It is difficult to automatically recognize defects using digital image processing method in X-ray radiograph that tested from lap joint of unequal thickness plates. The continuous change of the wall thickness for the lap joint workpiece brings about the very different gray levels in X-ray background image. Besides, due to the shape and fixturing of the workpiece, the distribution of the weld seam in the radiograph is not vertical and results in an existence of a certain angle with the vertical direction. This makes it difficult for automatic defect detection and localization. In this paper, a method of X-ray image correction based on invariant moments is presented to solve the problem. In addition, an automatic defect detection method combined of image noise suppression, background removal, image segmentation and mathematical morphology is introduced. The results show that the proposed method can effectively recognize the gas pores in lap joint of unequal thickness and it is suitable for automatic detection.

This work deals with the evaluation of adhesive single-lap joints using ultrasonic guided waves. An adhesive bonded lap joint can be modelled as a three-layer system made up of two adherents and an adhesive. The propagation of guided waves in the joint is developed from matrix formulations. The behavior of the different guided wave modes that propagate in the bonded region is characterized and it is found that their relative amplitudes can be estimated based on the properties of the incoming wave that propagates in the non-bonded region. It was verified that the excitation of these modes is ruled by the degree in which the shapes of both modes match each other. A 3D simulation of two aluminium bonded plates using 500 kHz ultrasonic transducers in a pitch and catch configuration was implemented using the Matlab k-Wave toolbox. Scattering effects due to some produced defects located in the bond line of the lap joints were simulated. Experimental setup with some artificial defects produced in the aluminum joints were used in order to compare with the simulated. Qualitative agreement was observed for the two approaches. The observed deviation can be due to the different characteristics of the experimental and simulated defects.

Natural Fibre Reinforced Composites (NFRCs) represent a significant category of materials that has the capacity to contribute to the worldwide objectives of sustainability and environmental preservation. To maximise the potential of composite materials, it is essential to establish their high quality and cost-effective manufacture. While the basic production procedures for producing items based on NFRCs have reached a high level of development, more research efforts are needed to advance their secondary manufacturing processes, namely in the areas of machining and joining. The primary factor leading to the rejection of composite goods with holes is the damage generated during the drilling process. The present experimental study aims to examine the effectiveness of hole production during the moulding phase. A comparative analysis has been conducted to assess the mechanical characteristics of moulded and drilled holes in jute fibre reinforced epoxy composite. It was observed that the moulded holes exhibit superior performance compared to the drilled holes for jute/epoxy materials. Additionally, the load extension curves indicate that the moulded holes experience greater extension in comparison to the drilled holes.

The electric vehicle (EV) industry challenges battery joining technologies by requiring higher energy density both by mass and volume. Improving the energy density via new battery chemistry would be the holy grail but is seriously hindered and progresses slowly. In the meantime, alternative ways, such as implementing more efficient cell packaging by minimizing the electrical resistance of joints are of primary focus. In this paper we discuss the challenges associated with the electrical characterisation of laser soldered joints in general, and the minimization of their resistive losses, in particular. In order to assess the impact of the joint resistance on the overall resistance of the sample, the alteration in resistance was monitored as a function of voltage probe distance and modelled by finite element simulation. The experimental measurements showed two different regimes: one far from of the joint area and another in its vicinity and within the joints cross section. The presented results confirm the importance of the thickness of the filler material, the effective and the total soldered area, and the area and the position of the voids within the total soldered area in determining the electrical resistance of joints.

Lap joints were friction stir welded in AA 7075-T6 at 1320 rpm and using two welding speeds: 70 mm/min and 120 mm/min. The temperature profiles of the weld zone were registered during the process; furthermore, the microhardness, tensile strength, and microstructure of the joints produced were examined. This study brings new insights into the thermomechanical behavior of the material during the process. The peak temperature obtained with the lower welding speed (70 mm/min) is 10% higher than that obtained with the higher speed (120 mm/min). Moreover, a 5% increase in microhardness and a 6% increase in tensile strength were were observed increasing the welding speed. Addition-ally, the microstructure examination demonstrated that, by decreasing the welding speed, the larger interaction between the tool and the material results in a deeper stir zone due to the increased heat diffusion downwards to the material. This heat flow affects the thermal profile and influences the resulting mechanical properties of the welded joints.

The article describes machine learning using artificial neural networks (ANN) to develop the parameters of the friction stir welding (FSW) process for three types of aluminum joints (EN AW 7075). A total of 608 experimental data were used to build the ANN. Two types of networks were built: the first one was used to classify good/bad connections with the MLP 7-19-2 topology, the second one was used to regress the tensile load-bearing capacity with the MLP 7-19-1 topology. Selected parameters of the FSW process were used as input data for ANN training: rotational speed, welding speed, joint and tool geometry. Based on a case study, the quality of the FSW joint was assessed in terms of microstructure and mechanical properties. The usefulness of both trained neural networks has been demonstrated. For the regression network, the quality of the validation set was approximately 93.6%. In the case of confusion matrix for the test set, errors never exceeded 6%. Only 184 epochs were needed to train the regression network. The quality of the validation set was approximately 87.1%. On their basis, predictive maps were developed and presented in the work allowing for the selection of optimal parameters of the FSW process for three types of joints.

This study proposed a regression model for predicting fatigue properties based on crucial weld geometry factors in lap-welded joints with gaps using statistical analysis. Welding conditions were varied to build various weld geometries in joints configured in a lap from with gaps of 0, 0.2, 0.5, and 1.0 mm, and 87 S-N curves for the lap-welded joints were derived. As input variables, 17 weld geometry factors (7 lengths, 7 angles, and 3 area factors) were selected. The slope of the S-N curve using Basquin model from the S-N curve and the safe fatigue strength were selected as output variables for prediction to develop the regression model. Multiple linear regression models, multiple non-linear regression models, and second-order polynomial regression models were proposed to predict fatigue properties. Backward elimination was applied to simplify the models and reduce overfitting. Among the three proposed regression models, the multiple non-linear regression model had a coefficient of determination greater than 0.86. In lap-welded joints with gaps, the weld geometry factors representing fatigue properties were identified through standardized regression coefficients, and four weld geometry factors related to stress concentration were proposed.

The extensive utilization of rebar during the construction of the project generates immense rebar waste, leading to increased construction costs and significant greenhouse gas emissions. Various approaches have been explored focusing on the minimization of rebar-cutting waste, such as optimizing cutting patterns, lap splice position, and special-length rebar utilization. Nonetheless, reducing rebar usage by minimizing the number of splices remains uninvestigated. In response to these issues, a two-stage optimization algorithm was developed, prioritizing special-length rebar to achieve near-zero rebar-cutting waste of less than 1% and concurrently reduce rebar usage, thereby promoting sustainable construction practices. The two-stage algorithm presented in this study involves the optimization of the lap splice position for the continuous rebar with a reduction of splices number. Furthermore, it integrates a special-length minimization algorithm for the additional rebars. Applying the algorithm to beam structures in a small-sized factory building project led to a notable reduction of 29.624 tons of rebar, equivalent to 12.31% of the total purchased quantity. Greenhouse gas emissions were reduced by 102.68 tons, and associated costs decreased by USD 30,256. A rebar-cutting waste of 0.99% which is near zero was achieved. These findings highlight the significant potential of the proposed algorithm in reducing rebar waste and facilitating sustainable construction practices. In addition, the application of the proposed algorithm in reinforced concrete construction projects will amplify the associated advantages accordingly.

Direct yaw moment control (DYC) is an effective way to alter the behaviour of electric cars with independent drives. Controlling the torque applied to each wheel can improve the handling performance of a vehicle making it safer andfaster on a race track. The state-of-the-art literature covers the comparison of various controllers (PID, LPV, LQR, SMC, etc.) using ISO manoeuvres. However, more advanced comparison on important characteristics of the controllers performance is missed, such as the robustness of the controllers under changes in the vehicle model, steering behaviour, use of the friction circle and, ultimately, lap time on a track. In this study, we have compared the controllers according to some of the aforementioned parameters on a modelled race car. Interestingly, best lap times are not provided by perfect neutral or close-to-neutral behaviour of the vehicle, but rather by allowing certain deviations from the target yaw rate. In addition, a modified PID controller showed that its performance is comparable to other more complex control techniques such as MPC.

Polymers synthesized with end-of-life consideration allow for recovery and reprocessing. ”Living-anionic polymerization (LAP)” and hydrosilylation reaction were utilized to synthesize hair-end furan functionalized hairy nanoparticles (HNPs) with hard polystyrene (PS) core and soft polydimethylsiloxane (PDMS) via a one-pot approach. The synthesis was carried out by first preparing the living core through cross-linking styrene with divinylbenzene using sec-butyl lithium, followed by the addition of the hexamethylcyclotrisiloxane (D3) monomer to the living core. The living polymer was terminated by dimethylchlorosilane to obtain the HNPs with Si-H functional end groups. The furan functionalization was carried out by the hydrosilylation reaction between Si-H of the functionalized HNP and 2-vinyl furan. Additionally, furan functionalized polystyrene (PS) and polydimethylsiloxane (PDMS) were also synthesized by LAP. 1H NMR and ATR-IR spectra confirm the successful synthesis of the target polymers. Differential scanning calorimetry shows two glass transition temperatures indicative of a poly(dimethylsiloxane) soft phase and a poly(styrene) hard phase, suggesting that the HNPs are microphase separated. The furan functionalized HNPs form thermo-reversible networks upon cross-linking with bismaleimide (BMI) via a Diels−Alder coupling reaction. The kinetics of the forward Diels–Alder reaction between the functionalized polymer and BMI were studied at three different temperatures: 50 °, 60 °, and 70 °C by UV-Vis spectroscopy. The activation energy for the furan functionalized HNPs reaction with the bismaleimide was lower compared to the furan functionalized polystyrene and poly(dimethylsiloxane) linear polymers. The crosslinked polymer network formed from the Diels−Alder forward reaction dissociates at around 140 °–154 °C, and the HNPs are recovered. The recovered HNPs can be re-crosslinked at 50 °C. The results suggest that furan functionalized HNPs are promising building blocks for preparing thermo-reversible elastomeric networks.