Using Indicators to Describe Distortion Along a Process Chain

Abstract

The objective of the Collaborative Research Center SFB 570 "Distortion Engineering" is the control of distortion of component parts. The overall challenge is developing suitable concepts and methods which can describe and predict distortion of a workpiece along the entire process chain. The objective to control distortion is to determine the mechanisms of distortion from the basic material up to the finished component part. Because of the complexity of the production chain of component parts, not all relationships between cause and effect are known so far. Therefore, manufacturers of component parts do not have continuous planning possibilities along the process chain to guarantee that the chosen material and the manufacturing steps lead to little distortion after the last process step quenching.A solution for this problem may be a prognosis model for distortion along the process chain in order to support the planning process. The inputs of this model should be indicators in terms of significant input parameters which are able to predict distortion. Current elaborations of how far indicators can be used for predicting distortion are presented by the example of the production of bearing rings in the SFB 570.

Avaliação dos efeitos de tensões residuais através da remoção de material

“…Estas pequenas diferenças podem ser explicadas por pequenas variações do equipamento durante o processamento do material. Também observa-se a ½ ø o comportamento trativo ou compressivo apresentado, este comportamento pode ser explicado pela influência do pré-endireitamento (etapa anterior) (Zoch, 2006;Klein et al, 2006). Na Figura 9 observam-se os resultados para a barra endireitada e polida por rolos cruzados ("PERC") com ângulo de 16°, as tensões residuais e deflexões na superfície da barra (remoções 1 a 4) apresentam caráter compressivo.…”

Process chain modeling and selection in an additive manufacturing context

“…[17][18][19][20][21]). However, all manufacturing processes are defined by the interaction of various artifacts.…”

Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

“…To make the theory introduced in the previous sections more feasible, an exemplary description of the manufacturing programme of a highly stressed steel product will be presented. This example is based on an adapted manufacturing programme following (Klein et al 2005) which consists of three process steps: forging, machining and heat treatment (Fig. 2.11).…”