International Journal of Material Forming最新文献

Additive manufacturing (AM) has emerged as a commonly utilized technique in the manufacturing process of a wide range of materials. Recent advances in AM technology provide precise control over processing parameters, enabling the creation of complex geometries and enhancing the quality of the final product. Moreover, Machine Learning (ML) has become widely used to make systems work better by using materials and processes more intelligently and controlling their resulting properties. In industrial settings, implementing ML not only reduces the lead time of manufacturing processes but also enhances the quality and properties of produced parts through optimization of process parameters. Also, ML techniques have facilitated the advancement of cyber manufacturing in AM systems, thereby revolutionizing Industry 4.0. The current review explores the application of ML techniques across different aspects of AM including material and technology selection, optimization and control of process parameters, defect detection, and evaluation of properties results in the printed objects, as well as integration with Industry 4.0 paradigms. The progressive phases of utilizing ML in the context of AM, including data gathering, data preparation, feature engineering, model selection, training, and validation, have been discussed. Finally, certain challenges associated with the use of ML in the AM and some of the best-practice solutions have been presented.

Knowledge of the deformation behaviour of Ti-6Al-4V using single-point micro incremental forming (SPMIF) is very important to understand the physics behind the microstructural changes, and forming limit. In SPIF, shape changes in sheet metals up to ultra-thin sizes can be performed without using a die and punch (does not require any specific tooling as in the conventional forming process) and hence, this process is recommended for the fabrication of parts in the aerospace, automobile, and bio-medical industries. Furthermore, in SPIF, the components are manufactured using a hemispherical end tool moving along a predefined path with an enhanced forming limit. The present research work has focused on studying the formability, microstructure, mechanical properties and fracture mechanics of Ti–6Al–4V alloy foils during SPMIF. The importance of spindle speed on the forming limits of the Ti–6Al–4V alloy foil was studied and it was found that the maximum forming limits were achieved at higher spindle speeds (200 rpm) due to strengthening of basal texture and weakening of prismatic texture components. A forming limit strain (FLS) was drawn at different spindle speeds (100, 150, and 200 rpm). XRD, EBSD and TEM analyses were performed for the phase analysis, orientation and dislocation density respectively. The fracture behaviour was investigated and the void coalescence parameters were compared with respect to spindle speed.

The morphology and distribution structure of the reinforcement have a significant effect on the mechanical properties and cutting process of composites. In this paper, two-dimensional tensile and cutting models are established respectively, and the validity of the models is verified by comparing with the cutting force and chip morphology in the experiment. The effects of network reinforcement distribution and particle aspect ratio on the mechanical properties and surface quality of SiCp/Al composites were compared and analyzed. Results show that the aggregation of particles can enhance their continuity, thereby improving their bearing capacity. Network structures and the increase in aspect ratios of particles both can enhance the degree of aggregation of particles, thereby improving their bearing capacity. However, an increase in the degree of aggregation of particles will also lead to an increase in cutting forces and a deterioration in surface quality. The hybrid network structure of particles with different aspect ratios was proposed. Compared with the ellipse particle reinforced network composites model, the network structure with hybrid particles improved the surface quality by 41.7%, while ensures the strengthening effect of the composite material.

Dual-phase (DP) steels are widely used in the automotive industry due to their exceptional performance. It offers excellent strength, ductility, formability, and weldability. However, there is a high risk of edge cracking, particularly in materials like DP1000 steel, caused by residual damage from blanking, such as microcracks and burrs, which needs further investigation. In this study, the transformative potential of laser-polishing on DP1000 steel was investigated. The goal was to reduce edge crack sensitivity and enhance edge formability. In this work, laser-polished samples produced by various pre-manufacturing techniques such as sawing, punching, and waterjet cutting were examined. Various evaluations were performed on laser-polished samples. Those included white-light-confocal microscopy, scanning electron microscopy, and Electron Backscatter Diffraction (EBSD) analysis. Those evaluations aimed to analyze the microstructural transformation, surface roughness, and micro grain size distribution resulting from laser-polishing. Laser-polishing is a process in which the edge of the sample is remelted locally. Hence, residual damage vanishes, and surface defects disappear, which should be beneficial for edge formability. On the other hand, the cooling rate during re-solidification is high, leading to high strength and reduced ductility compared to the initial DP steel. Therefore, hole expansion tests were conducted to evaluate the edge formability of the steel. The results indicated a significant improvement in the hole expansion ratio of the laser-polished samples compared to samples with conventional manufactured edges. These findings will help to assess the advantages and limitations of laser-polishing in sheet material manufacturing.

In this study, the influence of the surface effect on the bending behavior of ultra-thin austenitic stainless steel sheets was investigated. The presence of grains on the surface, which induces softening due to the absence of grain boundaries, can significantly impact the bending behavior. This phenomenon introduces errors in predicting bending behavior solely based on the tensile properties. To evaluate the strain-path dependent behaviors in bending, three-point bending experiments were performed on both unstretched and stretched austenitic stainless steel specimens with a thickness of 0.2 mm. To account for the distinct behavior of surface and inner grains, a surface layer model was developed, dividing the sheet thickness into surface and inner layers. Machine learning-based multi-objective optimization was used to calibrate the material parameters for each layer. The study examined the influence of the surface effect, thickness of the surface layer, and the choice of hardening model on the material behaviors. The findings revealed the important role played by the surface layer and highlighted the differences between the surface and inner layers. These results contribute to a better understanding of the bending behavior of ultra-thin austenitic stainless steel sheets, ultimately improving the accuracy of bending force predictions in engineering simulations.

Due to their biodegradable properties, magnesium- and zinc-based alloys are in the focus of interest for numerous medical applications, e.g. in the form of thin wires. To achieve improved processability by using hot forming and to obtain higher diameter reductions per pass, the dieless wire drawing process is presented in this paper. In order to investigate the processability and the resulting mechanical properties, a selection of magnesium- and zinc-alloys as well as process parameters are chosen, and wire manufacturing is carried out using the dieless drawing process. The resulting process windows and mechanical properties for the selected materials are discussed. It is found that the length of the forming zone is an important indicator for the process window and the cross-sectional area reduction accuracy in the dieless wire drawing process. Furthermore, process parameter variations result in a distinct variation of the mechanical properties of the wires, whereas process temperatures close to the wire extrusion temperature result in mechanical properties similar to the as-extruded wires. Good localization of the deformation is found for forming zones of 25–75 mm length at elevated temperatures and cross-sectional area reductions of up to 30% are possible for Z1 and ZX10 in one drawing step.

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Intelligent monitoring and maintenance protocols are undoubtedly crucial for improving manufacturing processes. Accordingly, machine learning techniques and predictive control models have been customized and optimized to account for the specific characteristics of the processes under investigation. In this context, the management of manufacturing processes in a “smart way” requires the development of specific models based on input-output empirical data. The aim of the proposed research was to develop an easily customizable application integrated into a milling process executed at the laboratory level. The application was designed to identify and record the operator, the order and the specific work sequences. It also supports the operator in setting processing parameters according to the type of work sequence to be performed. The application analyses specific process outputs, such as the wear growth on the inserts of the cutter in relation to the main input process parameters: depth of cut, feed rate, and spindle speed. This analysis is implemented by leveraging empirical evidence.

Sheet metal wrinkles-free forming method is a novel sheet metal forming technology which deforms a blank to the target part using low-melting point alloy (LMA) without the blank holder, increasing materials utilization rate and reducing the trimming process. The LMA medium used as a blank holder inhibiting wrinkles transfers forming load onto the surface of sheet metal, deforming and flowing as the deformation of the sheet metal, which affects the plastic flow state of the sheet metal. This paper aims to investigate the effect of the rheological behavior of LMA on the wall thickness distribution of sheet metal, combining  numerical simulation and experiments. The stress–strain relationship is obtained by high-temperature tensile test of LMA under different temperature conditions, and numerical modeling of sheet metal forming with LMA medium is established by ABAQUS software. The influence of the process parameters on the surface pressure distribution of the sheet metal is investigated. The effect of forming temperature and medium layer thickness on the thinning is analyzed for revealing the intrinsic correlation of the surface pressure distribution and the thinning. Furthermore, the research on influence of the LMA medium rheological behavior on the distribution of the wall thickness of the formed part is performed. The experimental results are consistent with the numerical simulation results.

The thermal condition plays an important role in the final thickness distribution and in the mechanical behavior of the Polyethylene Terephthalate (PET) bottle obtained from the stretch blow molding (SBM) process. A complete 3D modelling of the heating stage during the SBM process under industrial condition is very time-consuming. Based on a simplified approach to quickly achieve the numerical simulation of the preform heating, an optimization procedure is proposed to adjust the settings of the infrared lamps by comparing our simulation results to the target temperature profile. In this numerical approach, the radiation source is simulated by using a model for intensity of the incident radiation and the Beer-Lambert law. On the other hand, the ventilation effect under industrial conditions is taken into account by modelling the forced convection around a cylinder. The infrared (IR) flux and ventilation effects are implemented as thermal boundary conditions in COMSOL software for a 3D computation of the thermal problem for the preform only. Since the simulation has a very reasonable computational time, an optimization procedure can be generated to adjust the setting of IR lamps. This optimization tool provides quickly a first set of parameters to help industry to obtain the desired temperature profile.

Blood vessels are essential as they transport oxygen and nutrients. To address the increasing mortality rate from cardiovascular diseases, modern science is focusing on clinical trials for replacing human blood vessels with artificial ones. However, the challenge lies in replicating the intricate anatomy with exact dimensional accuracy on a small scale. This work concentrates on developing innovative fabrication solutions in tissue engineering 3D scaffolds. The study captured two prototypes; one based on traditional manufacturing and the other applied an additive manufacturing principle. Once single-layered construct were manufactured, the results were evaluated in terms of dimensional accuracy measuring the constructs’ length, diameter and thickness. Additional tests were performed for finding the strain at break by applying manual strain-induced method. The samples demonstrated that molding excelled in terms of precision however, the mechanical performance did not meet the ISO 7198 standard. Additive manufacturing approach on the other hand, fully satisfied the structural criteria yet the obtained thickness significantly varied from the given one. Furthermore, efforts were made for fabricating three-layered scaffolds and while AM approach brought preferable results, difficulties were faced with molding. Thus, the importance of this work lies in demonstrating the process capabilities of two methods. The results indicate that while AM is suitable for fabricating multilayered constructs with good structural integrity, molding appears promising for small diameter scaffolds, as it can reduce the anatomical mismatches. Therefore, future work will focus on improving the limitations of these methods for developing three-layered vascular grafts within the admissible dimensional and mechanical criteria.