Production Engineering

Cryogenic turning of metastable austenitic steels allows for a surface layer hardening integrated into the machining process, which renders a separate hardening process obsolete. This surface layer hardening is the result of a superposition of strain hardening mechanisms and deformation-induced phase transformation from austenite to martensite. The activation energy required for the latter depends on the chemical composition of the metastable austenitic steel. It can hence be expected that the austenitic stability of the workpiece material varies depending on the batch and that differences in the metallurgical surface layer properties and thus also in the microhardness result after cryogenic turning. Therefore, in this paper, various batches of the metastable austenitic steel AISI 347 were turned utilizing cryogenic cooling with the same machining parameters. The thermomechanical load during the experiments was characterized and the resulting subsurface properties were investigated. The content of deformation-induced α′-martensite was quantified via magnetic sensor measurements and the distribution was examined using optical micrographs of etched cross-sections. It was found that similar amounts of deformation-induced α′-martensite were generated in the workpiece surface layer for all batches examined. Furthermore, the workpieces were analyzed with regard to the maximal hardness increase and the hardness penetration depth based on microhardness measurements. A significant surface layer hardening was achieved for all batches. This shows that surface layer hardening integrated in the manufacturing process is possible regardless of batch-dependent differences in the chemical composition and thus varying austenite stability of the metastable austenitic steel.

Assembly complexity assessment is a widely addressed topic in manufacturing. Several studies proved the correlation between assembly complexity and the occurrence of defects, thus justifying this increasing attention. A measure of complexity provides control over quality costs and performances. Over the years, many methods have been proposed to provide an objective measure of complexity. One of the most widely diffused is the so-called MCAT (i.e., “Manufacturing Complexity Assessment Tool”) modified by Samy and ElMaraghy H. for assessing product assembly complexity. Although this method highlights some interesting aspects, it presents some critical issues. This work aims to thoroughly analyse this method, focusing on its strengths and limitations.

Within the European initiative OSACA (Open System Architecture for Controls with Automation Systems) a company-spanning open control architecture has been developed as of 1992. Since then the development of open control systems has continued until today. In 2008 a study was carried out with the objective to verify the results of 15 years of work for open control systems in Europe. It ought to identify especially the state of the art in the polled companies as well as their wishes and goals for the future. This article contains the most important results.

This paper presents a deep learning-based approach for the detection and classification of production defects that complements an existing thermographic online monitoring system for Automated-Fiber-Placement (AFP) processes. The detection and classification procedure is performed in two stages. In the first stage, the system monitors each tow individually and classifies its process status. Furthermore, it detects and classifies production defects that affect individual tows such as a tow-twist. In the second stage, the system monitors the total width of the faultless tows. In this stage, production defects effecting multiple tows, for example gaps or overlaps, are detected and classified. Twelve different deep convolution neural networks (CNN) with three various architectures are learned supervised relating to different data sets. The performance of both identification stages is explored separately before the entire system will be set up. Therefore, the thermal images of the data sets are superimposed by noise to test the performance of the selected CNN.

Gantry type component placement machines are widely used in electronics industry due to their accuracy, speed and flexibility. In order to make optimal plans of their usage one has to know the manufacturing time of a given component placement work before the actual processing. This presupposes the use of a reliable machine simulator. In the present article, discrete-event simulation is applied to predict component placement times of gantry type machines. The generic simulator model is based on a circumstantial analysis of the time factors by the means of observing and measuring the times of operations performed by the machine. As a result of this, very high accuracy is achieved; the operation times predicted by the simulator are for a particular machine type (Universal Instruments GC-60D) in range ±1 % from the observed operation times.

To overcome current limitations in micro manufacturing a new approach instead of evolutionary small step progress has to take place. Applying small machine tools to manufacture small workpieces allows for a leap of improvements enabled by the small size itself. This miniaturization in comparison to simply down-scaling current concepts qualifies the use of materials and technologies commonly not found in machine tools. This contribution describes the aspects, methodology, qualifications as well as cause and effects pursued by the approach of small machine tools for small workpieces.