External grind-hardening forces modelling and experimentation

Grind hardening process utilizes the heat generated in the grinding area for the surface heat treatment of the workpiece. The workpiece surface is heated above the austenitizing temperature by using large values of depth of cut and low workpiece feed speeds. However, such process parameter combinations result in high process forces that inhibit the broad application of grind hardening to smaller grinding machines. In the present paper, modelling and predicting of the process forces as a function of the process parameters are presented. The theoretical predictions present good agreement with experimental results. The results of the study can be used for the prediction of the grind hardening process forces and, therefore, optimize the process parameters so as to be used with every size grinding machine

Sustainability assessment for manufacturing operations

Sustainability is becoming more and more important as a decision attribute in the manufacturing environment. However, quantitative metrics for all the aspects of the triple bottom line are difficult to assess. Within the present paper, the sustainability metrics are considered in tandem with other traditional manufacturing metrics such as time, flexibility, and quality and a novel framework is presented that integrates information and requirements from Computer-Aided Technologies (CAx) systems. A novel tool is outlined for considering a number of key performance indicators related to the triple bottom line when deciding the most appropriate process route. The implemented system allows the assessment of alternative process plans considering the market demands and available resources

A generalised approach on kerf geometry prediction during CO2 laser cut of PMMA thin plates using neural networks

This study presents an application of feedforward and backpropagation neural network (FFBP-NN) for predicting the kerf characteristics, i.e. the kerf width in three different distances from the surface (upper, middle and down) and kerf angle during laser cutting of 4 mm PMMA (polymethyl methacrylate) thin plates. Stand-off distance (SoD: 7, 8 and 9 mm), cutting speed (CS: 8, 13 and 18 mm/sec) and laser power (LP: 82.5, 90 and 97.5 W) are the studied parameters for low power CO2 laser cutting. A three-parameter three-level full factorial array has been used, and twenty-seven (33) cuts are performed. Subsequently, the upper, middle and down kerf widths (Wu, Wm and Wd) and the kerf angle (KA) were measured and analysed through ANOM (analysis of means), ANOVA (analysis of variances) and interaction plots. The statistical analysis highlighted that linear modelling is insufficient for the precise prediction of kerf characteristics. An FFBP-NN was developed, trained, validated and generalised for the accurate prediction of the kerf geometry. The FFBP-NN achieved an R-all value of 0.98, in contrast to the ANOVA linear models, which achieved Rsq values of about 0.86. According to the ANOM plots, the parameter values which optimize the KA resulting in positive values close to zero degrees were the 7 mm SoD, 8 mm/s CS and 97.5 W LP