Probabilistic Sequential Prediction of Cutting Force Using Kienzle Model in Orthogonal Turning Process

Abstract

Probabilistic sequential prediction of cutting forces is performed applying Bayesian inference to Kienzle force model. The model uncertainties are quantified using the Metropolis algorithm of the Markov chain Monte Carlo (MCMC) approach. Prior probabilities are established and posteriors of the models parameters and force predictions are completed using the results of orthogonal turning experiments. Two types of tools with chamfer (rake) angles of 0 deg and −10 deg are tested under various cutting speed and feed per revolution values. First, Bayesian inference is applied to two force models, Merchant and Kienzle, to investigate the cutting force prediction at the low feed values for the 0 deg rake angle tool. Second, the results of the posteriors of the Kienzle model parameters are used as prior probabilities of the −10 deg rake angle tool. The simulation results of the 0 deg and −10 deg tool rake angle are compared with the experiments which are obtained under other cutting conditions for model verification. Maximum prediction errors of 7% and 9% are reported for the tangential and feed forces, respectively. This indicates a good capability of the Bayesian inference for model parameter identification and cutting force prediction considering the inherent uncertainty and minimum input experimental data.

Evaluation of the machinability performance of PH 13-8 Mo maraging steel used in the aerospace industry

“…Since chip geometry is also of great importance in this calculation, the chip cross-section area (A0) is calculated by multiplying the depth of cut (ap) and the feed rate (f) according to Equation 9. In Equation 9, a p represents the depth of cut (mm) and f represents the feed rate (mm/rev). 28 In the study conducted according to these calculations, it was observed that the chip cross-section area would decrease by decreasing the ap and f in Equation 9. Accordingly, the decreasing cross-section area and constant specific cutting resistance in Equation 8 would cause Fc to decrease.…”

“…In Equation 9, a p represents the depth of cut (mm) and f represents the feed rate (mm/rev). 28 In the study conducted according to these calculations, it was observed that the chip cross-section area would decrease by decreasing the ap and f in Equation 9. Accordingly, the decreasing cross-section area and constant specific cutting resistance in Equation 8 would cause Fc to decrease.…”

Toolpath planning method with the constraint of cutting force fluctuation in slow tool servo turning for complex curved surface parts

“…Based on a predictive machining theory, Fu et al [18] proposed a cutting force model for turning, which adopted the non-equidistant shear zone model and regarded the cutter geometry and processing conditions as the entering information. Salehi et al [19] performed the cutting force forecast by employing Bayesian inference in the cutting force model. A consecutive force forecast was performed by employing the posterior probabilities of the model arguments.…”

Cutting force prediction considering force–deflection coupling in five-axis milling with fillet-end cutter

“…Hui W [7] established the mechanism model of cutting forces in the feed direction of CFRP composite end face grinding with horizontal ultrasonic vibration, and established a mechanical model based on toughness model. Salehi M [8] proposed cutting forces prediction using Bayesian inference (MCMC simulation) for the Merchant and Kienzle force models. John M [9] developed a new energy dissipation mechanism, improving the previously derived steady-state cutting force analysis model, to improve the predictive ability of high blade cutting conditions.…”