International Journal of Advanced Manufacturing Technology最新文献

The objective of the study is to evaluate the performance of solid cellular structures in Polylactic Acid (PLA) by extrusion of material. The structures studied are Strut-Based, Triply Periodic Minimal Surfaces (TPMS) and Spinoidal. Impact tests allowed the identification of three categories of energy absorption (low, medium, high). The structures with lower deformation were subsequently subjected to cyclic impact tests, while the others were discarded from the analysis. Once the structures were deformed, they were immersed in a thermostat bath at 70 ºC, a temperature higher than the glass transition of PLA, necessary for the recovery of shape. TPMS structures display the best performance for high and medium impact energies, thanks to the presence of few internal defects. Spinoidal structures perform well at low impact energies but are less suitable for cyclic testing due to their geometric characteristics. Despite featuring the same density of TPMS structures, the strut based ones are not suitable for cyclic testing due to poor mechanical strength. The experimental findings are very promising as the best performing structures can be suitable for the fabrication of products with an increased life cycle, especially in the ever growing and flourishing market of technical items for impacts protection.

Abstract

Robotic force control is crucial for precise polishing and has a significant influence on the final effects. The blade has a free-form surface in space, and the curvature changes drastically, making traditional impedance control feedback untimely. To solve this problem, this paper proposes an adaptive impedance control method for blade polishing based on Kalman filter. The force data is denoised by Kalman filtering to obtain the real force data, then the data is gravity compensated to obtain the real polishing force. The method analyzes the influences of stiffness change and displacement change on the polishing force, and establishes a stiffness and displacement coupling compensation model. The method achieves timely feedback when the robot copes with unknown environmental stiffness changes. In addition, the Lyapunov function is applied to verify the stability of the method during implementation. Four processing conditions are simulated by using Matlab Simulink. The results indicate that the proposed method can provide faster response and higher force tracking accuracy by adjusting the reference position when the environment changes. In the experiment of polishing blade, the roughness is reduced to below Ra0.32 μm and fluctuation range of polishing force is within ±1 N. The force control method performance is significantly improved and the blade surface quality is effectively improved.

Cutting force is one of the most important physical quantities in the cutting process. Cutting force directly determines the generation of cutting heat and affects tool wear and machined surface quality. In this work, based on the geometric analysis of the turning tool, the cutting edge was discretized, and the local parameters of each cutting edge were calculated. According to the formation and assumption of brittle material chips, considering the energy dissipation in the process of chip formation, the cutting force of each cutting edge element was calculated. Then, the theoretical model of three-dimensional turning force of glass–ceramics was established by adding the forces contributed by all cutting edge elements. The change of tool geometry angle can lead to the change of local cutting parameters at each point on the cutting edge, thereby affecting the variation of cutting force. In order to evaluate the cutting force model, the turning experiment of fluormica glass–ceramics was carried out, and the influence of tool geometry angles (normal rake angle γ_n, tool nose radius r_ε, and tool cutting edge angle κr) on the cutting force was discussed. The predicted results are in good agreement with the measured results. This model can provide theoretical guidance for the efficient turning strategy of glass–ceramics.

We investigated the impact of die fillet shape, fillet size, die clearance, and die height on the microstamping of ultrathin 316L bipolar plates (BPPs) with stepped flow channels. Using the normalized Cockcroft–Latham damage fracture criterion combined with the response surface method, we developed an effective predictive model for the fracture behavior of ultrathin 316L BPPs. This model was employed to optimize the mold parameters. Numerical simulation results reveal that different fillet shapes—90° sector, irregular sector, ellipse, and parabola—significantly affect the formation of ultrathin 316L BPPs. Among these, the elliptical fillet shape yielded the best results. Further analysis indicated that increasing the radius of the die fillet while reducing the die height led to decreases in the stress, strain, thinning rate, and damage value of the BPPs. Conversely, the draft angle increased linearly. However, with varying die clearance, the stress, strain, thinning rate, and damage value of the BPPs initially decreased and then increased, while the draft angle continued to rise linearly. The optimized die parameters were identified using the damage prediction model: a fillet radius of 0.2 mm, clearance of 0.26 mm, height of 0.49 mm, and stepped height of 0.24 mm. The validity of these optimized parameters was confirmed experimentally.

Abstract

Collaborative robots, also known as cobots, are designed to work alongside humans in a shared workspace and provide assistance to them. With the rapid development of robotics and artificial intelligence in recent years, cobots have become faster, smarter, more accurate, and more dependable. They have found applications in a broad range of scenarios where humans require assistance, such as in the home, healthcare, and manufacturing. In manufacturing, in particular, collaborative robots combine the precision and strength of robots with the flexibility of human dexterity to replace or aid humans in highly repetitive or hazardous manufacturing tasks. However, human–robot interaction still needs improvement in terms of adaptability, decision making, and robustness to changing scenarios and uncertainty, especially in the context of continuous interaction with human operators. Collaborative robots and humans must establish an intuitive and understanding rapport to build a cooperative working relationship. Therefore, human–robot interaction is a crucial research problem in robotics. This paper provides a summary of the research on human–robot interaction over the past decade, with a focus on interaction methods in human–robot collaboration, environment perception, task allocation strategies, and scenarios for human–robot collaboration in manufacturing. Finally, the paper presents the primary research directions and challenges for the future development of collaborative robots.

Sintered gears manufactured through powder metallurgy technology contain residual porosity that can make them inadequate for high power supply. Crack propagation is significantly enhanced by both residual porosity and cyclical stresses involving the teeth. The use of densification processes can highly improve their performances, permitting the reduction of the residual porosity. Among the densification processes, the rolling assumes a key-role. The process permits the densification of the tooth flanks, the most stressed parts of the wheel. However, the performances of the rolled wheel depend on several process parameters, whose setup phase requires several efforts and many experiments. Finite element (FE) model can be a helpful tool, allowing a faster estimation of the process parameters, reducing waste and costs linked to the experimental tests. In this sense, FE modelling techniques discussed in literature only cover the simulation of spur gears densification process, since they consist of in-plane 2D finite elements. In this paper, different numerical modelling techniques, based on 2D finite elements, are proposed to simulate the densification process of spur gears and used to perform a tendency analysis to explore the effects of wheelbase reduction between the forming rollers on the material densification. Material densification appeared higher for reduced wheelbases, but an increasing cavity was observed at the tooth root as the wheelbases decreases. Moreover, a FE model based on 3D finite elements is proposed to reproduce numerically the rolling process of a helical gear. The accuracy of the 3D FE model was measured against the results provided by some experimental tests, herein discussed too. A good agreement between numerical and experimental results was observed.

The adequate filling and quenching of small corner features are major challenges in manufacturing complex-shaped boron steel tubular parts during the hot metal gas forming process. Considering that the tube forming process involves closed and invisible features, a single-sided die quenching experiment of boron steel sheets was proposed to simulate the in-die quenching process of tubes. The results confirmed that for a given sheet thickness, the critical size of the non-contact zone of achieving complete martensite transformation could be determined. A simple demonstrator geometry was designed to analyze the effects of bulging temperature and pressurizing rate on the corner filling, microstructure, and mechanical properties in the hot metal gas forming process, which are then correlated with the cooling rate in single-sided die quenching experiment. The corner filling was significantly improved with the increase in the bulging temperature and the pressurizing rate. At the bulging temperature of 900 °C, when the pressurizing rate increased from 1 to 3 MPa/s, the obtainable minimum corner radius decreased only from 24 to 16 mm. Under the above increased pressurizing rate, the width of the non-contact zone was 12.23 mm, which corresponded to a cooling rate that could reach 47 °C/s in the corner zone. The limited corner filling resulted from a significant temperature drop during hot metal gas forming. Decreasing the cooling rate of the tube or increasing the pressurizing rate can extend the range of reasonable process parameters in the boron steel tubes’ hot metal gas forming.

The direction in which wire and powder feedstock are fed influences deposit quality of as-built parts produced using laser-direct energy deposition (L-DED). While lateral wire feed has been explored in existing L-DED investigations, limitations like process instability persist, especially in achieving the required connection between the wire and the melt pool. Co-axial feedstock deposition offers a potential solution, enabling higher manufacturing flexibility and efficiency by co-axially feeding wire or powder. However, the full potential of L-DED using co-axial feeding for metal components remains underexplored due to equipment limitations. This study systematically evaluates the printability of stainless steel (SS) 316L and compares the microstructures and microhardness properties between co-axial powder-fed and wire-fed L-DED specimens. Utilizing the MELTIO M450 L-DED system in an argon environment, single-layer three-track specimens are produced with different combinations of process parameters. Comprehensive characterization, employing optical and scanning electron microscopy alongside microhardness testing, reveals powder-fed specimens exhibit greater melt pool depth and cooling rates, while wire-fed counterparts display fewer oxide inclusions and smoother surfaces. Microstructural differences include higher δ-ferrite content in wire-fed specimens. Microhardness values between powder-fed and wire-fed specimens are comparable. These findings hold implications for sequential powder and wire deposition, enabling the production of diverse mechanical structures with distinct characteristics. Overall, this paper provides an insight into feedstock selection for efficient metallic part production via co-axial feedstock deposition and recommends a range of process parameters suitable for fabricating SS316L parts using co-axial deposition in L-DED.

Abstract

The thermal error suppression rate depends on the cooling effect of the water cooling system, and the cooling water flow rate is a direct factor affecting the cooling effect. To better reduce the thermal error, a numerical model of cooling water is established to solve for the optimal cooling water flow rate. Firstly, a numerical model of thermal deformation of the pendulum angle milling head is established based on thermoelasticity theory to determine the main heat sources leading to thermal deformation. Then, a numerical analysis model of the cooling water flow rate is established to investigate the cooling water flow rate that has the best effect on the suppression of thermal errors. Finally, five flow rates are used for cooling experiments to verify the accuracy of the numerical model. The results show that the temperature of each measurement point increases with the flow rate from a significant decrease to the basic constant trend of gradual saturation. The reduction rate of thermal error at v=54 cm/s is as high as 73.4%, providing a theoretical basis for enterprises to optimize water cooling system parameters.

The transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system’s modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.