Feedforward control to suppress the leading edge bulge in photopolymerization-based additive manufacturing

Purpose This study aims to examine the feasibility of feedforward actuation of the recoater blade position to alleviate the resin surface non-uniformity while moving over deep-to-shallow transitions of submerged (already cured) geometric features. Design/methodology/approach A two-dimensional computational fluid dynamics (CFD) model has been used to determine optimized blade actuation protocols to minimize the resin surface non-uniformity. An experimental setup has been designed to validate the feasibility of the proposed protocol in practice. Findings A developed protocol for the blade height actuation is applied to a rectangular stair-like configuration of the underlying part geometry. The evaluation of the actuation protocol revealed the importance of two physical length scales, the capillary length and the size of the flow recirculation cell below in the liquid resin layer below the blade. They determine, together with the length scales defining the topography (horizontal extent and depth), the optimal blade trajectory. This protocol has also shown its efficiency for application to more complicated shapes (and, potentially, for any arbitrary geometry). Practical implications This study shows that incorporation of a feedforward control scheme in the recoating system might significantly reduce (by up to 80%) the surface unevenness. Moreover, this improvement of performances does not require major modifications of the existing architecture. Originality/value The results presented in this work demonstrate the benefits of the integration of the feedforward control to minimize the leading edge bulges over underlying part geometries in stereolithography.

Residual Stresses Control in Additive Manufacturing

Residual stresses are one of the primary causes for the failure of parts or systems in metal additive manufacturing (AM), since they easily induce crack propagation and structural distortion. Although the formation of residual stresses has been extensively studied, the core factors steering their development in AM have not been completely uncovered. To date, several strategies based on reducing the thermal gradients have been developed to mitigate the manifestation of residual stresses in AM; however, how to choose the optimal processing plan is still unclear for AM designers. In this regard, the concept of the yield temperature, related to the thermal deformation and the mechanical constraint, plays a crucial role for controlling the residual stresses, but it has not been duly investigated, and the corresponding approach to control stresses is also yet lacking. To undertake such study, a three-bar model is firstly used to illustrate the formation mechanism of the residual stress and its key causes. Next, an experimentally calibrated thermomechanical finite element model is used to analyze the sensitivity of the residual stresses to the scan pattern, preheating, energy density, and the part geometry and size, as well as the substrate constraints. Based on the numerical results obtained from this analysis, recommendations on how to minimize the residual stresses during the AM process are provided.

Microstructural Evolution Of LPBF AlSiMg – Effect Of Water Quenching Vs Furnace Cooling Vs Direct Aging

The heat treatment response of AlSi10Mg via laser powder bed fusion (LPBF) has been studied via detailed microstructural characterization. The effect of solutioning (S) and water quenching (WQ) vs furnace cooling (FC), and direct aging (DA) vs solutioning and aging (SA), has been analysed, for microstructure and tensile properties. 11 heat treatments were carried out to map the partitioning of Si, starting with stress relieving at 200 °C vs 300 °C, followed by solution heat treatment at 430°C vs 530 °C, water quenching vs furnace cooling, aging at 160 °C vs direct aging at 160 °C, to establish the microstructure of LPBF AlSi10Mg alloys for potential applications. The microstructure at 430 °C and 530 °C shows Si precipitate fractions of 25% and 14%, respectively. Room temperature mechanical properties, revealed the 300 °C, 2 h stress relieved sample with the highest strength and ductility (YS of 230 MPa and 16%). At 430 °C, both water quenching and furnace cooling showed similar strengths and 16% elongation, while at 530 °C, there was a much lower elongation (8–9%) with the T6 (53 °C, WQ, SA) showing higher strength and elongation. This study brings out the importance of being able to choose the heat treatments suitable to AlSiMg part geometry, via LPBF additive manufacturing for various applications.

A Computational Model of Melt Pool Morphology for Selective Laser Melting Process

Selective laser melting (SLM) is a promising metal additive manufacturing technology, which holds widespread applications in numerous fields. Unfortunately, it is arduous to predict the real SLM part geometry, which impedes its further development. While the morphology of melt pool, influenced and determined by process parameters, poses a crucial influence on the overall part geometry. Nonetheless, the association between process parameters and melt pool morphology is still unclear. Hence it is indispensable to explore relevant solution to address this issue. For this purpose, this paper proposes a new model to directly establish the mathematical relationship between process parameters and melt pool structure for SLM process. In this model, the status of melt pool is first qualitatively analyzed via the defined synthetic process index, and three types of melting states are differentiated including low melting, intermediate melting and high melting, which could cover different melt pool modes. Then, the computational model involving more physical mechanisms integrating mass conversion, heat exchange and temperature field is constructed. Melt pool critical geometries including the height, width, depth and length could be computed through the model. In order to validate the correctness of the proposed model, published experimental observations and existing models are compared. Calculation results from the proposed model show high consistency with the experimental samples and better accuracy than existing empirical models. Its applicability in melt pool classification and prediction is also verified, laying foundation for geometric simulation of SLM object which is successively shaped melt-pool by melt-pool.

Development of Calibration Knives for Hot Cutting Shears

In this paper, the problem distortion the end of a cylindrical workpiece in the cutting process on hot shears before being fed to the piercing mill is considered. To solve this problem, a new calibration of knives has been developed, and a finite element model hot cutting shears has been developed using the DEFORM-3D computer simulation software package. Modeling the cutting process using the proposed calibration has been carried out. It was found that the knife calibers geometry plays an important role in the formation end part geometry of the blank. Based on the simulation results, it was concluded that proposed calibration reduces the ovality workpiece end by almost 2 times in comparison with current calibration. Recommendations have been developed for the production of changes in the existing technology for cutting pipe billets.

Autonomous 3D geometry reconstruction through robot-manipulated optical sensors

Many industrial sectors face increasing production demands and the need to reduce costs, without compromising the quality. The use of robotics and automation has grown significantly in recent years, but versatile robotic manipulators are still not commonly used in small factories. Beside of the investments required to enable efficient and profitable use of robot technology, the efforts needed to program robots are only economically viable in case of large lot sizes. Generating robot programs for specific manufacturing tasks still relies on programming trajectory waypoints by hand. The use of virtual simulation software and the availability of the specimen digital models can facilitate robot programming. Nevertheless, in many cases, the virtual models are not available or there are excessive differences between virtual and real setups, leading to inaccurate robot programs and time-consuming manual corrections. Previous works have demonstrated the use of robot-manipulated optical sensors to map the geometry of samples. However, the use of simple user-defined robot paths, which are not optimized for a specific part geometry, typically causes some areas of the samples to not be mapped with the required level of accuracy or to not be sampled at all by the optical sensor. This work presents an autonomous framework to enable adaptive surface mapping, without any previous knowledge of the part geometry being transferred to the system. The novelty of this work lies in enabling the capability of mapping a part surface at the required level of sampling density, whilst minimizing the number of necessary view poses. Its development has also led to an efficient method of point cloud down-sampling and merging. The article gives an overview of the related work in the field, a detailed description of the proposed framework and a proof of its functionality through both simulated and experimental evidences.

Optimisation of Mould Design for Injection Moulding – Numerical Approach

Computer simulation of injection moulding process is a powerful tool for optimisation of moulded part geometry, mould design and processing parameters. One of the most frequent faults of the injection moulded parts is their warpage, which is a result of uneven cooling conditions in the mould cavity as well as after part ejection from the mould and cooling down to the environmental temperature. With computer simulation of the injection moulding process it is possible to predict potential areas of moulded part warpage and to apply the remedies to compensate/minimize the value of the moulded part warpage. The paper presents application of simulation software Moldex 3D in the process of optimising mould design for injection moulding of thermoplastic casing.

Machine Learning for Warpage Prediction of Fused Deposition Modelling Processed Parts

This paper provides a methodology for the application of a machine learning-based framework for fused deposition modelling manufacturing. The approach was developed to take into account the influence of the material, the part geometry, the process parameters on the maximum part warpage defined by the user. The results showed the effectiveness of machine learning for both classification and regression purposes so that the printability of the part is firstly provided, based on the selected warpage threshold, and secondly, the part warpage can be predicted within the problem design space variables, i.e. part material, part height, part length, and layer thickness. The limitations of the use of the analytic equation as a data-points generator are widely discussed, along with the future research based on the obtained preliminary results. In conclusion, the described methodology represents a concrete step towards a first-time-right strategy in the field of manufacturing processes.