Total knee replacement (TKR) is a remarkable achievement in biomedical science that enhances human life. However, human beings still suffer from knee-joint-related problems such as aseptic loosening caused by excessive wear between articular surfaces, stress-shielding of the bone by prosthesis, and soft tissue development in the interface of bone and implant due to inappropriate selection of TKR material. The choice of most suitable materials for the femoral component of TKR is a critical decision; therefore, in this research paper, a hybrid multiple-criteria decision-making (MCDM) tactic is applied using the degree of membership (DoM) technique with a varied system, using the weighted sum method (WSM), the weighted product method (WPM), the weighted aggregated sum product assessment method (WASPAS), an evaluation based on distance from average solution (EDAS), and a technique for order of preference by similarity to ideal solution (TOPSIS). The weights of importance are assigned to different criteria by the equal weights method (EWM). Furthermore, sensitivity analysis is conducted to check the solidity of the projected tactic. The weights of importance are varied using the entropy weights technique (EWT) and the standard deviation method (SDM). The projected hybrid MCDM methodology is simple, reliable and valuable for a conflicting decision-making environment.

The prevalence of micro-holes is widespread in mechanical, electronic, optical, ornaments, micro-fluidic devices, etc. However, monitoring and detection tool wear and tool breakage are imperative to achieve improved hole quality and high productivity in micro-drilling. The various multi-sensor signals are used to monitor the condition of the tool. In this work, the vibration signals and cutting force signals have been applied individually as well as in combination to determine their effectiveness for tool-condition monitoring applications. Moreover, they have been used to determine the best strategies for tool-condition monitoring by prediction of hole quality during micro-drilling operations with 0.4 mm micro-drills. Furthermore, this work also developed an adaptive neuro fuzzy inference system (ANFIS) model using different time domains and wavelet packet features of these sensor signals for the prediction of the hole quality. The best prediction of hole quality was obtained by a combination of different sensor features in wavelet domain of vibration signal. The model’s predicted results were found to exert a good agreement with the experimental results.

This paper analyses three components of total cutting force and chip shape changes when finish turning 17-4 PH (precipitation hardening) stainless steel. A Finite Element Method (FEM) simulation of cutting forces was also performed using the Johnson–Cook constitutive model. The results were compared with those obtained from experimental studies. Variable feeds of 0.05–0.4 mm/rev and depth of cut of 0.2–1.2 mm with a cutting speed of 220 m/min were used. The studies were carried out under dry and wet cooling conditions and with the use of minimum quantity lubrication (MQL). This research was realized based on the Parameter Space Investigation (PSI) method. Statistical analysis of the obtained results was carried out using Statistica-13 software. It was found that the cutting force F_c and feed force F_f depend on the depth of cut and feed, and the passive force F_p depends mainly on the feed. Compared to dry cutting conditions, a reduction of 43% and 39% of the cutting force F_c was achieved for wet machining and MQL machining, respectively. Regardless of the cooling conditions, a favorable chip shape was registered for a_p = 1–1.1 mm and f = 0.25–0.3 mm/rev. Compared to the experimental studies, the FEM simulation showed differences of ~13% for the cutting force F_c and of ~36% for the feed force F_f.

In this paper, the features of the strength, fractures, and tribological behavior of metal-matrix composites based on the FeGr1 material are discussed. To improve the material properties, a mixture of SiC, Al₂O₃ and C nanoparticulates have been added to an iron-based matrix. The simplex lattice design method and hardness, compression, and bending tests were used to determine the mechanical properties. Scanning electron microscopy was applied for fracture features analysis. Different fracture types, mainly trans-crystalline quasi-brittle and brittle fracture or inter-granular fracture and microcracks were registered for the composites tested. Depending on the type and amount of ceramic additives, significant changes in strength, as well as in the fracture features of the metal-matrix composites (MMCs), were observed. Based on tribological tests, changes in the momentary coefficients of friction, temperature of the friction surface, and wear rate of the composites with nanoparticulates were described. An analysis of the worn surface morphology revealed changes in the wear process depending on the MMC composition. It was shown that the use of hybrid mixed additives based on hard ceramic nanoparticulates improved both strength and tribological properties of composites.

In milling operation, the analysis of power signals are quite important because power signals provide the immediate information of the process that allows the operator to monitor the performance and make necessary modifications in real time. In addition, the power signals are correlated with the quality of machined surface because high power may result in tool breakage that leads to poor surface finish. Therefore, this work firstly investigates the analysis of power signals and its impact on surface characteristics during the milling of additively manufactured aluminium alloys under different conditions. The milling experiments were performed under different cooling conditions and the power signals were calculated from the main cutting forces. Then, the Swin transformer based deep learning model was used on the calculated power signals, and the performance was compared with different models to validate the accuracy of Swin transformer model. Further, the relation between cutter angle rotation and power signals were analyzed under different conditions. According to the surface analysis, the higher power ranges observed during dry machining have a major impact on surface quality and lead to rough milling surfaces. The outcomes also demonstrated that the Swin transformer shows better performance with higher accuracy and Kappa metrics with 0.0001 learning rate.