Optimization Of Process Chains Research Articles

Cause-Effect Relationships in Battery Cell Production - Data based validation of expert knowledge in electrode production

The advancing climate change requires a shift from conventional energy sources to renewable ones, which is accompanied by the indispensability of an efficient and sustainable storage of electrical energy. The production of lithium-ion battery cells comprises an enormous complexity, as it consists of several interlinked process steps, each with different quality requirements for the (intermediate) products and the processes itself. This complexity means that a supposedly insignificant process parameter can lead to errors and deviations in the manufactured products via complex cause-effect relationships, that are not fully known. The missing knowledge of the processes and inter-process interdependencies lead to difficulties in the setup of a suitable quality assurance in battery cell production to reduce scrap rates. Beside these potentials in planning and optimizing the process chain of the battery cell factory of tomorrow are impeded.We developed a method to build up a relevant knowledge base on cause-effect relationships for battery cell production. Expert knowledge based on experience and intuition has been collected and quantitative cause-effect relationships are established. This knowledge is used to set up the initial data acquisition, ensuring relevant data being available for analysis. Afterwards, the data analysis, that validates and extends this expert knowledge, is defined. This approach is applied to real-world use-cases including machinery and laboratory equipment in battery cell production. The electrode production is selected as the basis for validation. The found inter-relationships either validate the qualitative expert knowledge, contradict this prior knowledge or else add new insights about the battery cell electrode production process that have not been considered in the qualitative expert knowledge. Methods used include correlations and a predictive power score for non-linear interdependencies. The cause-effect relationships are constantly updated by automatically analyzing the production data of the digital factory. These identified and validated cause-effect relationships allow the planning of efficient and sustainable battery cell production and data-based process chain optimization.

Process Chain Optimization for SWCNT/Epoxy Nanocomposite Parts with Improved Electrical Properties

Electrically conductive nanocomposites present opportunities to replace metals in several applications. Usually, the electrical properties emerging from conductive particles and the resulting bulk values depend on the micro/nano scale morphology of the particle network formed during processing. The final electrical properties are therefore highly process dependent. In this study, the electrical resistivity of composites made from single-walled carbon nanotubes in epoxy was investigated. Three approaches along the processing chain were investigated to reduce the electrical resistivity of nanocomposites-the dispersion strategy in a three-roll mill, the curing temperature, and the application of electric fields during curing. It was found that a progressive increase in the shear forces during dispersion leads to a more than 50% reduction in the electrical resistivities. Higher curing temperatures of the nanocomposite resin also lead to a decrease of around 50% in resistivity. Furthermore, a scalable resin transfer molding set-up with gold-coated electrodes was developed and tested with different mold release agents. It has been shown that curing the material under electric fields leads to an electrical resistivity approximately an order of magnitude lower, and that the properties of the mold release agent also influence the final resistivity of different samples in the same batch.

Environmental evaluation of process chains

Environmental impacts of process chains have to be evaluated in order to be reduced. Over the last years, a number of models for environmental evaluation has been developed. However, their focus is on the product or the associated processes. The consideration of an entire process chain, including its productivity and the quantity of produced parts is not carried out. This article presents a new approach based on the overall energy demand, which extends the environmental evaluation of products by the productivity of process chains and provides a new key figure that enables an environmental evaluation and optimization of process chains.

Remote joining by plastic deformation in the process of linear flow splitting

Remote joining by plastic deformation in the process of linear flow splitting combines forming with simultaneously activated joining processes. While the forming process takes place in one region of a profile, the joining of the profile with another component occurs outside the forming zone. This approach promises a great potential with regard to joining multi-material units in a short process chain. Linear flow splitting is a rolling process which results in thin-walled and branched parts out of flat sheet metals without any heat treatment. As in other forming processes, residual stresses remain in the manufactured parts. They are used in the study at hand for a mechanical joining of the profile with other components. Based on numerical and experimental results, two models of joining mechanisms based on spatially distributed, uniaxial tensile and compressive stresses are presented and verified. The remote joining approach described in this paper accommodates two challenges of modern manufacturing: On the one hand, the increasing market demands for lightweight products which lead to various material combinations and hybrid structures in complex assembly units; on the other hand, the need for continuous optimization of process chains with regard to efficiency.

Application of methods for ecological optimization of crank shaft forging process

The manufacturing process of forged parts is usually conducted by different subsequent process steps. According to various technological as well as economical and ecological requirements, different and often opposing objectives have to be fulfilled. A representative example for this conflicting situation is the requirement of high product qualities on the one hand and low processing costs on the other hand. A third important factor influencing the process in addition to quality and cost-efficiency is the ecological aspect that is focused more and more in recent years. Material shortage, high energy costs and increasing intensification of statutory regulations lead to the necessity of ecologically optimized processes. Nevertheless, an economic-based evaluation of forging processes is not easy to carry out due to a versatile process chain from raw material to final product. Within the framework of the joint research project “Holistic process chain optimization for forging production of automotive parts in terms of manufacturing efficiency and saving operation energy” an entire evaluation and optimization with special regard to ecologic aspects will be conducted on the example of a crank shaft forging process (Denkena et al. in ZWF 4:224–228, 2013; Int J Precis Eng Manuf 14(7), 2013).

Development of a concept to optimize the energy efficiency in forging process chains

In the industrial production, approaches for the optimization of process chains mainly focus on criteria like quality, costs and time. Normally the energy consumption of process chains is not considered, although the variation of process parameters is an important possibility to reduce the consumption significantly. Besides that, the investigated processes are often optimized locally without considering the interaction between the different process elements of the whole process chain. Based on this background the developed concept realizes the optimization of the energy consumption of a forging process chain by adaptation of its energetic relevant parameters. Therefore, the concept defines at first variation intervals for the energetic most significant parameters of a forging process chain. After that, the resulting technical/technological modifications are evaluated energetically. To enable a holistic optimization of the process chain, the approach includes the use of a simulation model. The application of the concept has been approved with a simulation model of a 4-cylinder-crankshaft process chain. With the parameter variations “reduction of the forging temperature”, “reduction of the raw part volume” and “reduction of the forging time” three possibilities to reduce the energy consumption were identified successfully.

Integrative process chain optimization using a Genetic Algorithm

For the production of forged components, it is necessary to coordinate and optimize the production stages along the process chain. This includes the mainstream processes as well as the associated process chain of the die manufacturing. Up to now, these processes and process chains are planned and optimized independent from each other because of the different and often contradictory target criteria. In this paper, a new approach for a holistic optimization of forged process chains will be presented. At first, a systematic mathematical dependency-analysis between the processes of an application scenario was carried out. Based on this analysis, a holistic Pareto-based optimization of the process parameters by the use of a Genetic Algorithm was consecutively performed. The article ends with the presentation and discussion of the computational results.

Deep Drawing Simulations Based on Microstructural Data

AbstractFor the optimization of process chains in sheet metal forming it is required to accurately describe each partial process of the chain, e.g. rolling, press hardening and deep drawing. The prediction of the thickness distribution and the residual stresses in the blank has to be of high reliability, since the subsequent behavior of the semi‐finished product in the following subprocesses strongly depends on the process history. Therefore, high‐quality simulations have to be carried out which incorporate real microstructural data [1,2,3]. In this contribution, the ferritic steel DC04 is analyzed. A finite strain crystal plasticity model is used, for the application of which micro pillar compression tests were carried out experimentally and numerically to identify the material parameters of DC04. For the validation of the model, a two‐dimensional EBSD data set has been discretized by finite elements and subjected to homogeneous displacement boundary conditions describing a large strain uniaxial tensile test. The results have been compared to experimental measurements of the specimen after the tensile test. Furthermore, a deep drawing process is simulated, which is based on a two‐scale Taylor‐type model at the integration points of the finite elements. At each integration point, the initial texture data given by the aforementioned EBSD measurements is assigned to the model. By applying this method, we predict the earing profiles of differently textured sheet metals. (© 2010 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Water footprint analysis for the assessment of milk production in Brandenburg (Germany)

Abstract. The working group "Adaptation to Climate Change" at the Leibniz-Institute for Agricultural Engineering Potsdam-Bornim (ATB) is introduced. This group calculates the water footprint for agricultural processes and farms, distinguished into green water footprint, blue water footprint, and dilution water footprint. The green and blue water demand of a dairy farm plays a pivotal role in the regional water balance. Considering already existing and forthcoming climate change effects there is a need to determine the water cycle in the field and in housing for process chain optimisation for the adaptation to an expected increasing water scarcity. Resulting investments to boost water productivity and to improve water use efficiency in milk production are two pathways to adapt to climate change effects. In this paper the calculation of blue water demand for dairy farming in Brandenburg (Germany) is presented. The water used for feeding, milk processing, and servicing of cows over the time period of ten years was assessed in our study. The preliminary results of the calculation of the direct blue water footprint shows a decreasing water demand in the dairy production from the year 1999 with 5.98×109 L/yr to a water demand of 5.00×109 L/yr in the year 2008 in Brandenburg because of decreasing animal numbers and an improved average milk yield per cow. Improved feeding practices and shifted breeding to greater-volume producing Holstein-Friesian cow allow the production of milk in a more water sustainable way. The mean blue water consumption for the production of 1 kg milk in the time period between 1999 to 2008 was 3.94±0.29 L. The main part of the consumed water seems to stem from indirect used green water for the production of feed for the cows.

A Simulation Environment for Hierarchical Process Chains Based on OMNeT++

OMNeT++ is a discrete event simulation environment primarily designed for communication networks. In this paper we present an approach to enable OMNeT++ to simulate complex hierarchical process chains. Process chains are a common modeling paradigm in the logistics area for analysis and optimization, and have been used intensely in many practical applications. Their evaluation is supported by the ProC/B toolset, a collection of software tools for modeling, analysis, validation and optimization of process chains. Here we describe how OMNeT++ has been integrated as a new simulation engine into the toolset. The integration has to overcome some core problems to allow a smooth interaction between OMNeT++ and the other tools: in particular, the OMNeT++ model description of the logistics network should be kept manageable, it should reflect the entire model structure and non-standard performance figures, being relevant for an economic evaluation should be ascertainable in order to satisfy the specific needs of the application area. This paper highlights the main steps of the automatic transformation of a hierarchical process chain model into a hierarchical model in OMNeT++. Furthermore, we show how the transformation has been validated and how detailed performance figures can be evaluated with OMNeT++.

An algorithm implementation for tool wear of HSC

Manufacturing enterprises face competitive pressure. High Speed Cutting (HSC) offers the most appropriate opportunity for reduction of run times. Knowledge and experience are necessary to manage machine tools, and special cutting strategies are required for HSC. Tool wear estimation aids tool automatic management, product quality improvement and process chain optimisation. This paper presents an algorithm to predict tool wear before cutting and discusses the mechanism of wear formation calculation. The simulation model of continuous chips is presented to calculate chip sections that contain key parameters of wear prediction. An improved algorithm is also presented for better calculation efficiency.

Methodology for Dimensioning Technological Interfaces of Manufacturing Process Chains

The paper introduces a methodology for process chain planning that is based on the optimisation of technological interfaces and uses multi-criteria optimisation techniques. The manufacturing of helical car-gearings including precision forging technology was investigated as a reference for validation. An employment of modern materials and technologies is often not sufficient for an optimisation of entire process chains. In the future, technological and computational integration of alternative production technologies will be decisive for long-term success. This integration can be seen as an important step towards sustainable process chain optimisation. Having this in mind, the positioning of technological interfaces has been addressed by scientists at Hannover University.

Hybrid, AI- and simulation-supported optimisation of process chains and production plants

The paper describes a novel approach for generating multipurpose models of machining operations, combining machine learning and search techniques. A block-oriented framework for modelling and optimisation of process chains is introduced and its applicability is shown by the results of the optimisation of cutting processes. The paper illustrates how the framework can support the simulation-based optimisation of whole production plants. The benefits of substituting the time-consuming simulation by ANN models are also outlined. The applicability of the proposed solution is demonstrated by the results of an industrial project where the task was to optimise the size spectrum of the ordered raw material at a plant producing one- and multi-layered printed wires.