We present an automated turning insert wear detection system developed for aeronautical Low Pressure Turbines (LPT) casing machining process based on a binary classifier using Convolutional Neural Networks (CNN). This method involves acquiring the image on the machine itself. During this process, removing the insert from the tool holder is not necessary, and the wear assessment is performed before the next workpiece is mechanized. Since datasets in tool wear prediction are often imbalanced, a multi perspective camera technology as well as data augmentation and class weighting are utilized to address both the number of worn parts considered and the cost of image acquisition. In this study four different insert types and two CNN architectures (specific and universal models) are considered and evaluated. The effects of data augmentation and training set size are discussed. While the models trained perform well on round inserts, they fail on rhombic insert types. An accuracy up to 97.8 % (Matthew’s correlation coefficient of 0.955) is achieved by the machine learning model. Additionally, it can detect defects on a variety of insert types.

Thin walled-parts refer to lightweight structural parts comprised of thin plates and stiffeners. During the machining process of thin-walled parts, machining distortion often occurs due to uncertain factors such as varying stiffness, cutting force, cutting temperature, residual stress and other factors. This paper studied the minimization of the failure probability of machining distortion by controlling the uncertainties of inputs. For this, a fuzzy inference model for the machining system was proposed to determine the effects of uncertain factors on the machining distortion errors, which was composed of rule frame and result frame. In the rule frame, machining parameters, outline size, and wall thickness were used as inputs. In the result frame, linear stiffness, cutter path, as well as cutting force were taken as the input parameters. The values of machining distortion were the output, taken into a threshold function. Comprehensive matching was defined to measure the importance of uncertain inputs to outputs. Machining distortion will exceed the specification (failure) with the increase in comprehensive matching. Therefore, the comprehensive matching index evaluates the effects of the uncertainties on the machining distortion and quantify the effects of given uncertain parameters. Two engineering examples were employed to illustrate the accuracy and efficiency of the proposed approach. It revealed that the comprehensive matching of cutting force to the failure probability of machining distortion was the maximum, 0.040, which was 12 to 13 times greater than that of linear stiffness or cutter path.

Sensitivity analysis is widely used in engineering design to illustrate the effect of the variations of input variables on the output response of a system efficiently. The current study proposes a novel method of variance-based sensitivity analysis to evaluate the effects of initial residual stress (IRS) and surface residual stress (SRS) on the uncertainty of machining deformation. This analysis introduced the variance as the uncertainty evaluation index and the variance contribution to reflect the effects of residual stress on machining deformation. The variance contributions were divided into two components: (1) independent contributions due to the variations from the independent portion of a variable and (2) correlated contributions due to the variations from the portion of a variable correlated with other variables. Uncertainty characteristics (variance σ2) of residual stress were extracted and an analytical model for the relationship between residual stress and the bending moment was established. An importance measure matrix consisted of variance contribution of each residual stress variable was constructed. Concerning correlated residual stress variables, the effects of variances and correlation coefficients of residual stress on the variance of machining deformation were studied. Experimental work on a sidewall structure showed that the variance contribution of IRS at the surface layer was 5.3 to 17.8 times greater than SRS. Furthermore, the sensitivity analysis for correlated residual stress variables indicated that decreasing the variances of IRS and SRS (especially IRS) and keeping the values of correlation coefficients between −0.5 to 0.5 are beneficial to reduce the uncertainty of machining deformation.

Die additive Fertigung von Schneidstoffen bietet die Chance, leistungsfähigere Zerspanungswerkzeuge herzustellen. Vorgestellt wird zum einen das Lithography-based Ceramic-Manufacturing-(LCM)-Verfahren und zum anderen die Entwicklung damit gefertigter Wendeschneidplatten (WSP). Die Funktionstauglichkeit dieser keramischen WSP wird in Außenlängsdrehversuchen an vermicularem Gusseisen untersucht.