Tool Wear Prediction Method for High Volume Bipolar Plate (BPP) Forming Process considering Tool Geometry

Christian Brecher,S. Lee(이상욱),Christian Brecher,B. D. Youn(윤병동) Korean Society for Precision Engineering 2023 한국정밀공학회 학술발표대회 논문집 Vol.2023 No.11

Analysis of New, Energy-Efficient Hydraulic Unit for Machine Tools

Christian Brecher,David Jasper,Marcel Fey 한국정밀공학회 2017 International Journal of Precision Engineering and Vol.4 No.1

In machine tools, hydraulic units are one of the main consumers of energy. Hydraulically driven functionalities of machine tools include tool clamping, tool change, work piece clamping or palette change operating. For these functions, two pressure circuits (60 bar and 200 bar) are necessary. This paper introduces a novel, energy-efficient hydraulic unit that combines a variable displacement pump with a variable speed controlled drive and hydraulic booster. The result is a lower overall energy consumption and decreased operating temperatures of the hydraulic unit. Hydraulic fluids contain up to 85% mineral oil. This paper also introduces a mineral oil-free, water-based hydraulic fluid, which will be replacing a conventional mineral oil-based hydraulic fluid (ISO-VG 46) within the novel energy-efficient hydraulic unit. The effects on the unit’s performance (operating temperature, pressure, flow rate, power consumption, efficiency) will be evaluated.

Surface roughness prediction through internal kernel information and external accelerometers using artificial neural networks

Guillem Quintana,Thomas Rudolf,Joaquim Ciuran,Christian Brecher 대한기계학회 2011 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.25 No.11

In this paper, the average surface roughness parameter (Ra) is predicted using artificial neural network (ANN) models and internal kernel information and external piezoelectric accelerometer data. Experiments were conducted to obtain data to develop ANN models to predict surface roughness. A total of 72 samples were used to develop two networks, one based on accelerometer inputs and the other on kernel inputs. The Matlab ANN Toolbox was used for the modeling. The two networks had similar characteristics. Feed-forward backpropagation,‘newff’, was the network structure selected, with a Levenberg-Marquardt backpropagation training function, ‘trainlm’, and a backpropagation weight and bias learning function, ‘learngdm’. Samples obtained at the experimental stage were randomly divided into three groups to train (70% of the samples), validate (15% of the samples) and test (15% of the samples) the neural networks with a 'dividerand'data division function. The input processing functions used were 'fixunknowns', 'removeconstantrows' and 'mapminmax'. The transfer function was 'tansig' for hidden layers and 'purelin' for the output layer. The output processing functions used were 'removeconstantrows'and 'mapminmax'. The inputs consisted of the process parameters of radial depth of cut (Ae), the axial depth of cut (Ap), the spindle speed (N), the feed rate (f), the feed per tooth (fz), the cutting speed (Vc), the tooth passing frequency (ft), the cutting section (Cs),the material removal rate (MRR) and the cutting tool characteristics of the cutter radius (R), the number of teeth (Z) and the tool shape. The main difference between the two neural networks consisted of data origin: one considered data obtained with accelerometers and the other data collected in the NC kernel. Results showing high correlation factors between outputs and targets confirm that data provided by both internal and external sources can be useful for Ra prediction. However, NC kernel data provide several advantages.

Design and Development of a High Efficiency Air Turbine Spindle for Small-Part Machining

Paul Harris,Michael Wintterer,David Jasper,Barbara Linke,Christian Brecher,Stephen Spence 한국정밀공학회 2020 International Journal of Precision Engineering and Vol.7 No.5

This paper reports on the development of a high efficiency and low inertia miniature pneumatic turbine for ultra-high-speed manufacturing spindle applications. It is proposed that radial-inflow/axial-outflow type turbines are well suited for micromachining spindles. To validate the turbine performance potential, a proof-of-concept prototype turbo-spindle was developed and experimentally tested. The prototype spindle produced a net mechanical power output of over 60 W at a speed of 90,000 rpm. The experimental results demonstrate a micro-turbine efficiency of over 50% and a turbine power output of approximately 100 W. This compares to a typical turbine efficiency of around 20% in commercial micro-machining spindles. Further potential improvements to both turbine and spindle energy efficiency are outlined.

Simulation of electrical conductivity for nanoparticles and nanotubes composite sensor according to geometrical properties of nanomaterials

Min, Soo-Hong,Lee, Tae Hun,Lee, Sangwook,Song, Ji-Hyeon,Lee, Gil-Yong,Zontar, Daniel,Brecher, Christian,Ahn, Sung-Hoon Elsevier 2019 Composites Part B, Engineering Vol.174 No.-

<P><B>Abstract</B></P> <P>The nanocomposite based on conductive nanoparticles and nanotubes are widely used for stretchable strain sensors application. Since electrical properties varies by the geometrical properties of nanomaterials, it is important to understand the effects of nanomaterials by strain to optimise the sensor performance. However, it is difficult to fabricate strain sensor using nanomaterials with exactly desired properties. Hence, in this study, we have developed a simulation method for conductive nanoparticles and nanotubes composite using Lennard-Jones potential model and the voter model. First, we optimised the distribution of nanocomposites using Lennard-Jones potential model in the boundary conditions according to external strain. Then, we counted the average attachment among nanomaterials by strain using the voter model which is directly influence electrical conductivity of strain sensors. Moreover, we validated proposed simulation method using experimental value of fabricated strain sensor with various nanocomposite composition ratio and packing ratio. Using the suggested method, the effect of geometrical properties of nanomaterials can be accurately estimated with low simulation cost. Finally, we obtained the simulation value for strain sensor performance by various diameter of nanoparticle, diameter of nanotube, and length of nanotube. We demonstrated that the diameter of nanoparticle is a primary factor for sensor performance while the diameter of nanotubes does not have great influence. Based on the simulation results, it was confirmed that the change of electrical conductivity according to the strain is the largest at small and uniform nanomaterials. The developed simulation method can be applied to the general analysis of electrical properties for nanocomposites.</P>

Appropriate Smart Factory for SMEs: Concept, Application and Perspective

정우균,Dong-Ryul Kim,Hyunsu Lee,Tae Hun Lee,Insoon Yang,Byeng D. Youn,Daniel Zontar,Matthias Brockmann,Christian Brecher,Sung-Hoon Ahn 한국정밀공학회 2021 International Journal of Precision Engineering and Vol.22 No.1

In the manufacturing industry, the smart factory is considered the final stage of the Fourth Industrial Revolution. Manufacturing companies are pursuing breakthroughs by introducing various advanced technologies to ensure their competitiveness. However, it is difficult for small and medium-sized enterprises (SMEs) to adopt smart-factory technologies, owing to financial and technical burdens. This paper proposes a smart factory that can be applied technically and strategically to the introduction of a smart factory for SMEs. The concept of an ‘appropriate smart factory’ involves applying appropriate measures in terms of cost and scale with consideration of the situations faced by SMEs. The goal is to build a smart factory that has necessary functions (Essential) but can be easily operated (Simple) at a low cost (Affordable) and has compatibility (Interoperable). This paper presents technical application measures such as appropriate smart sensors, appropriate IoT (Internet of Things), and small data processing, along with the definition of an appropriate smart factory. In addition, a case study was examined where the quality inspection equipment for garment manufacturing SMEs was developed by applying the appropriate smart factory concept.

Evaluation of Industry 4.0 Data formats for Digital Twin of Optical Components

Arno Schmetz,Tae Hun Lee,Maximilian Hoeren,Marvin Berger,Susanne Ehret,Daniel Zontar,Soo-Hong Min,Sung-Hoon Ahn,Christian Brecher 한국정밀공학회 2020 International Journal of Precision Engineering and Vol.7 No.3

A wide range of software and hardware components are present in today’s production systems and plants using a variety of interfaces and data formats for information exchange on diff erent levels of the system. To increase the traceability, the lifecycle management and providing a single point of source of component-specifi c data, the Digital Twin technology is proposed, linking diff erent data sets tailored to the requirements of diff erent kind of users (e.g., machines, technicians, logistics, manufacturing execution systems). The data exchange between entities in the manufacturing network relies on machine-readable, fl exible and self-describing data formats. When implementing or integrating diff erent components into complex systems, the interoperability challenge is a major concern to address by the system designers and becomes a central task for the creation and integration of Digital Twin technology. In this paper, we evaluate diff erent formats that are used in real environments and create a requirements framework for an ideal format for exchanging fl exible and self-describing data in context of optical components manufacturing process and their special requirements.