Modeling and optimization of fused deposition modeling (FDM) process through printing PLA implants using adaptive neuro-fuzzy inference system (ANFIS) model and whale optimization algorithm

Abstract

Fused deposition modeling (FDM) is a widely used additive manufacturing (AM) technique for developing complex features and geometries within the shortest possible time as per customer needs. Nowadays, the customization of biomedical parts is becoming possible due to the increasing accuracy and enhanced ability of FDM machines to control the process parameters. The control on surface quality of parts produced by FDM process is of prime concern for the researchers, which are induced due to the stair steps on sloping surfaces, and needs addressing. In addition, to meet customer demands rapidly, the FDM part build time must be reduced without much compromising the strength of the parts necessary for specific applications. In the same context, this paper presents the effect and control of FDM process parameters, i.e., layer thickness, raster angle, infill density and internal structure, on surface roughness, build time and compressive strength of developed biomedical implant parts. The face-centered central composite design is employed to consummate the experimental trials, and experimental data are used to establish an adaptive neuro-fuzzy inference system (ANFIS) model for predicting the surface roughness, build time and compressive strength with respect to changes in FDM process parameters. At the end, whale optimization algorithm (WOA) has been applied to minimize surface roughness, minimize build time and maximize compressive strength simultaneously. Then, the optimal solutions obtained from WOA methodology have been compared with ANFIS predicted results. The results reveal that ANFIS-WOA methodology provides optimal combination of FDM process parameters accurately.

Analysis of AM Parameters on Surface Roughness Obtained in PLA Parts Printed with FFF Technology

“…Moreover, they used the centroid in order to de-fuzzify the aggregated output. Some other studies, such as that of Sai et al [ 26 ], employed a central composite design and an ANFIS to analyze the influence of process parameters in FDM of PLA implants. Moreover, hybrid optimization techniques based on genetic algorithm-adaptive neuro fuzzy interface system (GA-ANFIS) have also been used in order to optimize the FDM process parameters, as can be observed in Deshwal et al [ 27 ].…”

Hybrid manufacturing process planning with predictive approach: a comprehensive literature review

“…The model error was not quanti ed. Sai et al [133] proposed a simulation model based on an adaptive neuro-fuzzy inference system (ANFIS) and meta-heuristic optimization algorithm for modeling and optimization of the fused deposition modeling process (FDM) for implant printing. The percentage relative error for the three simulated parameters was approximately 13%.…”

Experimental investigation and multi-objective optimization of multiple mechanical characteristics for chemically treated kenaf fibre reinforced epoxy composite using grey fuzzy logic

“…To minimize or adequately handling such uncertainty or vague results, Zadeh coined the fuzzy logic theory and different membership functions that can effectively solve such problems. 43 Therefore, a fuzzy reasoning model of multi-variate characteristics can efficiently be combined with GRA and referred to as grey-fuzzy systems as shown in Figure 3. In contrast to complicated multi-response optimization, grey-fuzzy grade alone can be successfully used for optimization of multi-variate system.…”