CIRP Journal of Manufacturing Science and Technology最新文献

In machining, tool temperatures and thus tool wear are significantly influenced by frictional behaviour. Friction tests are used to determine the friction coefficient depending on relative speed, which serves as basis for parameterising friction models as input data for chip formation simulations. Therefore, this paper represents investigations towards the frictional behaviour of uncoated and coated (TiN, TiAlN) carbide tools when using two different relative movements (translational and rotary) and cooling lubricant conditions. In dry conditions, the investigations show insignificant influence of different engagement surfaces and testing kinematics on resulting friction. In lubricated conditions, three different friction coefficient sections were observed.

Grinding is one of the most widely employed machining processes in manufacturing. Achieving a successful grinding process characterized by low fault rates and short cycle times can significantly improve overall productivity and process efficiency. Nonetheless, finding optimal values for grinding process parameters is challenging due to complex underlying dynamics. Therefore, this work proposes a data-driven system that exploits various machine learning techniques and metaheuristic optimization algorithms to optimize grinding process parameters. Using data collected from grinding processes, the proposed system constructs a machine learning-based fault detection model and employs that model to define variable range constraints. In addition, a Gaussian process-based cycle time estimation model is developed. Process parameter optimization is performed using various metaheuristic algorithms based on the aforementioned methods. Experiments with actual internal cylindrical grinding process data have proven the proposed system’s effectiveness during process parameter optimization. Furthermore, real-world validation data verifies the final optimization solution, reducing the fault rate and process cycle time by 77.83% and 17.64%, respectively. In-depth interviews with six domain experts in the grinding process also verify the proposed system’s validity and real-world applicability. The proposed data-driven system is expected to bring substantial improvements in process productivity, especially when applied to manufacturing sites in practice.

Zirconia is a highly biodegradable ceramic material with excellent fracture resistance, in biomedical engineering, particularly dental implants. This research work has been focused on optimizing the quality of micro-hole generation in zirconia ceramic through ultrasonic micromachining (USMM). Three key process parameters such as abrasive slurry concentration, tool feed rate, and power rating are considered in this research work. The material removal rate, overcut, and taper angle are considered as responses. Response surface methodology has been employed for modeling during the USMM process, and a mathematical model has been developed to understand material removal mechanisms. Finite element analysis has been utilized to provide insights into the impacting process for industry requirements. A 3D model has been created to perform dynamic analysis under practical conditions. Multi-objective optimization has been applied to achieve optimum material removal rate (MRR), overcut, and taper angle. From multi-objective optimization, a slurry concentration of 49.59% g/l, tool feed rate of 1.16 mm/min, and power rating of 386.87 W has been found and in this parameter settings maximum MRR of 0.5333 mm³/min, minimum taper angle of 0.3428 degrees, and minimum overcut of 36.64 µm has been obtained during machining of ZrO₂ ceramics.

Machining is a process that involves intense heat generation at localized points within the tool-chip interface. This leads to elevated temperatures, which can be detrimental to cutting tools. This issue becomes even more crucial when dealing with superalloys like Inconel 718, as they exhibit high shear strength and good creep resistance. Consequently, a significant amount of energy is expended, increasing the cutting temperature. Until now, the primary technique employed to address this issue has been using Cutting Fluids (CFs). In machining, a portion of costs is attributed to fluid handling. It also contains harmful elements that can pose health risks, potentially leading to conditions such as cancer. Moreover, the toxic components can contribute to environmental degradation when improperly disposed of. Therefore, this study proposes an innovative cooling technique called Internally Cooled Tools (ICTs). The ICTs employ an internally circulating coolant fluid through closed cooling channels within the cutting tools, eliminating fluid dispersion into the atmosphere. The main objective was to compare the performance of ICTs in controlling the tool-chip interface temperature during Inconel 718 turning using hard metal tools. For this purpose, a complete factorial experimental design (2⁵) was utilized, with the response variable being the temperature measured by the tool-work thermocouples technique. Beyond that, a sustainable assessment was performed using the Pugh Matrix method. Many key sustainable factors were evaluated related to three atmospheres, cutting fluids in abundance – CFA, dry machining, and ICT. The data base used was a depth literature investigation together with results found in this work. The main findings of this entire work demonstrated that an increase in cutting parameters corresponded to an increase in temperature, as anticipated. TiNAl coating reduced the temperature by up to 10% compared to uncoated tools. Similarly, applying ICTs led to temperature reductions of up to 17% compared to dry machining conditions. The Pugh Matrix made considering 12 factors showed that ICT (14 points) was the most sustainable lubri-cooling method in comparison to CFA (3) and DM (5). Ultimately, ICTs showed to be a promising eco-friendly method. It outperformed conventional methods, showcasing a remarkable heat dissipation capacity. As a result, further studies are warranted to delve deeper into this promising approach.

Modern cyber–physical production systems provide advanced solutions to enhance factory throughput and efficiency. However, monitoring its behaviour and performance becomes challenging as the complexity of a manufacturing system increases. Artificial Intelligence (AI) provides techniques to manage not only decision-making tasks but also to support monitoring. The integration of AI into a factory can be facilitated by a reliable Digital Twin (DT) that enables knowledge-based and data-driven approaches. While computer vision and convolutional neural networks (CNNs) are crucial for monitoring production systems, the need for extensive training data hinders their adoption in real factories. The proposed methodology leverages the Digital Twin of a factory to generate labelled synthetic data for training CNN-based object detection models. Regarding their position and state, the focus is on monitoring entities in manufacturing systems, such as parts, components, fixtures, and tools. This approach reduces the need for large training datasets and enables training when the actual system is unavailable. The trained CNN model is evaluated in various scenarios, with a real case study involving an industrial pilot plant for repairing and recycling Printed Circuit Boards (PCBs).

Glow discharge polymer (GDP) is the unique artificial material for the target balls in Inertial Confinement Fusion tests, while its practical micromachining such as the fabrication of microstructure on material surfaces keeps challenging due to its unclear micro-cutting mechanism. Hence, this paper seeks to investigate the micro-cutting mechanism of GDP from the perspective of the material flow and cutting energy. To achieve it, the energy conservation equation of three cutting modes triggered by different ratios of uncut chip thickness to the tool cutting edge radius (RTS) was established based on cutting deformation behaviors. Meanwhile, the diamond cutting tests and the FEM simulation at different RTS were developed. The experimental observation of cutting forces and specific cutting forces verified the evolution of three cutting modes, including shearing, shearing-ploughing and ploughing in the micro-cutting of GDP with the decrease of RTS. Next, from the change of the node displacement vector observed from the simulated results, it can be seen that the real-time material flow behavior during micro-cutting of GDP varies obviously with the evolution of cutting modes. Besides, the fracture toughness G_c, and the energy dissipation of different cutting modes were analyzed. The proportion of the energy spent on material fracture (G_c=9.95 N/mm) is the largest one in shearing and shearing-ploughing modes, while in ploughing mode, the material plastic deformation consumed the most energy. The above results reveal the specific material properties and removal behaviors of GDP and contribute to optimizing the machining strategies for the practical micromachining of microstructures on material surfaces.

Braids can be found in structural composite parts used in the car and the aerospace industries. The architecture of the braid has a direct impact on its mechanical properties. However, traditional braiding machines are limited to a single braid architecture. Few braiding machines, so called “3D braiding machines”, have been developed to enable variable carrier’s paths. This allows fabrication of several braid architectures using the same machine. These machines use a switching mechanism, which complexifies the machine and the braiding process. A new type of braiding machine was introduced in Assi et al. [1], which uses a “chain and sprocket” mechanism, allowing variable carrier’s paths without any switching mechanism. Nevertheless, the original chain and sprocket braiding machine cannot be used for vertical braiding. When placed in the vertical position, the carrier jam between the horngears due to the force of gravity. This limitation does not allow braiding over a mandrel or coupling the braiding machine with a pultrusion line. This paper presents design guidelines for enabling vertical braiding for the chain and sprocket braiding machine. A new carrier design is proposed as well as a new horngear design. Additionally, the carrier is fitted with a guiding foot and a track is machined into the braider’s bedplate. A functional prototype has been developed to validate the design. The design complexity has been assessed and compared to existing braiding machines. The design proposed in this paper remains 30% less complex compared to other vertical braiding machines enabling variable carrier’s paths.

Predicting the behavior and mechanical properties of 3D-printed parts is crucial for 3D printer users. This study conducted experimental investigations on Onyx 3D-printed parts to identify the most important printing parameters. These parameters were specimen positioning and the number of specimen walls. The experimental results indicated that specimens oriented in the XZ direction were 48% stiffer than those oriented in the XY direction and 54% stiffer than those oriented in the ZX direction. Additionally, the results demonstrated that walls significantly influenced the mechanical properties of specimens in the XY and XZ orientations but had no effect on those in the ZX orientation. The Young's modulus increased by 60% between a specimen with one wall and another with eight walls. This paper presents an analytical model for predicting mechanical properties based on the number of walls, with a prediction error ranging from 1% to 15%. Additionally, a numerical simulation approach was proposed to predict the mechanical behavior of parts. The numerical and experimental results comparison showed a 1% to 9% prediction error and a good correlation between numerical and experimental curves. These findings can be a valuable aid to engineers in the design of 3D printed mechanical concepts.

As a new type of metal sheet, titanium/iron composite sheets are crucial in various fields. However, in the forming of the curved shells of the titanium/iron composite sheets with small thickness-to-diameter ratios, sidewall wrinkling is extremely easy to form, and it is hard to control. In this paper, an experimental setup for hydro-mechanical deep drawing was designed, and sidewall wrinkling on the hemispherical shell with a flat bottom and 0.47% thickness-to-diameter ratio of titanium/iron composite sheets was effectively suppressed while avoiding splitting. Mechanical and numerical analyses were conducted to reveal the deformation mechanism, and the effect of chamber pressure was studied. The forming defects, thickness, and stress distributions were determined to reflect the deformation behavior. Results show the greater the chamber pressure, the more significant the effect of wrinkling suppression. When the chamber pressure reaches 20 MPa, the sidewall wrinkles disappear. Enough hydraulic pressure can produce a strong backward-bulging effect, which increases the wrinkling suppression pressure and improves plastic deformation instability. Moreover, the stress state of the sidewall changes under the action of hydraulic pressure, thereby changing the in-plane deformation behavior. Thus, the wrinkles already formed in the earlier stage of sidewall formation were adjusted after contact with the punch. The forming process has considerable potential to fabricate thin-shell components.

While modern 5-axis computer numerical control (CNC) systems offer enhanced design flexibility and reduced production time, the dimensional accuracy of the workpiece is significantly compromised by geometric errors, thermal deformations, cutting forces, tool wear, and fixture-related factors. In-situ sensing, in conjunction with machine learning (ML), has recently been implemented on edge devices to synchronously acquire and agilely analyze high-frequency and multifaceted data for the prediction of workpiece quality. However, limited edge computational resources and lack of interpretability in ML models obscure the understanding of key quality-influencing signals. This research introduces $\mathrm{InterpHD}$, a novel graph-based hyperdimensional computing framework that not only assesses workpiece quality in 5-axis CNC on edge, but also characterizes key signals vital for evaluating the quality from in-situ multichannel data. Specifically, a hierarchical graph structure is designed to represent the relationship between channels (e.g., spindle rotation, three linear axes movements, and the rotary A and C axes), parameters (e.g., torque, current, power, and tool speed), and the workpiece dimensional accuracy. Additionally, memory refinement, separability, and parameter significance are proposed to assess the interpretability of the framework. Experimental results on a hybrid 5-axis LASERTEC 65 DED CNC machine indicate that $\mathrm{InterpHD}$ not only achieves a 90.7% F1-Score in characterizing a 25.4 mm counterbore feature deviation but also surpasses other ML models with an F1-Score margin of up to 73.0%. The interpretability of the framework reveals that load and torque have 12 times greater impact than power and velocity feed forward for the characterization of geometrical dimensions. $\mathrm{InterpHD}$ offers the potential to facilitate causal discovery and provide insights into the relationships between process parameters and part quality in manufacturing.