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Today, the automotive industry faces its biggest challenge, which is keeping a constant evolution, accounting for the market needs in what comes to the enormous quantity of parts and systems that go hand in hand with the production of the vehicle, and at the same time, keep up with the quality requirements and deadlines. Throughout time, automation and robotics became the best way to achieve the goals required by the market. In the automotive industry, automation presents itself at a continuous evolution, constantly presenting new solutions, and the production and assembly lines increasingly resort to completely automated processes. This work is based on the needs of an automotive parts’ supplier. In which regards to the injection of plastic, the production is fluent and quick, and to take advantage of such production speed, it is desirable to execute the assembly steps immediately after the removal of the parts from the injection moulds. The proposed work consists of the design of a robotized cell with the goal of inserting metal clips in two different automotive parts after the injection process. The idea is to conceptualize and conceive a completely automated equipment, capable of executing this task using a six-axis robot. To secure the part for clip assembly, the equipment must be ready to ensure the positioning and locking of the part in all axes and, to lighten the robot’s job, it must also be capable of using a feeding system to load the robot’s tool. The results of this project were positive, the equipment was successfully concluded and delivered to the customer, meeting the construction requirements, and, most importantly, the cycle time.

3-dimension printing (3DP) or Additive Manufacturing promises rapid prototyping with 3D printers, the precondition for manufacturing complex components and surfaces. Usually, three-axis 3D printers are widely applied with planar slicing, limiting solid freeform fabrication with finite printing direction. Non-planar slicing (or curved layer slicing) has been put forward to break through the layerwise framework with infinite directions, requiring multi-axis 3D printers with more than four DOFs. However, as far as the authors know, the specific design method of 3D printers’ structure has seldom been studied for non-planar printing. Furthermore, there is no specified systematical research on the type synthesis of the 3D printer, which has two collaborative modules functioning together. One module serves as the build platform and the other as the print head attached moving platform. Hence, to obtain topological structures of the collaborative 3D printer for planar and non-planar printing, this paper investigates the type synthesis by introducing a research background at first. Then, the motion characteristics of the collaborative 3D printer are determined based on the planned paths’ geometric characteristics and motion requirements of the 3D printer. After that, General Function sets theory and the distribution principles are introduced, and the case study of 3D printers with 3T, 3T1R, and 3T2R is given. Finally, this research paper proposes a class of rotary 3D printers having 2T1R, 2T2R, and 2T3R innovatively. Furthermore, one prototype of the above case study is designed, and a preliminary experiment for planar and non-planar printing is carried out to verify the feasibility of the type synthesis’s result. In general, aiming at planar and non-planar printing, this paper presents a theory for type synthesis of collaborative 3D printers to offer a much richer set of kinematic structures than the most commonly used three Degree of Freedom printers.

Intelligent vehicles require the ability to switch freely between manual and automatic driving modes, which opens up more possibilities for steer-by-wire. Among them, the design of transmission ratio for both driving modes can improve the steering performance of vehicle. In the manual driving mode, this paper optimizes the yaw rate gain upon the particle swarm optimization (PSO) algorithm and designs a variable steering transmission ratio, which can reduce the driver’s burden and improve the vehicle’s handling stability. Further, the steering wheel angular rate information and fuzzy control are used to design the high-speed transmission ratio to ensure the safety of the vehicle in high-speed emergency situations. In the automatic driving mode, the transmission ratio values at various vehicle speeds are optimized offline based on the PSO algorithm, and a variable transmission ratio curve is designed to enhance the trajectory tracking ability and the vehicle stability. Finally, the hardware-in-the-loop test results show that the variable transmission ratio designed in the manual driving mode reduces the burden of the driver at low speed and improves the vehicle stability at high speed, especially in emergency situations. It also proves that the optimized design of variable ratio in the automatic driving mode promotes the path-following ability and the vehicle stability.

Three-dimensional printing (3D), a vital technological pillar of industry 4.0 suffers from an important bed size limitation, wherein it cannot print any part larger than its bed size. Unfortunately, research in this domain has not kept pace with the other limitations hindering the acceptability of fused deposition modeling (FDM)-3D printers. This paper investigates the adhesive joining of dissimilar 3D-printed parts made from usually preferred materials (Acrylonitrile Butadiene Styrene (ABS), Polylactic Acid (PLA)) having different geometric joint designs (lap, scarf, stepped) employing diverse adhesives (epoxy, cyanoacrylate, polyurathane-based) subjected to different surface treatments (sanding, vapor, plasma). The aim is two-fold: to enhance the joint strength making adhesive bonding a suitable solution for complex structural application, as well as to overcome the bed size limitation of commercially available 3D printers. The individual and combined effect of the parameters, prediction, and validation for tensile strength were realized by statistical tools (DOE and ANOVA). The results revealed the significance of the process parameters in the following order: material type, joint configuration, adhesive types, and surface pre-treatments. The preferred material turned out to be ABS + ABS, with stepped configuration, subjected to plasma treatment and bonded with Loctite adhesives giving a strong improvement in terms of performance. Finally, the model summary with R2value of 95.6% implied that the experimentation was successful and could be easily reproduced on an industrial scale helping to attain high strength-less weight components even with smaller FDM-3D printers.

As the core equipment of hydropower plants, the healthy condition of hydropower generating unit (HGU) plays a vital role in the safe and stable operation of hydropower plants. Therefore, it is of great significance to measure the vibration trend of HGU, which can effectively reflect the health condition of HGU, allowing the development of appropriate countermeasures to improve the safety and stability operation of HGU. Given this, a hybrid approach for measuring vibration signals of HGU coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), phase space reconstruction (PSR), kernel extreme learning machine (KELM) optimized by hybrid slime mold algorithm and Harris hawks optimization (HSMAHHO), and error correction with gate recurrent unit (GRU) network is proposed in this paper. Specifically, CEEMDAN is initially applied to decompose the raw vibration signals into several intrinsic mode functions (IMFs). Subsequently, PSR is adopted to convert each IMF into the input–output matrix of KELM for prediction. Meanwhile, HSMAHHO algorithm is utilized to optimize the critical parameters within KELM. Afterward, the predicted values of each IMF are superposed to obtain the predicted values of the raw vibration signals, and the error sequence to be corrected is constructed. Eventually, the error sequence is predicted by combining CEEMDAN, PSR, GRU and then summed up with the previous predicted values to get the final measuring result. In addition, the feasibility of the proposed hybrid approach is further verified by the experimental comparative analysis with seven comparative models. The experimental results demonstrate that (1) the proposed HSMAHHO algorithm could better optimize the internal parameters of KELM, which effectively improves the measuring results (2) the proposed error correction strategy could effectively enhance the measuring accuracy.

The performance of the milling head is affected by the machining and assembly process of the parts. The key to improving machining accuracy and reliability of swing angle milling head is to establish the precision prediction model. Therefore, a precision prediction model of swing angle milling head under the influence of multiple errors’ sources is built. The transmission and accumulation of machining and assembly errors along the assembly sequence path for the transmission system are carried out. All essential components on the assembly sequence path are considered. In addition, the secondary impact of the bearing seat hole is also taken into account. The source of the gear assembly errors is explained. The predicted range of transmission error based on the coupling relationship between assembly errors and transmission error is obtained. A precision experiment is carried out, and the result is within the predicted range. This research demonstrates that the errors' coupling relationship considered in this paper does exist and has an effect on transmission error, which provides a new idea for calculating transmission error of other gear systems.

In the last few years, Motor Current Signature Analysis (MCSA) has proven to be an effective method for electrical machines condition monitoring. Indeed, compared to vibration and temperature analysis, current measurement proves to be a convenient and non-invasive alternative. Moreover, it has proven to be a reliable method since many mechanical and electrical faults manifest as side-band spectral components generated around the fundamental frequency component of the motor’s current. These components are called interharmonics and they are a major focus of fault detection using MCSA. However, the main drawback of this approach is that the interference of other more prevalent components such as the fundamental and noise components can obstruct the effect of interharmonics in the spectrum and may therefore affect fault detection accuracy. Thus, we propose in this paper an alternative approach that aims to decompose the different current components using a model based on a Vandermonde matrix, in order to monitor each component independently. Then, the tracking of each distinct component in time and spectral domains is implemented. This is achieved by estimating their respective relevant parameters using the Gradient Descent algorithm. This method has been favorably compared to an existing estimation algorithm (MUSIC) and its efficiency has been validated. The results of this work prove to be promising and establish the parametric tracking of the electrical current components using the Gradient Descent algorithm as a reliable monitoring approach.