The additive manufacturing technique of material extrusion has challenge of excessive process defects and not achieving the desired mechanical properties. The industry is trying to develop certification to better control variations in mechanical attributes. The current study is a progress towards understanding the evolution of processing defects and the correlation of mechanical behavior with the process parameters. Modeling of the 3D printing process parameters such as layer thickness, printing speed, and printing temperature is carried out through L27 orthogonal array using Taguchi approach. In addition, CRITIC embedded WASPAS is adopted to optimize the parts' mechanical attributes and overcome the defects. Flexural and tensile poly-lactic acid specimens are printed according to ASTM standards D790 and D638, respectively, and thoroughly analyzed based on the surface morphological analysis to characterize defects. The parametric significance analysis is carried out to explore process science where the layer thickness, print speed, and temperature significantly control the quality and strength of the parts. Mathematical optimization results based on composite desirability show that layer thickness of 0.1 mm, printing speed of 60 mm/s, and printing temperature of 200 °C produce significantly desirable results. The validation experiments yielded the maximum flexural strength of 78.52 MPa, the maximum ultimate tensile strength of 45.52 MPa, and maximum impact strength of 6.21 kJ/m². It is established that multiple fused layers restricted the propagation of cracks with minimum thickness due to enhanced diffusion between the layers.

The superior engineering properties and excellent biocompatibility of titanium alloy (Ti6Al4V) stimulate applications in biomedical industries. Electric discharge machining, a widely used process in advanced applications, is an attractive option that simultaneously offers machining and surface modification. In this study, a comprehensive list of roughening levels of process variables such as pulse current, pulse ON time, pulse OFF time, and polarity, along with four tool electrodes of graphite, copper, brass, and aluminum are evaluated (against two experimentation phases) using a SiC powder-mixed dielectric. The process is modeled using the adaptive neural fuzzy inference system (ANFIS) to produce surfaces with relatively low roughness. A thorough parametric, microscopical, and tribological analysis campaign is established to explore the physical science of the process. For the case of the surface generated through aluminum, a minimum friction force of ~25 N is observed compared with the other surfaces. The analysis of variance shows that the electrode material (32.65%) is found to be significant for the material removal rate, and the pulse ON time (32.15%) is found to be significant for arithmetic roughness. The increase in pulse current to 14 A shows that the roughness increased to ~4.6 µm with a 33% rise using the aluminum electrode. The increase in pulse ON time from 50 µs to 125 µs using the graphite tool resulted in a rise in roughness from ~4.5 µm to ~5.3 µm, showing a 17% rise.

Machining operations are very common for the production of auto parts, i.e., connecting rods, crankshafts, etc. In machining, the use of cutting oil is very necessary, but it leads to higher machining costs and environmental problems. About 17% of the cost of any product is associated with cutting fluid, and about 80% of skin diseases are due to mist and fumes generated by cutting oils. Environmental legislation and operators’ safety demand the minimal use of cutting fluid and proper disposal of used cutting oil. The disposal cost is huge, about two times higher than the machining cost. To improve occupational health and safety and the reduction of product costs, companies are moving towards sustainable manufacturing. Therefore, this review article emphasizes the sustainable machining aspects of steel by employing techniques that require the minimal use of cutting oils, i.e., minimum quantity lubrication, and other efficient techniques like cryogenic cooling, dry cutting, solid lubricants, air/vapor/gas cooling, and cryogenic treatment. Cryogenic treatment on tools and the use of vegetable oils or biodegradable oils instead of mineral oils are used as primary techniques to enhance the overall part quality, which leads to longer tool life with no negative impacts on the environment. To further help the manufacturing community in progressing towards industry 4.0 and obtaining net-zero emissions, in this paper, we present a comprehensive review of the recent, state of the art sustainable techniques used for machining steel materials/components by which the industry can massively improve their product quality and production.

Early diagnosis of pandemic diseases such as COVID-19 can prove beneficial in dealing with difficult situations and helping radiologists and other experts manage staffing more effectively. The application of deep learning techniques for genetics, microscopy, and drug discovery has created a global impact. It can enhance and speed up the process of medical research and development of vaccines, which is required for pandemics such as COVID-19. However, current drugs such as remdesivir and clinical trials of other chemical compounds have not shown many impressive results. Therefore, it can take more time to provide effective treatment or drugs. In this paper, a deep learning approach based on logistic regression, SVM, Random Forest, and QSAR modeling is suggested. QSAR modeling is done to find the drug targets with protein interaction along with the calculation of binding affinities. Then deep learning models were used for training the molecular descriptor dataset for the robust discovery of drugs and feature extraction for combating COVID-19. Results have shown more significant binding affinities (greater than −18) for many molecules that can be used to block the multiplication of SARS-CoV-2, responsible for COVID-19.

The exponential rise of healthcare problems like human aging and road traffic accidents have developed an intrinsic challenge to biomedical sectors concerning the arrangement of patient-specific biomedical products. The additively manufactured implants and scaffolds have captured global attention over the last two decades concerning their printing quality and ease of manufacturing. However, the inherent challenges associated with additive manufacturing (AM) technologies, namely process selection, level of complexity, printing speed, resolution, biomaterial choice, and consumed energy, still pose several limitations on their use. Recently, the whole world has faced severe supply chain disruptions of personal protective equipment and basic medical facilities due to a respiratory disease known as the coronavirus (COVID-19). In this regard, local and global AM manufacturers have printed biomedical products to level the supply–demand equation. The potential of AM technologies for biomedical applications before, during, and post-COVID-19 pandemic alongwith its relation to the industry 4.0 (I4.0) concept is discussed herein. Moreover, additive manufacturing technologies are studied in this work concerning their working principle, classification, materials, processing variables, output responses, merits, challenges, and biomedical applications. Different factors affecting the sustainable performance in AM for biomedical applications are discussed with more focus on the comparative examination of consumed energy to determine which process is more sustainable. The recent advancements in the field like 4D printing and 5D printing are useful for the successful implementation of I4.0 to combat any future pandemic scenario. The potential of hybrid printing, multi-materials printing, and printing with smart materials, has been identified as hot research areas to produce scaffolds and implants in regenerative medicine, tissue engineering, and orthopedic implants.