Intelligent dual curve-driven tool path optimization and virtual CMM inspection for sculptured surface CNC machining

This paper investigates the profitability of a dual‐curve driven surface finish tool path under the concept of optimizing crucial machining parameters such as toroidal end‐mill diameter, lead angle and tilt angle. Surface machining error as well as tool path time are treated as optimization objectives under a multi‐criteria sense, whilst a central composite design is conducted to obtain experimental outputs for examination and, finally, fit a full quadratic model considered as the fitness function for process optimization by means of a genetic algorithm. A benchmark sculptured surface given as a second‐order parametric equation was tested and simulated using a cutting‐edge manufacturing modeling software and best parameters recommended by the genetic algorithm were implemented for validation. Further assessment involves the virtual inspection to selected profile sections on the part. It was shown that the approach can produce dual‐curve driven tool trajectories capable of eliminating sharp scallop heights, maximizing machining strip widths as well as maintaining smoothness quality and machining efficiency

Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks

The present paper discussed the development of a reliable and robust artificial neural network (ANN) capable of predicting the tribological performance of three highly alloyed tool steel grades. Experimental results were obtained by performing plane-contact sliding tests under non-lubrication conditions on a pin-on-disk tribometer. The specimens were tested both in untreated state with different hardening levels, and after surface treatment of nitrocarburizing. We concluded that wear maps via ANN modeling were a user-friendly approach for the presentation of wear-related information, since they easily permitted the determination of areas under steady-state wear that were appropriate for use. Furthermore, the achieved optimum ANN model seemed to be a simple and helpful design/educational tool, which could assist both in educational seminars, as well as in the interpretation of the surface treatment effects on the tribological performance of tool steels

Surface roughness investigation of poly-jet 3D printing

An experimental investigation of the surface quality of the Poly-Jet 3D printing (PJ-3DP) process is presented. PJ-3DP is an additive manufacturing process, which uses jetted photopolymer droplets, which are immediately cured with ultraviolet lamps, to build physical models, layer-by-layer. This method is fast and accurate due to the mechanism it uses for the deposition of layers as well as the 16 microns of layer thickness used. To characterize the surface quality of PJ-3DP printed parts, an experiment was designed and the results were analyzed to identify the impact of the deposition angle and blade mechanism motion onto the surface roughness. First, linear regression models were extracted for the prediction of surface quality parameters, such as the average surface roughness (Ra) and the total height of the profile (Rt) in the X and Y directions. Then, a Feed Forward Back Propagation Neural Network (FFBP-NN) was proposed for increasing the prediction performance of the surface roughness parameters Ra and Rt. These two models were compared with the reported ones in the literature; it was revealed that both performed better, leading to more accurate surface roughness predictions, whilst the NN model resulted in the best predictions, in particular for the Ra parameter. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Modeling of surface finish in electro-discharge machining based upon statistical multi-parameter analysis

A multi-parameter analysis of surface finish imparted to Ck60 steel plates by electro-discharge machining (EDM) is presented. The interrelationship between surface texture parameters and process parameters is emphasized. An increased number of parameters is studied including amplitude, spacing, hybrid, as well as random process and fractal parameters. The correlation of these parameters with the machining conditions is investigated. Observed characteristics become more pronounced, when intensifying machining conditions. Close correlation exists between certain surface finish parameters and EDM input variables and single and multiple statistical regression models are developed. (C) 2004 Elsevier B.V. All rights reserved

An investigation into abrasive water jet machining of TRIP sheet steels using Taguchi technique and regression models

An experimental investigation of abrasive water-jet machining (AWJM) of transformation induced plasticity (TRIP) multi-phase sheet steels using design of experiments was carried out. The quality characteristics selected for examination were the Kerf mean width and average surface roughness. The process parameters were the nozzle diameter, the stand-off distance and the traverse speed. For the design of experiments the Taguchi methodology was applied. Optimal process parameter values were identified and regression models were applied to the experimental results and were tested by using evaluation experiments. All the predictions are reasonable and compares well with the experimental values. The experimental design indicated that the nozzle diameter is the most important parameter that affects the mean Kerf width and surface roughness followed by the stand-off distance. The proposed methodology could be easily applied to different materials and initial conditions giving reliable predictions, resulting in process optimization and providing a possible way to avoid time- And money-consuming experiments. © Springer-Verlag London 2013

Modeling of abrasive water jet machining using Taguchi method and artificial neural networks

This work presents a hybrid approach based on the Taguchi method and the Artificial Neural Networks (ANNs) for the modeling of surface quality characteristics in Abrasive Water Jet Machining (AWJM). The selected inputs of the ANN model are the thickness of steel sheet, the nozzle diameter, the stand-off distance and the traverse speed. The outputs of the ANN model are the surface quality characteristics, namely the kerf geometry and the surface roughness. The data used to train the ANN model was selected according to the Taguchi's design of experiments. The acquired results indicate that the proposed modelling approach could be effectively used to predict the kerf geometry and the surface roughness in AWJM, thus supporting the decision making during process planning

An investigation of surface quality characteristics of 3D printed PLA plates cut by CO2 laser using experimental design

In this work, two typical surface characteristics, i.e., mean surface roughness and angle of the kerf during laser processing of 3D-printed Polylactic Acid (PLA) plates with 4.00 mm in thickness, are investigated. A carbon dioxide laser was utilized to separate 27 work pieces of rectangular shape. The governing laser parameters, speed of cutting and laser power, were varied according to full factorial experimental methodology. An orthogonal array (OA) having nine combinations was implemented, and nine specimens were cut with the same set-up and the same parameters three times (27 replicates in total). The experimental results were analyzed using descriptive statistical analysis, i.e., histograms, box plots, interaction charts and optimized using analysis of means (ANOM) plots as well as ANOVA analysis. The data analysis indicated that laser speed is the dominant parameter for the kerf angle, whilst both the laser velocity and power are important for mean surface roughness of the cut surface for PLA 3D-printed parts. The spread of the data is smaller in Y direction, which indicates that the weaving phenomenon affects the laser cut performance. © 2021 Taylor & Francis