With the advent of the fourth industrial revolution, the application of artificial intelligence in the manufacturing domain is becoming prevalent. Maintenance is one of the important activities in the manufacturing process, and it requires proper attention. To decrease maintenance costs and to attain sustainable operational management, Predictive Maintenance (PdM) has become important in industries. The principle of PdM is forecasting the next failure; thus, the respective maintenance is scheduled before the predicted failure occurs. In the construction of maintenance management, facility managers generally employ reactive or preventive maintenance mechanisms. However, reactive maintenance does not have the ability to prevent failure and preventive maintenance does not have the ability to predict the future condition of mechanical, electrical, or plumbing components. Therefore, to improve the facilities’ lifespans, such components are repaired in advance. In this paper, a PdM planning model is developed using intelligent methods. The developed method involves five main phases: (a) data cleaning, (b) data normalization, (c) optimal feature selection, (d) prediction network decision-making, and (e) prediction. Initially, the data pertaining to PdM are subjected to data cleaning and normalization in order to arrange the data within a particular limit. Optimal feature selection is performed next, to reduce redundant information. Optimal feature selection is performed using a hybrid of the Jaya algorithm and Sea Lion Optimization (SLnO). As the prediction values differ in range, it is difficult for machine learning or deep learning face to provide accurate results. Thus, a support vector machine (SVM) is used to make decisions regarding the prediction network. The SVM identifies the network in which prediction can be performed for the concerned range. Finally, the prediction is accomplished using a Recurrent Neural Network (RNN). In the RNN, the weight is optimized using the hybrid J-SLnO. A comparative analysis demonstrates that the proposed model can efficiently predict the future condition of components for maintenance planning by using two datasets—aircraft engine and lithium-ion battery datasets.

Titanium aluminides qualify adequately for advanced aero-engine applications in place of conventional nickel based superalloys. The combination of high temperature properties and lower density gives an edge to the titanium aluminide alloys. Nevertheless, challenges remain on how to process these essentially intermetallic alloys in to an actual product. Electron Beam Melting (EBM), an Additive Manufacturing Method, can build complex shaped solid parts from a given feedstock powder, thus overcoming the shortcomings of the conventional processing techniques such as machining and forging. The amount of energy supplied by the electron beam has considerable influence on the final build quality in the EBM process. Energy input is decided by the beam voltage, beam scan speed, beam current, and track offset distance. In the current work, beam current and track offset were varied to reflect three levels of energy input. Microstructural and mechanical properties were evaluated for these samples. The microstructure gradually coarsened from top to bottom along the build direction. Whereas higher energy favored lath microstructure, lower energy tended toward equiaxed grains. Computed tomography analysis revealed a greater amount of porosity in low energy samples. In addition, the lack of bonding defects led to premature failure in the tension test of low energy samples. Increase in energy to a medium level largely cancelled out the porosity, thereby increasing the strength. However, this trend did not continue with the high energy samples. Electron microscopy and X-ray diffraction investigations were carried out to understand this non-linear behavior of the strength in the three samples. Overall, the results of this work suggest that the input energy should be considered primarily whenever any new alloy system has to be processed through the EBM route.

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.

Purpose: Electron beam melting (EBM) is one of the potential additive manufacturing technologies to fabricate aero-engine components from gamma titanium aluminide (γ-TiAl) alloys. When a new material system has to be taken in to the fold of EBM, which is a highly complex process, it is essential to understand the effect of process parameters on the final quality of parts. This paper aims to understand the effect of melting parameters on top surface quality and density of EBM manufactured parts. This investigation would accelerate EBM process development for newer alloys. Design/methodology/approach: Central composite design approach was used to design the experiments. In total, 50 specimens were built in EBM with different melt theme settings. The parameters varied were surface temperature, beam current, beam focus offset, line offset and beam speed. Density and surface roughness were selected as responses in the qualifying step of the parts. After identifying the parameters which were statistically significant, possible reasons were analyzed from the perspective of the EBM process. Findings: The internal porosity and surface roughness were correlated to the process settings. Important ones among the parameters are beam focus offset, line offset and beam speed. By jointly deciding the total amount of energy input for each layer, these three parameters played a critical role in internal flaw generation and surface evolution. Research limitations/implications: The range selected for each parameter is applicable, in particular, to γ-TiAl alloy. For any other alloy, the settings range has to be suitably adapted depending on physical properties such as melting point, thermal conductivity and thermal expansion co-efficient. Practical implications: This paper demonstrates how melt theme parameters have to be understood in the EBM process. By adopting a similar strategy, an optimum window of settings that give best consolidation of powder and better surface characteristics can be identified whenever a new material is being investigated for EBM. This work gives researchers insights into EBM process and speeds up EBM adoption by aerospace industry to produce critical engine parts from γ-TiAl alloy. Originality/value: This work is one of the first attempts to systematically carry out a number of experiments and to evaluate the effect of melt parameters for producing γ-TiAl parts by the EBM process. Its conclusions would be of value to additive manufacturing researchers working on γ-TiAl by EBM process.

Zirconia ceramics are versatile materials with remarkable properties such as a high thermal resistance, high fracture strength, and low thermal conductivity. They are chemically inert and highly wear- and corrosion-resistant, making them ideal for a wide range of applications in the aerospace, automotive, and biomedical fields. In dentistry, zirconia ceramics are used for veneers, crowns, bridges, and implants because of their biocompatibility. Despite the various benefits of zirconia ceramics, they are difficult to process because of their high hardness and brittleness. Additive manufacturing (AM) has proven to be a viable alternative to conventional fabrication processes, particularly for the processing of difficult-to-cut materials. AM of ceramics has gained significant attention in recent years because of its flexibility and ability to produce customized geometries rapidly and economically. In this study, the digital light processing (DLP) technique was employed to 3D print yttria-stabilized zirconia. The fabricated zirconia was evaluated and characterized for use in dental applications. Thermogravimetric analysis (TGA) and differential thermogravimetry (DTG) were performed on the green body to assess the decomposition of the additives in the slurry and determine the debinding temperatures. The as-built parts were subjected to debinding and sintering to obtain fully dense zirconia parts. The parts tended to shrink after sintering; therefore, the shrinkage ratios were evaluated and found to be 1.2817, 1.2900, and 1.3388 in the x-, y-, and z-directions, respectively. The average density after sintering was 6.031 g/cc. The flexural strength determined using four-point bending tests was 451.876 MPa, and the tensile and compressive strengths were 143 MPa and 298.4 MPa, respectively.

The quality of micro-features in various technologies is mostly affected by the choice of the micro-fabrication technique, which in turn results in several limitations with regard to materials, productivity, and cost. Laser beam micro-machining has a distinct edge over other non-traditional methods in terms of material choices, precision, shape complexity, and surface integrity. This study investigates the effect of laser fluence and pulse overlap while developing microchannels in alumina ceramic using an neodymium-doped yttrium aluminum garnet (Nd:YAG) laser. Microchannels 200 µm wide with different depths were machined using different laser peak fluence and pulse overlap (percentage of overlap between successive laser pulses) values. It was found that high pulse overlaps and fluences should be avoided as they give rise to V-shaped microchannels i.e., 100% bottom width errors. The optimal peak fluence range was found to be around 125–130 J/cm² corresponding to 3–5 µm depth per scan. In addition, channels fabricated with moderate pulse overlap were found to be of good quality compared to low pulse overlaps.