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Abstract Effects of process parameters such as rotational speed, feed rate, and drill diameters on the drilling behavior of basalt-epoxy-based composites including 2.5 wt.% Al2O3 particles manufactured by mixing and compression method were investigated by Taguchi’s technique. The experimental results showed that the burr height (BH) increased considerably almost linearly with an increase in the drill diameter, while it remained stable with speed and decreased the feed rate slightly. There was an excellent correlation between the control factors and responses, BH of basalt fiber-reinforced plastics (BFRPs) through the Taguchi approach. The model had an adjusted R2 value of 96.3%. Generally, the inclusion of Al2O3 particles in BFRP increased its cutting force properties. Optimized drilling conditions for the input variables to produce the lowest response of the BH for composites were rotational speed of 560 rpm and feed rate of 0.28 mm/rev and a drill diameter of 4.5 mm.

Abstract The high quality of the machined parts in a short time is a research challenge for enhancing these parts’ operating performance. Optimizing the machining operations and adequately selecting the cutting parameters can solve this challenge. Thus, this work proposes an optimization approach of the machining process parameters of epoxy hybrid nanocomposites reinforced by multiwall carbon nanotubes (MWCNTs) and aluminum oxide (Al2O3). Cutting speed (V), feed rate (F), insert nose radius, and depth of cut (D) were the machining parameters. The roundness error and surface roughness (Ra) were selected as process response control parameters. The optimization techniques such as response surface method (RSM) and grey relation analysis (GRA) with the variance of analysis (ANOVA) were involved. Forty experimental runs were performed. The RSM optimization and ANOVA results showed that the insert nose radius and F are the most significant factors that affect the Ra.

Abstract Finite Element Analysis (FEA) of metal cutting is largely the domain of research organizations. Despite significant advances towards accurately modelling metal machining processes, industrial adoption of these advances has been limited. Academic studies, which mainly focused on orthogonal cutting, fail to address this discrepancy. This paper bridges the gap between simplistic orthogonal cutting models and the complex components typical in the manufacturing sector. This paper outlines how to utilize results from orthogonal cutting simulations to predict industrially relevant performance measures efficiently. In this approach, using 2D FEA cutting models a range of feed, speed and rake angles are simulated. Cutting force coefficients are then fit to the predicted cutting forces. Using these coefficients, forces for 3D cutting geometries are calculated.

Abstract In an automated machining process, monitoring the conditions of the tool is essential for deciding to replace or repair the tool without any manual intervention. Intelligent models built with sensor information and machine learning techniques are predicting the condition of the tool with good accuracy. In this study, statistical models are developed to identify the conditions of the abrasive grinding wheel using the Acoustic Emission (AE) signature acquired during the surface grinding operation. Abrasive grinding wheel conditions are identified using the abrasive wheel wear plot established by conducting experiments. The piezoelectric sensor is used to capture the AE from the grinding process, and statistical features of the abrasive wheel conditions are extracted in time and wavelet domains of the signature. Machine learning algorithms, namely, Classification and Regression Trees (CART) and Support Vector Classifiers (SVC), are used to build statistical models.

Abstract In order to meet the worldwide increasingly stringent particulate matter (PM) and particulate number (PN) emission limits, the diesel particulate filter (DPF) is widely used today and has been considered to be an indispensable feature of modern diesel engines. To estimate the soot loading amount in the DPF accurately and in real-time is a key function of realizing systematic and efficient applications of diesel engines, as starting the thermal regeneration of DPF too early or too late will lead to either fuel economy penalty or system reliability issues. In this work, an open-loop and on-line approach to estimating the DPF soot loading on the basis of soot mass balance is developed and experimentally investigated, through establishing and combining prediction models of the NOx and soot emissions out of the engine and a model of the catalytic soot oxidation characteristics of passive regeneration in the DPF.

Abstract Combining ball milling with stir casting in the synthesis of nanocomposites is found effective in increasing the strength and ductility of the nanocomposites. In the first step, the nanoparticles used as reinforcement are generated by milling a mixture of aluminum (Al) and manganese dioxide (MnO2) powders. A mixture of Al and MnO2 powders are mixed in the ratio of 1:2.4 by weight and milled at 300 rpm in a high-energy planetary ball mill for different durations of 120 min, 240 min, and 360 min to generate nano-sized alumina (Al2O3) particles. It is supposed that the powders have two different roles during milling, firstly, to generate nano-sized Al2O3 by oxidation at the high-energy impact points due to collision between Al and MnO2 particles, and secondly, to keep nano-sized Al2O3 particles physically separate by the presence of coarser particles.

Abstract The efficiency of the traditional machining process becomes limited because of the mechanical properties and complexity of the geometric shape of the processed materials. This difficulty is resolved through the nonconventional machining process. Electric Discharge Machining (EDM) process is one of the popular nonconventional machining processes among all nonconventional machining processes for processing such materials. The main objective of the present research work is to evaluate the effect of percentage weight fraction of reinforcement and process parameters on machining responses during EDM of aluminum (Al) 7075-reinforced boron carbide (B4C) and graphite metal matrix composite (MMC) and optimization of the result.

Abstract The impact of Laser Beam Machining (LBM) process parameters on Surface Roughness (SR) and kerf width during machining is investigated in this work. Stainless Steel is a material that is resistant to corrosion. LBM is a nontraditional machining method in which material is removed by melting and vaporizing metal when a laser beam collides with the metal surface. There are numerous process variables that influence the quality of the LBM-cut machined surface. However, the most essential factors are laser power, cutting speed, assist gas pressure, nozzle distance, focus length, pulse frequency, and pulse width. SR, Material Removal Rate (MRR), and kerf width and heat affected zone are significant performance indicators in LBM. The influence of LBM process parameters on SR and kerf width while machining stainless steel material is investigated in this study.

Abstract This work presents an experimental analysis of the bulk content characterization of 2024 T351 Aluminum alloy under cyclic loadings used for precision applications such as balancing, optical, and laser instruments. Test samples with various machining directions (longitudinal and orthogonal) are formed using a CNC milling machine. Inelastic and plastic deformations in the nanoscale are the investigated characteristics of interest; hence, the fabric’s time constant at a fixed quarter-hour span. Samples with specific geometry are subjected to a tensile stress range of 10-150 N/mm2 provided by an electromagnetic test device. It should be said that all types of deformations considered were measured with and without loading using interferometers and capacitive sensors. Experiments are performed under constant temperature-stable housing whereas experimental measurements are recorded within the residual strain range of 10 microns.

Abstract Directional drilling rigs and hydraulic stimulation equipment typically use diesel fueled compression ignition (CI) engines. The majority of these engines are compliant with US Environmental Protection Agency (EPA) Tier 2 standards. To reduce fuel costs, industry is investing in dual fuel (DF) and dedicated natural gas (DNG) engines. DF engines use diesel oxidation catalysts (DOCs) to reduce CO and NMHC emissions. DNG engines may be either lean-burn or rich-burn and the latter uses three-way catalysts (TWC) to reduce CO, NMHC, and NOx emissions. This research presents in-use catalyst efficiency data collected pre- and post-catalyst for three DF engines and two DNG engines. One DF engine was converted earlier and did not include a DOC. Data were collected from six Tier 2 engines, two CI drilling engines converted to operate as DF, two CI hydraulic fracturing engines converted to operate as DF, and two SI DNG drilling engines.

Abstract The conception of Automated Driving Systems is expanding fast with the expectation of the whole society and with heavy investments toward research and development. However, the insertion of these vehicles in real scenarios worldwide is still a challenge for governments, once they require an important evolution of the legal and regulatory framework. Although there are several initiatives to accelerate the insertion process, each country has specificities when considering the traffic scenario. In order to contribute to this emerging problem, this article presents a perspective of how the insertion of these vehicles can be performed considering specificities of the Brazilian scenario, one of the world's biggest car markets. Thus, it is discussed the global scenario of autonomous vehicles, the Brazilian traffic system, and the certification and homologation process, focusing on a new test track proposal.

Abstract Peripheral grinding of the aluminum alloy EN AB-AlSi9Cu3(Fe) using a vitrified silicon carbide grinding wheel was investigated in this article. The effect of grinding parameters, namely, grinding speed, feed and depth of cut, and grinding condition, up-grinding or down-grinding, on resulting forces, grinding energy, and surface roughness were analyzed. A 22 × 32 full factorial design of experiments was performed. The ground surface morphology showed evidence of rubbing and plowing effects, and ductile material removal was the main mechanism. Within the analyzed process window, the minimum value of surface roughness was 0.28 μm. The experimental evaluation highlighted that forces and grinding energy are directly dependent on chip thickness, and this relationship was further explored as a function of depth of cut and feed per grain. Conversely, an inverse dependence was observed in the case of surface roughness.

Abstract Fatigue performance can be a critical attribute for the production of structural parts or components via additive manufacturing (AM). In comparison to the static tensile behavior of AM components, there is a lack of knowledge regarding the fatigue performance. The growing market demand for AM implies the need for more accurate fatigue investigations to account for dynamically loaded applications. A357.0 parts are processed by laser-based powder bed fusion (L-PBF) in order to evaluate the effect of surface finishing on fatigue behavior. The specimens are surface finished by shot peening using ϕ = 0.2 and ϕ = 0.4 mm steel particles and ϕ = 0.21-0.3 mm zirconia-based ceramic particles.

Abstract Grinding is a precision machining process that is widely used to achieve good surface integrity and inish. In order to study and reveal the influence of grinding process parameters such as grinding depth, feed speed, and wheel linear speed on the surface quality of the slider raceway, a series of single-factor grinding experiments under different grinding parameters are carried out on high-speed precision surface grinding machine in this research. 3D surface profiles of the slider raceway are obtained after the grinding experiments. An image processing method is employed to evaluate the surface quality of slider raceway by surface roughness, height distribution function, skewness, and kurtosis. Vibrations of spindle and workpiece, maximum undeformed chip thickness (MUCT), and grinding force are taken into consideration to reveal the correlation between grinding parameters and surface quality.

Abstract This article focuses on a comparative research of the emissions discharged from four vehicles equipped with SI engines, which comply with different emission control systems (Euro 6, Euro 5, and Euro 3). The vehicles used for this work were installed with two different fuel injection technologies (direct injection and port fuel injection) and were operated with three different types of fuels (RON 95, M15, and E10). The tests were performed at the Joint Research Center (JRC) in Ispra using a state-of-the-art emissions test facility according to the European emissions legislation. The test bench included a chassis dynamometer and two different driving cycles were used: NEDC and US06.

Abstract Aluminum Metal Matrix Composite (AMMC) materials have loftier individualities and are known as an alternative material for a range of aerospace and automotive engineering applications. Reinforcement inclusion makes the components tougher, resulting in low performance of machining by traditional conservative machining practices. The present study presents a detailed review of the machinability of AMMC (Pure Aluminum + Graphene nanoplatelets) using Wire Electric Discharge Machining (WEDM). For WEDM of AMMC, a multi-objective optimization method is proposed to evaluate possible machining parameters in order to achieve better machining efficiency. Taguchi’s approach to the design of experiments is used to organize the experiments. For performing experiments, an L27 orthogonal array was selected. Five input process variables were considered for this study. The Grey Relational Analysis (GRA) is used to achieve the best features of multi-performance machining.

Abstract During aircraft wing assembly, machined fiberglass shims are often used between mating parts to compensate for inherent geometric variability due to manufacturing. At present, fiberglass shims for large aerospace structures, such as shims attached to wing ribs, are manufactured either manually or by precision machining, both of which pose a challenge due to tight tolerance requirements and wide geometric variations in the aircraft structures. Relative to articulated arm industrial robots, gantry-style computer numerical control (CNC) machines are costly, consume large footprints, and are inflexible in the application. Therefore, industrial robots are viewed as potential candidates to replace these gantry systems to facilitate metrology, shim machining, and permanent joining of aircraft structure, with all these processes taking place in the assembly process step.

Abstract The elastic hydrodynamic lubrication (EHL) region of the crankpin bearing (CB) not only creates the high friction force due to the solid asperity contact but also reduces the CB’s lubrication effectiveness. To improve the CB’s tribological properties, the partial textures (PT) designed on the EHL region are proposed. Based on a new hydrodynamic approach combined between the CB’s lubrication model and the slider-crank-mechanism (SCM) dynamics model, the distribution density of spherical dimples (SDs) and different structures of the SDs, circular-cylindrical dimples (CCDs), square-cylindrical dimples (SCDs), and wedge-shaped dimples (WSDs) are then simulated and assessed for their effectiveness on improving CB’s tribological properties, respectively. The oil film pressure (p), friction force (F f), and friction coefficient (μ) of the CB are selected as the evaluation indexes.

Abstract Autonomy is a key enabling factor in uncrewed aircraft system (UAS) and advanced air mobility (AAM) applications ranging from cargo delivery to structure inspection to passenger transport, across multiple sectors. In addition to guiding the UAS, autonomy will ensure that they stay safe in a large number of off-nominal situations without requiring the operator to intervene. While the addition of autonomy enables the safety case for the overall operation, there is a question as to how we can assure that the autonomy itself will work as intended. Specifically, we need assurable technical approaches, operational considerations, and a framework to develop, test, maintain, and improve these capabilities. We make the case that many of the key autonomy functions can be realized in the near term with readily assurable, even certifiable, design approaches and assurance methods, combined with risk mitigations and strategically defined concepts of operations.

Abstract Milling is a prevalent machining technique employed in various industries for the production of metallic and non-metallic components. This article focuses on the optimization of cutting parameters for polyamide (PA6) using carbide tools, utilizing a recently developed multi-objective, nature-inspired metaheuristic algorithm known as the Multi-Objective Grasshopper Optimization Algorithm (MOGOA). This optimization process’s primary objectives are minimizing surface roughness and maximizing the material removal rate. By employing the MOGOA algorithm, the study demonstrates its efficacy in successfully optimizing the cutting parameters. This research’s findings highlight the MOGOA algorithm’s capability to effectively fine-tune cutting parameters during PA6 machining, leading to improved outcomes in terms of surface roughness reduction and enhanced material removal rate.