Integrated Process Simulation and Die Design in Sheet Metal Forming

During the recent 10-15 years, Computer Aided Process Planning and Die Design evolved as one of the most important engineering tools in sheet metal forming, particularly in the automotive industry. This emerging role is strongly emphasized by the rapid development of Finite Element Modelling, as well. The purpose of this paper is to give a general overview about the recent achievements in this very important field of sheet metal forming and to introduce some special results in this development activity. Therefore, in this paper, an integrated process simulation and die design system developed at the University of Miskolc, Department of Mechanical Engineering will be analysed. The proposed integrated solutions have great practical importance to improve the global competitiveness of sheet metal forming in the very important segment of industry. The concept described in this paper may have specific value both for process planning and die design engineers

Recent Achievements in Numerical Simulation in Sheet Metal Forming Processes

Purpose of this paper: During the recent 10-15 years, Computer Aided Process Planning and Die Design evolved as one of the most important engineering tools in sheet metal forming, particularly in the automotive industry. This emerging role is strongly emphasized by the rapid development of Finite Element Modelling, as well. The purpose of this paper is to give a general overview about the recent achievements in this very important field of sheet metal forming and to introduce some special results in this development activity. Design/methodology/approach: Concerning the CAE activities in sheet metal forming, there are two main approaches: one of them may be regarded as knowledge based process planning, whilst the other as simulation based process planning. The author attempts to integrate these two separate developments in knowledge and simulation based approach by linking commercial CAD and FEM systems. Findings: Applying the above approach a more powerful and efficient process planning and die design solution can be achieved radically reducing the time and cost of product development cycle and improving product quality. Research limitations: Due to the different modelling approaches in CAD and FEM systems, the biggest challenge is to enhance the robustness of data exchange capabilities between various systems to provide an even more streamlined information flow. Practical implications: The proposed integrated solutions have great practical importance to improve the global competitiveness of sheet metal forming in the very important segment of industry. Originality/value: The concept described in this paper may have specific value both for process planning and die design engineers

TOWARDS ADAPTIVE ENTERPRISES USING DIGITAL TWINS

Modern enterprises are large complex systems operating in highly dynamic environments thus requiring quick response to a variety of change drivers. Moreover, they are systems of systems wherein understanding is available in localized contexts only and that too is typically partial and uncertain. With the overall system behaviour hard to know a-priori and conventional techniques for system-wide analysis either lacking in rigour or defeated by the scale of the problem, the current practice often exclusively relies on human expertise for monitoring and adaptation. We present an approach that combines ideas from modeling & simulation, reinforcement learning and control theory to make enterprises adaptive. The approach hinges on the concept of Digital Twin - a set of relevant models that are amenable to analysis and simulation. The paper describes illustration of approach in two real world use cases

Beyond the Big Leave: The Future of U.S. Automotive Human Resources

Based on industry interviews and trends analyses, forecasts employment levels and hiring nationwide and in Michigan through 2016, and compiles automakers' input on technical needs, hiring criteria, and suggestions for training and education curricula

Modelling, simulation and optimization of the materials flow of a multi-product assembling plant

Abstract: Various dynamic factors impact the movement of materials within a manufacturing environment, increasingly becoming complex for multi-product assembling plants owing to the multiplicity and interconnectedness of these factors. Analyzing these factors can be equally complex, requiring modelling and simulation tools. This paper looks at the modelling and simulation of the materials flow of a multi-product furniture assembling plant to develop an efficient system that accomplishes timely product deliveries at minimal cost. Generic simulation models based on 2 products were developed and constructed using Arena® Simulation Software. Following the simulation experiments and implementation of the results, the average hourly throughput was significantly increased and additional space to store materials prior to processing at workstations was created. The generic models were compatible with the company's other products and hence useful for the company’s production planning and scheduling

IMPROVING OF MANUFACTURING PRODUCTIVITY THROUGH SIMULATION

Improvement of manufacturing system is must do process due to development of manufacturing technology and increase in customer needs. Due to development of technology, companies need to do improvement of their current system in order to survive in competition. This study will analyse overall productivity and identified critical process that consider bottleneck. This study also will quantify impact of batch capacity in manufacturing productivity. Computer aided simulation software will be used as main method. Data of manufacturing system will be collected and will be used as input in simulation software.. Altering several parameters such as machines quantity and batch size helps author to studied final output. It helps author reduce time to do trial for new design as simulation software will done based on real time and system performance will be address to help improvise new design. Simulation also can be applied at both the justification phase and design phase. By using this method, critical area can be identified in manufacturing system and explore several solution based on different scenario

Manufacturing System Energy Modeling and Optimization

World energy consumption has continued increasing in recent years. As a major consumer, industrial activities uses about one third of the energy over the last few decades. In the US, automotive manufacturing plants spends millions of dollars on energy. Meanwhile, due to the high energy price and the high correlation between the energy and environment, manufacturers are facing competing pressure from profit, long term brand image, and environmental policies. Thus, it is critical to understand the energy usage and optimize the operation to achieve the best overall objective. This research will establish systematic energy models, forecast energy demands, and optimize the supply systems in manufacturing plants. A combined temporal and organizational framework for manufacturing is studied to drive energy model establishment. Guided by the framework, an automotive manufacturing plant in the post-process phase is used to implement the systematic modeling approach. By comparing with current studies, the systematic approach is shown to be advantageous in terms of amount of information included, feasibility to be applied, ability to identify the potential conservations, and accuracy. This systematic approach also identifies key influential variables for time series analysis. Comparing with traditional time series models, the models informed by manufacturing features are proved to be more accurate in forecasting and more robust to sudden changes. The 16 step-ahead forecast MSE (mean square error) is improved from 16% to 1.54%. In addition, the time series analysis also detects the increasing trend, weekly, and annual seasonality in the energy consumption. Energy demand forecasting is essential to production management and supply stability. Manufacturing plant on-site energy conversion and transmission systems can schedule the optimal strategy according the demand forecasting and optimization criteria. This research shows that the criteria of energy, monetary cost, and environmental emission are three main optimization criteria that are inconsistent in optimal operations. In the studied case, comparing to cost-oriented optimization, energy optimal operation costs 35% more to run the on-site supply system. While the monetary cost optimal operation uses 17% more energy than the energy-oriented operation. Therefore, the research shows that the optimal operation strategy does not only depends on the high/low level energy price and demand, but also relies on decision makers’ preferences. It provides not a point solution to energy use in manufacturing, but instead valuable information for decision making. This research complements the current knowledge gaps in systematic modeling of manufacturing energy use, consumption forecasting, and supply optimization. It increases the understanding of energy usage in the manufacturing system and improves the awareness of the importance of energy conservation and environmental protection