Editor’s Choice Articles

Predicting the performances of a manufactured part is extremely important, especially for industries in which there is almost no room for uncertainties, such as aeronautical or automotive. Simulations performed by means of numerical methods such as Finite Element Methods represent a powerful instrument in achieving high level of predictability. However, some particular combinations of manufactured materials and manufacturing processes might lead to unfavorable conditions in which the classical mathematical models used to predict the behavior of the continuum are not anymore able to deliver predictions that are in good agreement with experimental evidence. Since the first evidences of the shortcomings of the classical model were highlighted, many non-classical continuum mechanics theories have been developed, and most of them introduce dependencies at different levels with the Plastic Strain Gradient. This manuscript aims at gathering the milestone contributions among the Strain Gradient Plasticity Theories developed so far, with the object of exploring the way they interface with the requirements posed by the challenges in simulating manufacturing operations. Finally, the most relevant examples of the applications of Strain Gradient Plasticity Theories for manufacturing simulations have been reported from literature. Full article

Shearing processes of high strength steel sheets increasingly applied to lightweight automobile parts were reviewed. With the increase in strength of the high strength steel sheets, shearing operations become hard. First, the sheared edge quality in shearing of high strength steel sheets and the effects on the formability and fatigue strength were shown. Next, ironing processes with a taper punch and a punched slug, a slight clearance punching with a punch having a small round corner and a thickening process of the sheared edge were explained as processes for improving the sheared edge quality. Finally, hydrogen-induced delayed fractures of cold-sheared ultra-high strength steel sheets and of hot-trimmed parts were evaluated. Full article

The increasing demand for complex and wear-resistant forming tools made of difficult-to-machine materials requires efficient manufacturing processes. In terms of high-strength materials; highly suitable processes such as micromilling are limited in their potential due to the increased tool loads and the resulting tool wear. This promotes hybrid manufacturing processes that offer approaches to increase the performance. In this paper; conduction-based thermally assisted micromilling using a prototype device to homogeneously heat the entire workpiece is investigated. By varying the workpiece temperature by 20 °C < T_W < 500 °C; a highly durable high-speed steel (HSS) AISI M3:2 (63 HRC) and a hot-work steel (HWS) AISI H11 (53 HRC) were machined using PVD-TiAlN coated micro-end milling tools (d = 1 mm). The influence of the workpiece temperature on central process conditions; such as tool wear and achievable surface quality; are determined. As expected; the temporary thermal softening of the materials leads to a reduction in the cutting forces and; thus; in the resulting tool wear for specific configurations of the thermal assistance. While only minor effects are detected regarding the surface topography; a significant reduction in the burr height is achieved. Full article

In conventional forming processes, quasi-static conditions are a good approximation and numerical process optimization is the state of the art in industrial practice. Nevertheless, there is still a substantial need for research in the field of identification of material parameters. In production technologies with high forming velocities, it is no longer acceptable to neglect the dependency of the hardening on the forming speed. Therefore, a method for determining material characteristics in processes with high forming speeds was developed by designing and implementing a test setup and an inverse parameter identification. Two acceleration concepts were realized: a pneumatically driven one and an electromagnetically driven one. The method was verified for a mild steel and an aluminum alloy proving that the identified material parameters allow numerical modeling of high-speed processes with good accuracy. The determined material parameters for steel show significant differences for different stress states. For specimen geometries with predominantly uniaxial tensile strain at forming speeds in the order of 10⁴–10⁵/s the determined yield stress was nearly twice as high compared to shear samples; an effect which does not occur under quasi-static loading. This trend suggests a triaxiality-dependent rate dependence, which might be attributed to shear band induced strain localization and adiabatic heating. Full article

Industry 4.0 requires phenomenon twins to functionalize the relevant systems (e.g., cyber-physical systems). A phenomenon twin means a computable virtual abstraction of a real phenomenon. In order to systematize the construction process of a phenomenon twin, this study proposes a system defined as the phenomenon twin construction system. It consists of three components, namely the input, processing, and output components. Among these components, the processing component is the most critical one that digitally models, simulates, and validates a given phenomenon extracting information from the input component. What kind of modeling, simulation, and validation approaches should be used while constructing the processing component for a given phenomenon is a research question. This study answers this question using the case of surface roughness—a complex phenomenon associated with all material removal processes. Accordingly, this study shows that for modeling the surface roughness of a machined surface, the approach called semantic modeling is more effective than the conventional approach called the Markov chain. It is also found that to validate whether or not a simulated surface roughness resembles the expected roughness, the outcomes of the possibility distribution-based computing and DNA-based computing are more effective than the outcomes of a conventional computing wherein the arithmetic mean height of surface roughness is calculated. Thus, apart from the conventional computing approaches, the leading edge computational intelligence-based approaches can digitize manufacturing processes more effectively. Full article

This paper presents the methodology and results of an extensive benchmarking of laser powder bed fusion (LPBF) machines conducted across five top machine producers and two end users. The objective was to understand the influence of the individual machine on the final quality of predesigned specimens, given a specific material and from multiple perspectives, in order to assess the current capabilities and limitations of the technology and compare them with the capabilities of an 11-year-old machine belonging to one of the end users participating in this investigation. The collected results give a clear representation of the status of LPBF technology considering its maturity in terms of process capabilities and potential applications in a production environment. Full article

Fused deposition modeling (FDM) is an additive manufacturing (AM) process that is often used to fabricate geometrically complex shaped prototypes and parts. It is gaining popularity as it reduces cycle time for product development without the need for expensive tools. However, the commercialization of FDM technology in various industrial applications is currently limited due to several shortcomings, such as insufficient mechanical properties, poor surface quality, and low dimensional accuracy. The qualities of FDM-produced products are affected by various process parameters, for example, layer thickness, build orientation, raster width, or print speed. The setting of process parameters and their range depends on the section of FDM machines. Filament materials, nozzle dimensions, and the type of machine determine the range of various parameters. The optimum setting of parameters is deemed to improve the qualities of three-dimensional (3D) printed parts and may reduce post-production work. This paper intensively reviews state-of-the-art literature on the influence of parameters on part qualities and the existing work on process parameter optimization. Additionally, the shortcomings of existing works are identified, challenges and opportunities to work in this field are evaluated, and directions for future research in this field are suggested. Full article

The quality and reliability in additive manufacturing is an emerging area. To ensure process quality and reliability, the influence of all process parameters and conditions needs to be understood. The product quality and reliability characteristics, i.e., dimensional accuracy, precision, repeatability, and reproducibility are mostly affected by inherent and systematic manufacturing process variations. This paper presents research on dimensional quality and distortion analysis of AlSi10Mg thin-walled parts developed by a selective laser melting technique. The input process parameters were fixed, and the impact of inherent process variation on dimensional accuracy and precision was studied. The process stability and variability were examined under repeatability and reproducibility conditions. The sample length (horizontal dimension) results revealed a 0.05 mm maximum dimensional error, 0.0197 mm repeatability, and 0.0169 mm reproducibility. Similarly, in sample height (vertical dimension) results, 0.258 mm maximum dimensional error, 0.0237 mm repeatability, and 0.0863 mm reproducibility were observed. The effect of varying design thickness on thickness accuracy was analyzed, and regression analysis performed. The maximum 0.038 mm error and 0.018 mm standard deviation was observed for the 1 mm thickness sample, which significantly decreased for sample thickness ≥2 mm. The % error decreased exponentially with increasing sample thickness. The distortion analysis was performed to explore the effect of sample thickness on part distortion. The 0.5 mm thickness sample shows a very high distortion comparatively, and it is reduced significantly for >0.5 mm thickness samples. The study is further extended to examine the effect of solution heat treatment and artificial aging on the accuracy, precision, and distortion; however, it did not improve the results. Conclusively, the sample dimensions, i.e., length and height, have shown fluctuations due to inherent process characteristics under repeatability and reproducibility conditions. The ANOVA results revealed that sample length means are not statistically significantly different, whereas sample height means are significantly different. The horizontal dimensions in the xy-plane have better accuracy and precision compared to the vertical dimension in the z-axis. The accuracy and precision increased, whereas part distortion decreased with increasing thickness. Full article

A wide variety of coating methods and materials are available for different coating applications with a common purpose of protecting a part or structure exposed to mechanical or chemical damage. A benefit of this protective function is to decrease manufacturing cost since fabrication of new parts is not needed. Available coating materials include hard and stiff metallic alloys, ceramics, bio-glasses, polymers, and engineered plastic materials, giving designers a variety freedom of choices for durable protection. To date, numerous processes such as physical/chemical vapor deposition, micro-arc oxidation, sol–gel, thermal spraying, and electrodeposition processes have been introduced and investigated. Although each of these processes provides advantages, there are always drawbacks limiting their application. However, there are many solutions to overcome deficiencies of coating techniques by using the benefits of each process in a multi-method coating. In this article, these coating methods are categorized, and compared. By developing more advanced coating techniques and materials it is possible to enhance the qualities of protection in the future. Full article

A simplified analytical model of the laser powder bed fusion (LPBF) process was used to develop a novel density prediction approach that can be adapted for any given powder feedstock and LPBF system. First, calibration coupons were built using IN625, Ti64 and Fe powders and a specific LPBF system. These coupons were manufactured using the predetermined ranges of laser power, scanning speed, hatching space, and layer thickness, and their densities were measured using conventional material characterization techniques. Next, a simplified melt pool model was used to calculate the melt pool dimensions for the selected sets of printing parameters. Both sets of data were then combined to predict the density of printed parts. This approach was additionally validated using the literature data on AlSi10Mg and 316L alloys, thus demonstrating that it can reliably be used to optimize the laser powder bed metal fusion process. Full article