State-of-the-art in empirical modelling of rapid prototyping processes

Abstract

Purpose -The empirical modelling of major rapid prototyping (RP) processes such as fused deposition modelling (FDM), selective laser sintering (SLS) and stereolithography (SL) has attracted the attention of researchers in view of their contribution to the overall cost of the product. Empirical modelling techniques such as artificial neural network (ANN) and regression analysis have been paid considerable attention. In this paper, a powerful modelling technique using genetic programming (GP) for modelling the FDM process is introduced and the issues related to the empirical modelling of RP processes are discussed. The present work aims to investigate the performance of various potential empirical modelling techniques so that the choice of an appropriate modelling technique for a given RP process can be made. The paper aims to discuss these issues. Design/methodology/approach -Apart from the study of applications of empirical modelling techniques on RP processes, a multigene GP is applied to predict the compressive strength of a FDM part based on five given input process parameters. The parameter setting for GP is determined using trial and experimental runs. The performance of the GP model is compared to those of neural networks and regression analysis. Findings -The GP approach provides a model in the form of a mathematical equation reflecting the relationship between the compressive strength and five given input parameters. The performance of ANN is found to be better than those of GP and regression, showing the effectiveness of ANN in predicting the performance characteristics of the FDM part. The GP is able to identify the significant input parameters that comply with those of an earlier study. The distinct advantages of GP as compared to ANN and regression are highlighted. Several vital issues related to the empirical modelling of RP processes are also highlighted in the end. Originality/value -For the first time, a review of the application of empirical modelling techniques on RP processes is undertaken and a new GP method for modelling the FDM process is introduced. The performance of potential empirical modelling techniques for modelling RP processes is evaluated. This is an important step in modernising the era of empirical modelling of RP processes.

The manufacture of honeycomb cores using Fused Deposition Modeling

“…Many methods for improving the mechanical properties of the components have been suggested, such as the use of curved layers, 30,31 design optimization, 32,33 and process parameter optimization. 18,19,34,35 Material choice is limited by the melting point achievable by the extruder and viscosity during extrusion.…”

Fundamental Requirements for Data Representations in Laser-Based Powder Bed Fusion

“…Scan path planning includes the determination of path, speed, laser power, and hatching width [14,15]. These process parameters are determined by either trial-and-error, or using empirical process models [16] or physics-based process models [17,18,19].…”

Predicting the mechanical characteristics of hydrogen functionalized graphene sheets using artificial neural network approach

“…ANN network consist of three layers: input, hidden, and output layers. Input layer consist of two neurons since there are two inputs [22][23][24][25][26][27][28][29][30][31]. The number of neurons in the hidden layer is chosen based on trial-and-error method.…”