The present work deals with a technological study to integrate the 1.4034 martensitic stainless steel sheet alloy in the conventional press hardening process. Based on preliminary work, side sill demonstrators with ta...
The present work deals with a technological study to integrate the 1.4034 martensitic stainless steel sheet alloy in the conventional press hardening process. Based on preliminary work, side sill demonstrators with tailored mechanical properties were manufactured by press hardening under conventional process parameters. The resulting microstructure and mechanical properties of the produced parts were characterized. The tailoring of the mechanical properties consists of the development of two sections with completely different mechanical properties in a single part. To achieve this, a half of the blank was insulated with a refractory during austenitization treatment. This avoided the heating of the insulated side until the austenitization temperature. Therefore, only the non-insulated side was hardened by quenching. Moreover, depending on the austenitization temperature the resulting mechanical properties can be adjusted.
Vibration assisted machining (VAM)adds sinusoidal tool vibration to the conventional machining (CM) process. It is generally recognized that VAM can effectively improve machining quality and machining efficiency, howe...
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Vibration assisted machining (VAM)adds sinusoidal tool vibration to the conventional machining (CM) process. It is generally recognized that VAM can effectively improve machining quality and machining efficiency, however, the theoreticalbasis for this isnot fully developed. One feasible explanation is about the improvement of cutting stability. In the previous literatures, time-domain simulationsof chatter forCM and VAM have been compared, showing VAM can suppress chatter and increase cutting stability under various conditions. However, the stability lobe diagram that givesa global picture of the stability behavior is still unavailable for VAM. This paper is dedicated to draw the stability lobe diagram for VAM and compare it with that for CM. An analytical predictive force model is developed to determine the dynamic cutting forcein VAM, incorporating material properties, tool geometry, cutting conditions and vibration parameters. Then a stability analysis based on the proposed force model is done and the corresponding stability lobe diagram is obtained, which shows the effect of VAM on cutting stability on a large spindle speed scale. Finally, cutting experiments about surface roughnessare carried out to verify the theoretical conclusion.
Milling forces play an importantrole in the milling process and are generally calculatedbythe mechanistic or numerical methods which are considered time-consuming and impractical forvarious cutting conditions and work...
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Milling forces play an importantrole in the milling process and are generally calculatedbythe mechanistic or numerical methods which are considered time-consuming and impractical forvarious cutting conditions and workpiece-toolpair. Therefore, this paper proposes an analytical method for modelling themilling forcesin helical end milling process based on a predictive machining theory, which regards the workpiece material properties, tool geometry, cutting conditions and types of milling as the input data. In this method,each cutting edge is discretized into a series of infinitesimal elements along the cutter axis and the cutting action of each elementis equivalent to the classical oblique cutting process. The three dimensional cutting force components applied on eachelement are predicted analytically using this predictive oblique cutting modelwith the effect of cutting edge radius. Finally, the proposed analytical model of milling forcesis verified by the publishedresultsandthe simulation values using the software AdvantEdgeFEM.
Information extracting method from numerous measured signals is a critical technique for intelligent manufacturing application to further reduce the manpower cost and improve the productivity and workpiece quality. Ma...
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Information extracting method from numerous measured signals is a critical technique for intelligent manufacturing application to further reduce the manpower cost and improve the productivity and workpiece quality. Manually defining signal features,as the common way, unfortunately will lose most of the information and the performance can’t be guaranteed. In the past few years, machine learning method with deep structure has been the most promising automatic feature extracting method which has made great breakthrough in computer vision and automatic speech recognition. In this paper, deep belief networksareemployed using vibration signal obtained from endmilling to build feature space for cutting states monitoring. Greedy layer-wise strategy is adopted to pre-train the network and standard samples are used for fine-tuning by applying back-propagation method. Comparisons are made with several manually defined features both in time and frequency domain, like MFCC and wavelet method. Different modeling methods are also employed in the research forcomparisons. Resultsshow that the deep learning method has similar ability to characterize the signal for cutting states monitoring compared to those manually defined features. And the modeling accuracy ismuch better thanother traditionalmodeling ***, benefitting fromthe potentialcapability in information fusion, deep learning method would be a promising solution for more complex applications, like tool wear monitoring, machining surface prediction et al.
The mold-making industry faces nowadays the challenge of selecting the appropriate manufacturing technology for machining micro molds, normally made of high strength and difficult-to-machine steels. This selection of ...
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ISBN:
(纸本)9780956679079
The mold-making industry faces nowadays the challenge of selecting the appropriate manufacturing technology for machining micro molds, normally made of high strength and difficult-to-machine steels. This selection of appropriate technology shall consider the productivity, the involved costs, the operating times, form accuracy and surface quality. The industry producing micro molds is facing the challenge of choosing from two technologies available: the micro milling and the die-sinking μ-EDM. With regard to identical cavities, experiments were conducted aiming the comparison of the technological limitations for both technologies, micro milling and die-sinking μ-EDM, in respect to form accuracy of parts, surface quality and machining time.
Experimental investigations carried out for this study aimed on developing a μ-EDM technology for producing smallest and finest structures with width of 230 μm and depth of 800 μm into a micro mold made of stainles...
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ISBN:
(纸本)9780956679079
Experimental investigations carried out for this study aimed on developing a μ-EDM technology for producing smallest and finest structures with width of 230 μm and depth of 800 μm into a micro mold made of stainless steel. The main goal was to develop and to optimize the μ-EDM technology concerning the machining time, the tool wear of the electrode, and the form and positioning accuracy of the features. The metrological analysis using measuring distinct devices enabled the choice of the appropriate μ-EDM technology for producing the final cavities. Total machining time below 16 min as well as a tool electrode relative wear below 65 μm could be achieved. The cavity with micro structures could be produced at the hardened steel with respect to the requirements, including the exact size of structures (226 μm width) and precise position of cavity on the workpiece. The die-sinking μ-EDM confirmed to be a suitable technology for producing the cavities with micro-structures in hardened steel.
Jet Electrochemical Machining (Jet-ECM) is a technology for quickly and flexibly generating micro structures and micro geometries in metallic parts independently from the material's hardness and without any therma...
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Jet Electrochemical Machining (Jet-ECM) is a technology for quickly and flexibly generating micro structures and micro geometries in metallic parts independently from the material's hardness and without any thermal or mechanical impact [1] . In the process no tool wear occurs and the machined surface is very smooth [2] . The Jet-ECM process strongly depends on the shape of the electrolyte jet. In a previous study Hackert [3] built a numerical model with COMSOL Multiphysics based on a predefined jet shape. The simulated dissolution results of this model progressively differ from experimental results with increasing processing time. Hence, a multiphysics model which integrated fluid dynamics using the level set method for two-phase flow was created. Furthermore, in the present study the electric boundary resistance at the interface of workpiece and electrolyte is considered. According to the real Jet-ECM process the simulation is divided into two steps. In the first simulation step the jet is formed, and in the second simulation step the anodic dissolution is simulated by deforming the geometry. The dynamic behavior of the electrolyte jet could be simulated during the material removal process. So effects became visible which affect the machining results.
Increasing demands regarding the functionality and the functional density of micro fluidic systems require constant miniaturisation of single components and also of the complete system. A cost efficient and effective ...
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ISBN:
(纸本)9780956679031
Increasing demands regarding the functionality and the functional density of micro fluidic systems require constant miniaturisation of single components and also of the complete system. A cost efficient and effective manufacturing process requires replication processes such as injection moulding or hot embossing to structure disposables cost-effectively. This paper presents the development of the sequential process combination of laser micro structuring and micro milling for the manufacturing of micro fluidic moulds.
Due to the very high demands on availability and efficiency of production systems and industrial systems, condition-based maintenance is becoming increasingly important. The use of condition monitoring approaches to i...
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Due to the very high demands on availability and efficiency of production systems and industrial systems, condition-based maintenance is becoming increasingly important. The use of condition monitoring approaches to increase the machine availability and reduce the maintenance costs, as well as to enhance the process quality, has increased over the last years. The installation of industrial sensors for condition monitoring reasons is complex and cost-intensive. Moreover, the condition monitoring systems available on the market are application specific and expensive. The aim of this paper is to present the concept of a wireless sensor network using Micro-Electro-Mechanical Systems – MEMS sensors and Raspberry Pi 2 for data acquisition and signal processing and classification. Moreover, its use for condition monitoring applications and the selected and implemented algorithm will be introduced. This concept realized by fraunhoferinstitute for Production Systems and Design technology IPK, can be used to detect faults in wear-susceptible rotating components in production systems. It can be easily adapted to different specific applications because of decentralized data preprocessing on the sensor nodes and pool of data and services in the cloud. A concrete example for an industrial application of this concept will be represented. This will include the visualization of results which were achieved. Finally, the evaluation and testing of this concept including. implemented algorithms on an axis test rig at different operation parameters will be illustrated.
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