Manufacturing of advanced smart tooling for metal forming

Cao, Jian; Brinksmeier, E.; Fu, Ming; Gao, Robert X.; Liang, Biao; Merklein, Marion; Schmidt, Michael; Ye, Jun

doi:10.1016/j.cirp.2019.05.001

Cited by 101 publications

(32 citation statements)

References 200 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To ensure the consistent quality of the manufactured products, monitoring of the tools used in the stamping process is crucial. Therefore, monitoring data are required to clearly understand factors affecting the conditions of stamping tools [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network

Huang

Dzulfikri

2021

Sensors

View full text Add to dashboard Cite

Stamping is one of the most widely used processes in the sheet metalworking industry. Because of the increasing demand for a faster process, ensuring that the stamping process is conducted without compromising quality is crucial. The tool used in the stamping process is crucial to the efficiency of the process; therefore, effective monitoring of the tool health condition is essential for detecting stamping defects. In this study, vibration measurement was used to monitor the stamping process and tool health. A system was developed for capturing signals in the stamping process, and each stamping cycle was selected through template matching. A one-dimensional (1D) convolutional neural network (CNN) was developed to classify the tool wear condition. The results revealed that the 1D CNN architecture a yielded a high accuracy (>99%) and fast adaptability among different models.

show abstract

Section: Introductionmentioning

confidence: 99%

Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network

Huang

Dzulfikri

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…For one, it is desirable to reduce waste during the manufacturing of components; second, the lifetime and energy necessary to operate a component or system ought to be optimized. The first aspect can be addressed through the near net-shape manufacturing of components by 3D-printing, i.e., additive manufacturing (AM), and through the high degree of geometric freedom provided by respective techniques, eventually leading to additional weight reduction, e.g., through topology optimization [ 1 , 2 , 3 ]. The second aspect can be approached by better tribological practices.…”

Section: Introductionmentioning

confidence: 99%

Tribological Performance of Additively Manufactured AISI H13 Steel in Different Surface Conditions

et al. 2021

View full text Add to dashboard Cite

Additive manufacturing of metallic tribological components offers unprecedented degrees of freedom, but the surface roughness of most as-printed surfaces impedes the direct applicability of such structures, and postprocessing is necessary. Here, the tribological performance of AISI H13 steel samples was studied. These were additively manufactured through laser powder bed fusion (L-PBF), also referred to as selective laser melting (SLM). Samples were tested in four different surface conditions: as-printed, polished, ground and polished, and laser-surface-textured (LST) with round dimples. Friction experiments were conducted in a pin-on-disk fashion against bearing steel disks under lubrication with an additive-free mineral base oil for sliding speeds between 20 and 170 mm/s. Results demonstrated that, among the four surface treatments, grinding and polishing resulted in the lowest friction coefficient, followed by the as-printed state, while both polishing alone and laser-surface texturing increased the friction coefficient. Surprisingly, direct correlation between surface roughness and friction coefficient, i.e., the rougher the surface was, the higher the friction force, was not observed. Wear was minimal in all cases and below what could be detected by gravimetrical means. These results highlight the need for an adequate post-processing treatment of additively manufactured parts that are to be employed in tribological systems.

show abstract

“…The rapid development of micro-forming processes, including the micro-bending process, has focused attention on the requirements for fabricating micro parts with high dimensional accuracy and good forming quality [1,2]. With respect to the micro-bending process, springback, caused by the elastic recovery after releasing the load, is usually adopted to evaluate the dimensional accuracy and forming quality of micro-bent parts.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of micro W-bending process parameters using I-optimal design-based response surface methodology

et al. 2021

View full text Add to dashboard Cite

There is an increasingly recognised requirement for high dimensional accuracy in micro-bent parts. Springback has an important influence on dimensional accuracy and it is significantly influenced by various process parameters. In order to optimise process parameters and improve dimensional accuracy, an approach to quantify the influence of these parameters is proposed in this study. Experiments were conducted on a micro W-bending process by using an I-optimal design method, breaking through the limitations of the traditional methods of design of experiment (DOE). The mathematical model was established by response surface methodology (RSM). Statistical analysis indicated that the developed model was adequate to describe the relationship between process parameters and springback. It was also revealed that the foil thickness was the most significant parameter affecting the springback. Moreover, the foil thickness and grain size not only affected the dimensional accuracy, but also had noteworthy influence on the springback behaviour in the micro W-bending process. By applying the proposed model, the optimum process parameters to minimize springback and improve the dimensional accuracy were obtained. It is evident from this study that the I-optimal design-based RSM is a promising method for parameter optimisation and dimensional accuracy improvement in the micro-bending process.

show abstract

Manufacturing of advanced smart tooling for metal forming

Cited by 101 publications

References 200 publications

Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network

Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network

Tribological Performance of Additively Manufactured AISI H13 Steel in Different Surface Conditions

Optimisation of micro W-bending process parameters using I-optimal design-based response surface methodology

Contact Info

Product

Resources

About