this research is to investigate the use of scaffolds for craniofacial reconstruction and their fabrication using electron-beam additive manufacturing (EBAM). Personalized cheekbone scaffolds are additively fabricated using Ti6Al4V and subjected to compression testing. Finally, the scaffold design with the highest compressive strength is subjected to biomechanical analysis. The biomechanical analysis results indicate that the maximum Von Mises stress (40 MPa) and equivalent strain (0.4 µm) are significantly low in magnitude, thus providing a desirable implant that is both flexible and stable. The custom-designed cheekbone scaffold manufactured with AM technology not only aids in bone-implant ingrowth but also helps in reducing implant weight and ensuring implant stability and long-term effectiveness.

Additive manufacturing is a recent trend in production processes owing to its many benefits. It can be defined as the process of producing parts through the deposition of material in a layer-by-layer fashion. It has been a topic of intense study and review by many researchers. In this work, a comprehensive review pertaining to additive manufacturing has been accomplished. The evolution of additive manufacturing as a prominent technology and its various phases are discussed. The importance of part orientation, build time estimation, and cost computation has also been reviewed. The remarkable aspect of this work is the identification of problems associated with different additive manufacturing methods. Because of the imperfections in additive manufacturing, its hybridization with other methods, such as subtractive manufacturing, has been emphasized. This review will help readers understand the different aspects of additive manufacturing and explore new avenues for future research.

Friction stir alloying (FSA) of commercially pure Cu with Ni, Zn, and Mg is implemented in the current study. Mechanical and microstructural aspects of the successfully fabricated alloy structure have been examined. Energy dispersive X-ray spectroscopy revealed a uniform distribution of alloying elements and coalescence at the atomic level. The compositional and grain size heterogeneity is managed in the stir zone, allowing for microstructural control with FSA. Thus, the present study is essential for the development of novel materials whose fabrication requires temperature well below the melting point of base metals. The alloying process is found to be accompanied by ultra-refined grains, with the smallest grain size being ~0.44 μm. The fabricated alloy managed to retain the FCC phase, and no brittle intermetallic compounds formed, according to X-ray diffraction. The fabricated alloy exhibits maximum and average microhardness enhancements of 18.4% and 6%, respectively. Tensile properties have also been investigated and correlated with microstructural morphology. A shift toward grain bimodality has also been documented, which is a highly sought-after property nowadays, especially to overcome the strength-ductility trade-off.

Additive manufacturing (AM), particularly electron beam melting (EBM), is becoming increasingly common in the medical industry because of its remarkable benefits. The application of personalized titanium alloy implants produced using EBM has received considerable attention in recent times due to their simplicity and efficacy. However, these tailored implants are not cost-effective, placing a tremendous strain on the patient. The use of additional materials as support during the manufacturing process is one of the key causes of its high cost. A lot of research has been done to lessen the use of supports through various types of support designs. There is indeed a noticeable paucity of studies in the literature that have examined customized implants produced without or minimal supports. This research, therefore, reports on the investigation of cranial implants fabricated with and without supports. The two personalized implants are evaluated in terms of their cost, fabrication time, and accuracy. The study showed impressive results for cranial implants manufactured without supports that cost 39% less than the implants with supports. Similarly, the implant’s (without supports) build time was 18% less than its equivalent with supports. The two implants also demonstrated similar fitting accuracy with 0.2613 mm error in the instance of implant built without supports and 0.2544 mm for the implant with supports. The results indicate that cranial implants can be produced without EBM supports, which can minimize both production time and cost substantially. However, the manufacture of other complex implants without supports needs further study. The future study also requires a detailed review of the mechanical and structural characteristics of cranial implants built without supports.

Titanium alloy is widely used in modern automobile industries due to its higher strength with corrosion resistance. Such higher strength materials can be effectively machined using unconventional machining processes, especially the electro-chemical micro machining (ECMM) process. It is important to enhance the machining process by investigating the effects of electrolytes and process parameters in ECMM. The presented work describes the influence of three different combinations of Sodium Chloride-based electrolytes on machining Titanium (Ti-6Al-4V) alloy. Based on the ECMM process parameters such as applied voltage, electrolytic concentration, frequency and duty cycle on response, characteristics are determined by the Taguchi design of experiments. The highest material removal rate (MRR) was achieved by the Sodium Chloride and Sodium Nitrate electrolyte. The combination of Sodium Chloride and Citric Acid achieve highest Overcut and Circularity. The optimal overcut was observed from the Sodium Chloride and Glycerol electrolyte due to the presence of glycerol. The better conicity was obtained from Sodium Chloride and Citric Acid in comparison with other electrolytes. A Sodium Chloride and Glycerol combination could generate better machined surface owing to the chelating effect of Glycerol.

Technological advancements in healthcare influence medical practitioners as much as they impact the routine lives of the patients. The mandible reconstruction, which constitutes an important branch in facioplasty, has been a challenging task for medical professionals. As part of scientific innovation, tailor-made implants are valuable for sustaining and regenerating facial anatomy, as well as preserving the natural appearance. The challenge of choosing an acceptable implant design is a tedious process due to the growing number of designs with conspicuous effectiveness. The design should be agreeable, easy-to-design, sustainable, cost-effective, and undemanding for manufacturing. The optimal implant design can efficiently and effectively recover the structure and morphology of the flawed region. Evidently, among the many variants, the choice of appropriate design is one of the prevalent implant design problems and is still under consideration in most studies. This work is focused on the multiattribute decision-making (MCDM) approach to choosing the most effective implant design. The prevalence of subjectivity in decision-making and the presence of inconsistency from multiple sources emphasize the strategies that must take ambiguity and vagueness into account. An integrated MCDM methodology, assimilating two modern and popular techniques is adopted in this work. The preferred approach implements the Fuzzy Analytical Hierarchy Process based on the trapezoidal fuzzy number to extract the criteria weights in decision mapping and the Technique for Order of Preference by Similarity to Ideal Solution and VIKOR to assess design choices. A two-stage mechanism is the cornerstone of the established methodology. The first stage analyses the criteria from the point of view of the designer, the context of fabrication, and consumer experience. The second stage identifies the most viable and feasible design. The procedure applied in this analysis can be considered to choose the optimal implant design and to decide on areas of improvement that ensure greater patient experience.

Mandibular reconstruction is a complicated task because of the complex nature of the regional anatomy. Computer-assisted tools are a promising means of improving the precision and safety of such complex surgeries. The digital techniques utilized in the reconstruction of mandibular defects based on medical data, computer-aided-design approaches, and three-dimensional (3D) printing are widely used to improve the patient’s aesthetic appearance and function, as well as the accuracy and quality of diagnosis, and surgical outcomes. Nevertheless, to ensure an acceptable aesthetical appearance and functional outcomes, the design must be based on proper anatomical reconstruction, mostly done in a virtual environment by skilled design engineers. Mirroring is one of the widely used techniques in the surgical navigation and reconstruction of mandibular defects. However, there are some discrepancies and mismatches in the mirrored anatomical models. Hence, in order to overcome these limitations in the mirroring technique, a novel approach called the cavity-filled technique was introduced. The objective of this study was to compare the accuracy of the newly recommended cavity-filled technique with the widely used mirror reconstruction technique in restoring mandibular defects. A prominent 3D comparison technique was employed in this work, where the resected and the reconstructed mandibles were superimposed to quantify the accuracy of the two techniques. From the analysis, it can be inferred that the cavity-filled technique with a root-mean-square value of 1.1019 mm produced better accuracy in contrast to the mirroring approach, which resulted in an error of 1.2683 mm. Consequently, by using the proposed cavity-filled design, the discrepancy between the reconstruction plate and the bone contour was mitigated. This method, owing to its high precision, can decrease the number of adjustments and the time of surgery, as well as ensure a quick recovery time with better implant tissue in-growth.

Reconstruction of zygomatic complex defects is a surgical challenge, owing to the accurate restoration of structural symmetry as well as facial projection. Generally, there are many available techniques for zygomatic reconstruction, but they hardly achieve aesthetic and functional properties. To our knowledge, there is no such study on zygomatic titanium bone reconstruction, which involves the complete steps from patient computed tomography scan to the fabrication of titanium zygomatic implant and evaluation of implant accuracy. The objective of this study is to propose an integrated system methodology for the reconstruction of complex zygomatic bony defects using titanium comprising several steps, right from the patient scan to implant fabrication while maintaining proper aesthetic and facial symmetry. The integrated system methodology involves computer-assisted implant design based on the patient computed tomography data, the implant fitting accuracy using three-dimensional comparison techniques, finite element analysis to investigate the biomechanical behavior under loading conditions, and finally titanium fabrication of the zygomatic implant using state-of-the-art electron beam melting technology. The resulting titanium implant has a superior aesthetic appearance and preferable biocompatibility. The customized mirrored implant accurately fit on the defective area and restored the tumor region with inconsequential inconsistency. Moreover, the outcome from the two-dimensional analysis provided a good accuracy within 2 mm as established through physical prototyping. Thus, the designed implant produced faultless fitting, favorable symmetry, and satisfying aesthetics. The simulation results also demonstrated the load resistant ability of the implant with max stress within 1.76 MPa. Certainly, the mirrored and electron beam melted titanium implant can be considered as the practical alternative for a bone substitute of complex zygomatic reconstruction.

The use of additive manufacturing in medical applications has become more prevalent over the last decade. Studies have proved that reconstruction plates with a mesh structure enhance the biocompatibility and bone-ingrowth formation. However, limited studies have been reported in the customization and in vivo clinical assessment of mesh implants. The purpose of this study was to investigate the surgical treatment and implant fitting accuracy using three different reconstruction plates. Fifteen goats were divided into one control and three experimental groups (Groups 1, 2, and 3) with five in each group. An experimental segmental defect was created on these animals and was adopted with customized electron beam melting reconstruction titanium plates with mesh in Group 1 and without mesh in Group 2 and commercial reconstruction plate in Group 3. All the animals were subjected to radiographic analysis before and after surgery. The subjected animals were sacrificed after 3 months and the electron beam melting reconstruction plates were compared with the commercial plate based on clinical and histology analysis and implant fitting accuracy. Both the electron beam melting reconstruction plates (with mesh and without mesh) and commercial plates survived the three months post-operation, revealing good wound-healing with new bone formation and without any foreign-body reaction. The electron beam melting reconstructed plate with mesh (Group 1) was found to have a better implant fitting when compared to the other two groups. The average discrepancy between Groups 2 and 3 was not significant. Certainly, the commercial plate (Group 3) was found to have the least accuracy as compared to other electron beam melting reconstruction plates (Group 1 and Group 2). Custom design electron beam melting fabricated reconstruction plates possessed better functionality, aesthetic outcome, and long-term biocompatibility when compared to commercial plates. Animal results indicated that the electron beam melting plates with mesh (Group 1) were superior in comparison to the other two groups due to its ability to provide better bone-in-growth and osseointegration on its porous microstructure.

A custom made implant is critical in cranioplasty to cushion and restore intracranial anatomy, as well as to recover the appearance and attain cognitive stability in the patient. The utilization of customized titanium alloy implants using three-dimensional (3D) reconstruction technique and fabricated using Electron Beam Melting (EBM) has gained significant recognition in recent years, owing to their convenience and effectiveness. Besides, the conventional technique or the extant practice of transforming the standard plates is unreliable, arduous and tedious. As a result, this work aims to produce a customized cranial implant using 3D reconstruction that is reliable in terms of fitting accuracy, appearance, mechanical strength, and consistent material composition. A well-defined methodology initiating from EBM fabrication to final validation has been outlined in this work. The custom design of the implant was carried out by mirror reconstruction of the skull’s defective region, acquired through computer tomography. The design of the customized implant was then analyzed for mechanical stresses by applying finite element analysis. Consequently, the 3D model of the implant was fabricated from Ti6Al4V ELI powder with a thickness of ≃1.76−2 mm. Different tests were employed to evaluate the bio-mechanical stability and strength of the fabricated customized implant design. A 3D comparison study was performed to ensure there was anatomical accuracy, as well as to maintain gratifying aesthetics. The bio-mechanical analysis results revealed that the maximum Von Mises stress (2.5 MPa), strain distribution (1.49 × 10−4) and deformation (3.26 × 10−6 mm) were significantly low in magnitude, thus proving the implant load resistance ability. The average yield and tensile strengths for the fabricated Ti6Al4V ELI EBM specimen were found to be 825 MPa and 880 MPa, respectively, which were well over the prescribed strength for Ti6Al4V ELI implant material. The hardness study also resulted in an acceptable outcome within the acceptable range of 30−35 HRC. Certainly, the chemical composition of the fabricated EBM specimen was intact as established in EDX analysis. The weight of the cranial implant (128 grams) was also in agreement with substituted defected bone portion, ruling out any stress shielding effect. With the proposed approach, the anatomy of the cranium deformities can be retrieved effectively and efficiently. The implementation of 3D reconstruction techniques can conveniently reduce tedious alterations in the implant design and subsequent errors. It can be a valuable and reliable approach to enhance implant fitting, stability, and strength.

Segmental mandibular reconstruction has been a challenge for medical practitioners, despite significant advances in medical technology. There is a recent trend in relation to customized implants, made up of porous structures. These lightweight prosthesis scaffolds present a new direction in the evolution of mandibular restoration. Indeed, the design and properties of porous implants for mandibular reconstruction should be able to recover the anatomy and contour of the missing region as well as restore the functions, including mastication, swallowing, etc. In this work, two different designs for customized prosthesis scaffold have been assessed for mandibular continuity. These designs have been evaluated for functional and aesthetic aspects along with effective osseointegration. The two designs classified as top and bottom porous plate and inner porous plate were designed and realized through the integration of imaging technology (computer tomography), processing software and additive manufacturing (Electron Beam Melting). In addition, the proposed designs for prosthesis scaffolds were analyzed for their biomechanical properties, structural integrity, fitting accuracy and heaviness. The simulation of biomechanical activity revealed that the scaffold with top and bottom porous plate design inherited lower Von Mises stress (214.77 MPa) as compared to scaffold design with inner porous plate design (360.22 MPa). Moreover, the top and bottom porous plate design resulted in a better fit with an average deviation of 0.8274 mm and its structure was more efficiently interconnected through the network of channels without any cracks or powder material. Verily, this study has demonstrated the feasibility and effectiveness of the customized porous titanium implants in mandibular reconstruction. Notice that the design and formation of the porous implant play a crucial role in restoring the desired mandibular performance.

The developments and improvements in human–computer interaction and information and communication technologies have resulted in innovative assistive technology (AT) solutions for the people with disabilities (PWDs). Although, there have been many new ideas and technologies for assisting the PWDs, still very few of them have considered the workplace environment. Therefore, there is a need to develop a comprehensive universal AT solution that can maximize the PWDs productivity at work. In this work, an AT and smart context-aware solution has been developed to assist the PWDs at the workplace. This has been achieved through the designing and the implementation of a smart unified interface to guide and assist the PWDs. A wireless sensor network has been employed in this context-aware smart solution in order to track and determine user’s location. The unified interface is adaptive and includes four modules that are smart helping, smart communication, smart editor, and auto emergency response. A proper AT process and technological design considerations have been adopted to achieve a comprehensive, universal, adaptive, and context-aware solution.

This study aims to investigate the manufacturability of overhang round holes with and without support. The experiments were conducted by electron beam melting (EBM) using a Ti6Al4V powder. A large number of overhanging holes with and without support were fabricated and evaluated. The geometrical accuracy, mechanical properties, and microstructures were utilized as a measure of the process performance. It was demonstrated that overhanging features can be built successfully without support up to a certain dimension (or threshold value). Beyond that value, a minimal support structure can be employed to achieve the most suitable trade-off between production time, cost, and accuracy.

The process of generating a computerized geometric model for an existing part is known as Reverse Engineering (RE). It is a very useful technique in product development and plays a significant role in automotive, aerospace, and medical industries. In fact, it has been getting remarkable attention in manufacturing industries owing to its advanced data acquisition technologies. The process of RE is based on two primary steps: data acquisition (also known as scanning) and data processing. To facilitate point data acquisition, a variety of scanning systems is available with different capabilities and limitations. Although the optical control of 3D scanners is fully developed, still several factors can affect the quality of the scanned data. As a result, the proper selection of scanning parameters, such as resolution, laser power, shutter time, etc., becomes very crucial. This kind of investigation can be very helpful and provide its users with guidelines to identify the appropriate factors. Moreover, it is worth noting that no single system is ideal in all applications. Accordingly, this work has compared two portable (handheld) systems based on laser scanning and white light optical scanning for automotive applications. A car door containing a free-form surface has been used to achieve the above-mentioned goal. The design of experiments has been employed to determine the effects of different scanning parameters and optimize them. The capabilities and limitations have been identified by comparing the two scanners in terms of accuracy, scanning time, triangle numbers, ease of use, and portability. Then, the relationships between the system capabilities and the application requirements have been established. The results revealed that the laser scanner performed better than the white light scanner in terms of accuracy, while the white light scanner performed better in terms of acquisition speed and triangle numbers.

Shape memory alloys (SMAs) are advanced engineering materials which possess shape memory effects and super-elastic properties. Their high strength, high wear-resistance, pseudo plasticity, etc., makes the machining of Ni-Ti based SMAs difficult using traditional techniques. Among all non-conventional processes, micro-electric discharge machining (micro-EDM) is considered one of the leading processes for micro-machining, owing to its high aspect ratio and capability to machine hard-to-cut materials with good surface finish.The selection of the most appropriate input parameter combination to provide the optimum values for various responses is very important in micro-EDM. This article demonstrates the methodology for optimizing multiple quality characteristics (overcut, taper angle and surface roughness) to enhance the quality of micro-holes in Ni-Ti based alloy, using the Grey–Taguchi method. A Taguchi-based grey relational analysis coupled with principal component analysis (Grey-PCA) methodology was implemented to investigate the effect of three important micro-EDM process parameters, namely capacitance, voltage and electrode material.The analysis of the individual responses established the importance of multi-response optimization. The main effects plots for the micro-EDM parameters and Analysis of Variance (ANOVA) indicate that every parameter does not produce same effect on individual responses, and also that the percent contribution of each parameter to individual response is highly varied. As a result, multi-response optimization was implemented using Grey-PCA. Further, this study revealed that the electrode material had the strongest effect on the multi-response parameter, followed by the voltage and capacitance. The main effects plot for the Grey-PCA shows that the micro-EDM parameters “capacitance” at level-2 (i.e., 475 pF), “discharge voltage” at level-1 (i.e., 80 V) and the “electrode material” Cu provided the best multi-response.