Structural integrity is a crucial aspect of engineering components, particularly in the field of additive manufacturing (AM). Surface roughness is a vital parameter that significantly influences the structural integrity of additively manufactured parts. This research work focuses on the prediction of the surface roughness of additive-manufactured polylactic acid (PLA) specimens using eight different supervised machine learning regression-based algorithms. For the first time, explainable AI techniques are employed to enhance the interpretability of the machine learning models. The nine algorithms used in this study are Support Vector Regression, Random Forest, XGBoost, AdaBoost, CatBoost, Decision Tree, the Extra Tree Regressor, the Explainable Boosting Model (EBM), and the Gradient Boosting Regressor. This study analyzes the performance of these algorithms to predict the surface roughness of PLA specimens, while also investigating the impacts of individual input parameters through explainable AI methods. The experimental results indicate that the XGBoost algorithm outperforms the other algorithms with the highest coefficient of determination value of 0.9634. This value demonstrates that the XGBoost algorithm provides the most accurate predictions for surface roughness compared with other algorithms. This study also provides a comparative analysis of the performance of all the algorithms used in this study, along with insights derived from explainable AI techniques.

The fused deposition modelling (FDM) technique involves the deposition of a fused layer of material according to the geometry designed in the software. Several parameters affect the quality of parts produced by FDM. This paper investigates the effect of FDM printing process parameters on tensile strength, impact strength, and flexural strength. The effects of process parameters such as printing speed, layer thickness, extrusion temperature, and infill percentage are studied. Polyactic acid (PLA) was used as a filament material for printing test specimens. The experimental layout is designed according to response surface methodology (RSM) and responses are collected. Specimens are prepared for testing of these parameters as per ASTM standards. A mathematical model for each of the responses is developed based on the nonlinear regression method. The desirability approach, nonlinear regression, as well as experimental values are in close agreement with each other. The desirability approach predicted the tensile strength, impact strength, and flexural strength with a less percentage error of 3.109, 6.532, and 3.712, respectively. The nonlinear regression approach predicted the tensile strength, impact strength, and flexural strength with a less percentage error of 2.977, 6.532, and 3.474, respectively. The desirability concept and nonlinear regression approach resulted in the best mechanical property of the FDM-printed part.

The current research aims to optimize the multi-response during Wire Electrical Discharge Machining (Wire-EDM) of SLMed AlSi10Mg by using Taguchi integrated Grey Relational Analysis (GRA). Selective Laser Melting (SLM) is one of the most well-known and practical Additive Manufacturing (AM) methods, with the potential to replace many traditional production processes. The complex metallic support structures created during SLM require special attention as they are difficult to remove by hand. Therefore, this study performs post-processing using the Wire-EDM precision machining technique to evaluate the machinability of the SLMed AlSi10Mg as-built part. The multi-response optimization in this study aims to achieve the maximum material removal rate and the lowest surface roughness while considering four important influencing factors (pulse On time, pulse Off time, servo voltage, and wire feed rate) at four distinct levels. The Taguchi integrated Grey Relational Analysis (GRA) revealed that a pulse On time of 118 μs (Level 3), a pulse Off time of 44 μs (Level 1), a servo voltage of 60 V (Level 4), and a wire feed rate of 7 m s⁻¹ (Level 4) are recommended to achieve optimal machining of SLMed AlSi10Mg. Furthermore, the derived optimization results were carefully verified through a confirmatory experiment, which showed a 38.57% improvement in multi-response characteristics compared to the initial Wire-EDM parameter settings. The methodology proposed in this work offers a standardized approach that has the potential to be implemented for the rapid and precise prediction and optimization of surface roughness, while achieving better material removal during Wire-EDM of SLMed AlSi10Mg.

Amorphous carbon film, also known as diamond-like carbon (DLC) film, is a promising material for tribological application. It is noted that properties relevant to tribological application change significantly depending on the method of preparation of these films. These properties are also altered by the composition of the films. In view of this, the purpose of the present study was to determine the optimal values of selected deposition parameters of hydrogenated DLC films on high-speed steel tool substrates with the inductively coupled plasma enhanced chemical vapor deposition (IC-PECVD) method. To optimize the deposition parameters for hydrogenated DLC films, Taguchi's method was used. Deposition parameters (bias voltage, bias frequency, deposition pressure, and gas composition) were optimized with consideration to hardness of the film. Based on the experimental results, the optimal parameter setting are −50 V, 500 Hz, 4 µbar, and 90:10 for achieving maximum value of hardness. It was found that bias voltage has greater influence on hardness. At the optimum conditions, the conformance run resulted in a hardness value of 1580 KHN. Atomic force microscopy images showed that the DLC films are smooth with an average roughness (Ra) of 1.24 nm on silicon substrate.