Application of 3D Scanning as an Indirect Method to Analyze and Eliminate Errors on the Manufactured Yoke-Type Forgings Forged in SMED Device on Modernized Crank Press

This article proposes an indirect measurement method based on a dimensional and shape analysis of forgings for the evaluation of the manufacture and the proper operation of the key elements of the crank press, in which after modernization, a quick tool assembly based on SMED (Single Minute Exchange of Die) was implemented. As a result of the introduced changes aiming at improving the forging aggregate and increasing the production efficiency, errors were observed on the manufactured products-forgings in the form of twists and joggles. In order to solve the problem, a lot of advanced methods was used, including: dynamic system of deformation analysis, numerical modeling and as well as dimensional and shape analysis by 3d scanning. Despite the above, this approach (classic way) did not solve the problem. A proprietary method with the use of 3D reverse scanning was proposed, which allows to solve the problem of forgings errors. Based on the measurement results and analyses for a few variants of production cycles, the necessary changes were obtained, making it possible to minimize the errors and obtain proper products in respect of geometry and quality.

Efficient joint identification and fluted segment modelling of shrink-fit tool assemblies by updating extended tool models

In milling, the dynamic behavior of the tool center point is crucial for estimating surface quality of the workpiece as well as the process stability behavior. Experimental-analytical receptance coupling can be used for predicting the tool tip dynamics but requires accurate analytical modelling of the holder-tool assembly. This includes the reliable identification of the holder-tool joint properties as well as the correct modelling of the fluted segment of end mills. However, the modelling effort associated with accurately representing the dynamic behavior of the fluted segment is significant. In addition, the joint identification requires a reference tool tip frequency response function of the tool assembly clamped in the machine spindle. This is inefficient and can also lead to incorrect estimation of joint properties. This paper provides an efficient method for joint identification and fluted section modelling using an offline, free–free excitation approach. The objective of this paper is to enable a direct comparison of the dynamic behavior of the freely constrained analytical tool assembly model with that of the real freely constrained tool assembly. The comparison of displacement to force frequency response at certain points on the tool assembly allows for the identification of tool model parameters such as the joint properties and effective diameter of the fluted segment. The comparability is realized by extending the analytical holder-tool beam model to include the receptance model of the standard spindle-holder interface. In this study, as an example, a thermal shrink-fit holder-tool beam model is extended to include an HSK-A63 interface. Subsequently, frequency response functions at two points on the real freely constrained tool assembly are measured in order to identify the joint stiffness and effective diameter of the fluted segment using the corresponding proposed formulations. The updated holder-tool model is then coupled with a 4-axis milling machine and validated. Despite the reduced modelling effort, a good prediction accuracy could be achieved for different holder-tool combinations.

DYNAMICAL CONTACT PARAMETER IDENTIFICATION OF SPINDLE-HOLDER-TOOL ASSEMBLIES USING SOFT COMPUTING TECHNIQUES

In industry, the capability to predict the tool point frequency response function (FRF) is an essential matter in order to ensure the stability of cutting processes. Fast and accurate identification of contact parameters in spindle-holder-tool assemblies is very important issue in machining dynamics analysis. This work is an attempt to illustrate the utility of soft computing techniques in identification and prediction contact parameters of spindle-holder-tool assemblies. In this paper, three soft computing techniques, namely, genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) were used for identification of contact dynamics in spindle-holder-tool assemblies. In order to verify the proposed identification approaches, numerical and experimental analysis of the spindle-holder-tool assembly was carried out and the results are presented. Finally, a model based on the adaptive neural fuzzy inference system (ANFIS) was used to predict the dynamical contact parameters at the holder-tool interface of a spindle-holder-tool assembly. Accuracy and performance of the ANFIS model has been found to be satisfactory while validated with experimental results.