Computer aided inspection (CAI) of non-rigid parts significantly contributes to improving performance of products, reducing assembly time and decreasing production costs. CAI methods use scanners to measure point clouds on parts and compare them with the nominal computer aided design (CAD) model. Due to the compliance of non-rigid parts and for inspection in supplier and client facilities, two sets of sophisticated and expensive dedicated fixtures are usually required to compensate for the deformation of these parts during inspection. CAI methods for fixtureless inspection of non-rigid parts aim at scanning these parts in a free-state for which, one of the main challenges is to distinguish between possible geometric deviation (defects) and flexible deformation associated with free-state. In this work the generalized inspection fixture (GNIF) method is applied to generate a prior set of corresponding sample points between CAD and scanned models. These points are used to deform the CAD model to the scanned model via finite element non-rigid registration. Then defects are identified by comparing the deformed CAD model with the scanned model. The fact that some sample points can be located close to defects, results in an inaccurate estimation of these defects. In this paper a method is introduced to automatically filter out sample points that are close to defects. This method is based on curvature and von Mises stress. Once filtered, remaining sample points are used in a new registration, which allows identifying and quantifying defects more accurately. The proposed method is validated on aerospace parts.

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The high entropy alloy (HEA) filler used during the fabrication method determines the reliability of HEAs for steel-aluminum dissimilar alloy configuration. HEAs have a direct impact on the formation of intermetallic compounds (IMC) formed by the interaction of iron (Fe) and aluminum (Al), and influence the size of the joint’s interaction zone. A novel welding process for Fe-Al alloy joints was developed to prevent the development of a brittle iron-aluminum interface. This research involved investigation of the possibility of using HEA powdered filler. Fe5Co20Ni20Mn35Cu20 HEAs was used as a filler for the laser joining lap configuration joining hyper-duplex stainless steel UNS S33207 to aluminum alloy 6061. This HEA has unique properties, such as high strength, good ductility, and high resistance to corrosion and wear. A tiny portion of the stainless-steel area was melted by varying the welding parameters. The high-entropy alloy (HEA) with slow kinetic diffusion and large entropy was employed to aid in producing solid solution structures, impeding the blending of iron and aluminum particles and hindering the development of Fe-Al IMCs. The weld seam was created without the use of Fe-Al IMCs,. The specimen broke at the HEAs/Al alloy interface with a tensile-shear strength of 237 MPa. The tensile-shear strength achieved was 12.86% higher than for the base metal AA 6061 and 75.57% lower than for the UNS S33207 hyper-duplex stainless steel.

In the era of the fourth industrial revolution, several concepts have arisen in parallel with this new revolution, such as predictive maintenance, which today plays a key role in sustainable manufacturing and production systems by introducing a digital version of machine maintenance. The data extracted from production processes have increased exponentially due to the proliferation of sensing technologies. Even if Maintenance 4.0 faces organizational, financial, or even data source and machine repair challenges, it remains a strong point for the companies that use it. Indeed, it allows for minimizing machine downtime and associated costs, maximizing the life cycle of the machine, and improving the quality and cadence of production. This approach is generally characterized by a very precise workflow, starting with project understanding and data collection and ending with the decision-making phase. This paper presents an exhaustive literature review of methods and applied tools for intelligent predictive maintenance models in Industry 4.0 by identifying and categorizing the life cycle of maintenance projects and the challenges encountered, and presents the models associated with this type of maintenance: condition-based maintenance (CBM), prognostics and health management (PHM), and remaining useful life (RUL). Finally, a novel applied industrial workflow of predictive maintenance is presented including the decision support phase wherein a recommendation for a predictive maintenance platform is presented. This platform ensures the management and fluid data communication between equipment throughout their life cycle in the context of smart maintenance.

The fatigue life of overhead conductors is usually evaluated through experimental tests on clamp/conductor assemblies. Some recent studies aim to estimate the fatigue life of conductors using uniaxial tests on individual strands. This paper presents an innovative method for assessing the fretting fatigue life of overhead conductors combining the effect of both tension and bending loadings. It consists of coupling a numerical approach based on modeling the clamp/conductor assembly using the finite element method and an experimental one based on fretting fatigue tests on individual wires. A biaxial fretting fatigue test rig has been developed and validated through preliminary tests performed on 1350-H19 aluminum wires under uniaxial and an equivalent biaxial loading. Tension and bending loadings obtained from the numerical model were then applied on individual wires. Results showed a good correspondence with existing experimental data of the fatigue tests carried on the aluminum conductor steel reinforced (ACSR) Bersfort conductor with a metal-to-metal suspension clamp.

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This study displays the effect of laser surface hardening parameters on the hardness profile (case depth) of a splined shaft made of AISI 4340 steel. The approach is mainly based on experimental tests wherein the hardness profile of laser hardened splines is acquired using micro-hardness measurements. These results are then evaluated with statistical analysis (ANOVA) to determine the principal effect and the contributions of each parameter in the laser hardening process. Using empirical correlations, the case depth of splined shaft at tip and root of spline’s teeth is also estimated and verified with measured data. The obtained results were then used to study the sensitivity of the measured case depths according to the evolution of laser process parameters and geometrical factors. The feasibility and efficiency of the proposed approach lead to a reliable statistical model in which the hardness profile of the spline is estimated with respect to its specific geometry.

The coronavirus disease 2019 (COVID-19) rapidly spread to over 180 countries and abruptly disrupted production rates and supply chains worldwide. Since then, 3D printing, also recognized as additive manufacturing (AM) and known to be a novel technique that uses layer-by-layer deposition of material to produce intricate 3D geometry, has been engaged in reducing the distress caused by the outbreak. During the early stages of this pandemic, shortages of personal protective equipment (PPE), including facemasks, shields, respirators, and other medical gear, were significantly answered by remotely 3D printing them. Amidst the growing testing requirements, 3D printing emerged as a potential and fast solution as a manufacturing process to meet production needs due to its flexibility, reliability, and rapid response capabilities. In the recent past, some other medical applications that have gained prominence in the scientific community include 3D-printed ventilator splitters, device components, and patient-specific products. Regarding non-medical applications, researchers have successfully developed contact-free devices to address the sanitary crisis in public places. This work aims to systematically review the applications of 3D printing or AM techniques that have been involved in producing various critical products essential to limit this deadly pandemic’s progression.