Powder Bed Selective Laser Processing of Alumina: Scanning Strategies Investigation

Powder Bed Selective Laser Processing (PBSLP) is a promising technique for the additive manufacturing of alumina. For the method’s success, PBSLP process parameters such as laser power, scanning speed, hatching distance, and scanning strategies need to be investigated. This paper focuses on studying the scanning strategies’ effects on the PBSLP of alumina numerically and experimentally. Scanning strategies such as linear with different orientation, concentric, and islands were investigated. A numerical model was developed in which the PBSLP parameters, scanning strategy effects, and interpreting the experimental results could be observed. The numerical model proved its ability to reach the proper process parameters instead of using experimental trails which are time and cost consuming. For relative density, the island strategy succeeded to print alumina samples with a high relative density reaching 87.8%. However, there are round passages formed inside the samples that remain a barrier for the island strategy to be effectively used in PBSLP of alumina. Both linear and concentric strategies achieved a relative density of 75% and 67%, respectively. Considering the top surface roughness, samples printed with linear strategies gave low top surface roughness compared to the island and concentric strategies. Linear-45° is considered the effective strategy among the studied strategies as it achieved good relative density and low roughness at top and side surfaces. For PBSLP of alumina, new scanning strategies should be considered, and this study presents a new scanning strategy that is mainly based on space filling mathematical curves and should be studied in future work.

Artificial neural network tools for predicting the functional response of ultrafast laser textured/structured surfaces

Artificial Neural Networks (ANNs) are well-established knowledge acquisition systems with proven capacity for learning and generalisation. Therefore, ANNs are widely applied to solve engineering problems and are often used in laser-based manufacturing applications. There are different pattern recognition and control problems where ANNs can be effectively applied, and one of them is laser structuring/texturing for surface functionalisation, e.g. in generating Laser-Induced Periodic Surface Structures (LIPSS). They are a particular type of sub-micron structures that are very sensitive to changes in laser processing conditions due to processing disturbances like varying Focal Offset Distance (FOD) and/or Beam Incident Angle (BIA) during the laser processing of 3D surfaces. As a result, the functional response of LIPSS-treated surfaces might be affected, too, and typically needs to be analysed with time-consuming experimental tests. Also, there is a lack of sufficient process monitoring and quality control tools available for LIPSS-treated surfaces that could identify processing patterns and interdependences. These tools are needed to determine whether the LIPSS generation process is in control and consequently whether the surface’s functional performance is still retained. In this research, an ANN-based approach is proposed for predicting the functional response of ultrafast laser structured/textured surfaces. It was demonstrated that the processing disturbances affecting the LIPSS treatments can be classified, and then, the surface response, namely wettability, of processed surfaces can be predicted with a very high accuracy using the developed ANN tools for pre- and post-processing of LIPSS topography data, i.e. their areal surface roughness parameters. A Generative Adversarial Network (GAN) was applied as a pre-processing tool to significantly reduce the number of required experimental data. The number of areal surface roughness parameters needed to fully characterise the functional response of a surface was minimised using a combination of feature selection methods. Based on statistical analysis and evolutionary optimisation, these methods narrowed down the initial set of 21 elements to a group of 10 and 6 elements, according to redundancy and relevance criteria, respectively. The validation of ANN tools, using the salient surface parameters, yielded accuracy close to 85% when applied for identification of processing disturbances, while the wettability was predicted within an r.m.s. error of 11 degrees, equivalent to the static water contact angle (CA) measurement uncertainty.