Experimental and empirical model analysis of microsurface texturing on 316 L press-fit joints fabricated by selective laser melting

In this study, selective laser melting (SLM) was investigated for the manufacturing of 316L stainless steel press-fit joints. The accuracy of selective laser melting technique in fabrication of texture profile in shape, pitch and height of microsurface texturing was examined. The resulting insertion and removal forces achieved from the produced textured pins for cold-formed high-end fixation applications were studied. The experimental results showed that the shape, pitch and height of the texture, as well as the resultant bonding strength of the joints, can be effectively set via control of the SLM processing parameters. While trapezoidal and triangular shapes of the texture lead to stronger bonding compared with oval-shaped texture profiles, the texture height was found to have a predominant effect on bond strength. To a much lower extent, larger pitch distances also resulted in higher bond strengths. A combination of abrasive and adhesive wear mechanisms was detected via examination of the inner surface of the hub into which the press fit was inserted. Along with a process map of design of the microsurface texture geometry of metal interference fit joints, this paper also presents the underlying mechanics for their bonding. The SLM process is shown to present a useful one-step method for the manufacturing of knurl metallic interference fit pins of customisable and definable texture and ensuing bond strength

Predictive quality modelling of polymer and metal parts fabricated by laser-based manufacturing processes

Laser processing techniques are widely used in industrial applications for their repeatability and reliability. However, the optimization of a laser process for a specific application is challenging and require detailed experimental investigations to determine the input processing conditions and parameter values that deliver high repeatability and reliability. The objective of this doctoral work was therefore to develop prediction models for laser-based processing techniques to understand the laser processing parameter relationship with the output properties and to forecast events not observed experimentally. The important techniques of Selective Laser Sintering (SLS), Laser Surface Texturing (LST),and Selective Laser Melting (SLM) were selected for development of the predictive models. For SLS of glass filled polyamide parts, an Adaptive Neuro-Fuzzy Inference system using Simulated Annealing method (ANFIS-SA) and Grey Relational Analysis (GRA) were utilised to determine processing parameters (laser power and scan speed, spacing and length) delivering best mechanical properties (tensile strength and elongation). ANFIS-SA system outperformed the GRA in finding optimal solutions for the SLS process applied for glass fiber reinforced part production. For LST study, Artificial Intelligence (AI) models were developed to predict the properties (diameter increase, insertion force and pullout force) of laser processed stainless steel 316 samples used for interference fit. Artificial Neural Network (ANN) and ANFIS were used to predict the characteristics of laser surface texturing. The models based on feedforward neural network (FFNN) were used to examine the effect of the laser process parameters for surface texturing on 316L cylindrical pins. This study demonstrated that ANFIS prediction was 48% more accurate compared to that provided by the FFNN model. Stainless steel 316L cylindrical pins with defined surface structures for interference fit application were manufactured by the Selective Laser Melting Additive Manufacturing technique. The fabricated pins were assessed for resulting bond strength within interference fit joints. The effects of texture profile on the insertion and removal forces were investigated using Box-Behnken design of Response Surface Methodology (RSM) and results are presented and discussed. ANalysis Of VAriance (ANOVA) was used to check the adequacy of the developed empirical relationships. Two quadratic models were generated. One for correlation between profile geometry and insertion force and second for relating the profile geometry to removal force. The models were validated using experimental results and demonstrated good agreement with less than 10% error

The generation and propagation of deep water multichromatic non-linear long crested surface wave

A two dimensional rectangular basin containing an incompressible inviscid homogeneous fluid, initially at rest with a horizontal free surface of finite extent is considered to generate and propagate nonlinear, long-crested waves. A depth profile for the potential is assumed, giving us a waveform relaxation method, thereby drastically reducing the computational cost of solving Laplace?s equation. A multichromatic stochastic wavemaker employing a Dirichlet type boundary condition is applied, with the latter following a standard wave energy spectrum. Laplace's equation is solved using a non-orthogonal boundary fitted curvilinear coordinate system, which follows the free surface, and the full nonlinear kinematic and dynamic free surface boundary conditions are employed. The behavior of this model is studied using standard signal processing tools and a discussion of the results is given. In addition, statistical properties of the output of the model are related to the corresponding statistical properties of the input.NRC publication: Ye