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

Performance adaptive manufacturing processes

Part of: Seliger, Günther (Ed.): Innovative solutions : proceedings / 11th Global Conference on Sustainable Manufacturing, Berlin, Germany, 23rd - 25th September, 2013. - Berlin: Universitätsverlag der TU Berlin, 2013. - ISBN 978-3-7983-2609-5 (online). - http://nbn-resolving.de/urn:nbn:de:kobv:83-opus4-40276. - pp. 296-301.Energy efficiency is of increasing importance towards sustainable manufacturing in the automotive industry, in particular due to growing environment regulations and rising electricity costs. Approaches within the manufacturing planning phase are insufficient to address dynamic influences during run-time (e.g., electricity tariffs or workload). Additionally, conventional production monitoring and control systems consider the ‘Overall Equipment Effectiveness‘ of manufacturing systems, but do not include related energy efficiency. This paper introduces a novel approach that combines these both aspects and provides more effectiveness based on socalled production variants. The latter are designed during the planning phase and used to adapt manufacturing behavior when facing dynamically changes during run-time. A simulation shows how dynamic adjustments of cycle times lead to a high reduction of energy costs while maintaining high throughputs

Understanding the Theory and Use of Resistive Welding Technology for Fiber-Reinforced Thermoplastic Composite Structures in Automotive Applications

Transportation accounts for 14% of global greenhouse gas emissions. With a projected rise in GDP for more than half of the global population, the demand for transportation is only going to increase sharply. It is essential to reduce the overall weight of the automobile and ensure that its constituent materials are being reused with the minimal energy consumption during treatment and conversion. This is especially critical for the heaviest components in an automobile – its structure and closures. In this regard, carbon fiber reinforced composites have high light-weighting potential for automotive structures. However, most OEMs use thermoset polymers as matrix material, which are not recyclable. This has led to a great push towards the use of thermoplastics as matrix material in the future. A key issue associated with this possibility is the need for an optimal joining mechanism – since while structural adhesives are the most common joining mechanism used at present, most of these adhesives are thermoset polymers themselves that are also expensive and have longer curing time. Additionally, when used with thermoplastic matrix materials, these adhesives bring forth the problem of compatibility. The ability to be joined in fast, strong and repeatable methods is crucial for automotive structures, given that a typical body structure has between 150-400 individual parts, and their timely and strong joining is essential to ensure their applicability for mass production. In this context, the ability to be fusion bonded (or welded) is one of the key advantages of FRTPCs over thermoset composites. Welding thermoplastic reinforced composites can be segregated into three major categories: resistive implant welding (RIW), vibration welding, and electromagnetic welding. Resistive implant welding is an attractive technology due to faster cycle times, lower cost, higher design freedom, and ease of automation. Most research till date primarily focuses on processing and optimizing RIW joints for FRTPCs with high-performance polymer matrix materials that are typically used in aerospace. This dissertation primarily focuses on understanding the processability and optimizing RIW joint for FRTPC materials with engineering-grade polymers. Moreover, research to date also predominantly uses only lap shear strength to characterize these joints. However, this is not enough to adequately understand the mechanical behavior of welded joints. In this dissertation, both lap shear and peel strength were experimentally evaluated, and finite element models were created to simulate these joints under large non-linear loads such as crash tests. This exercise provided in-depth insights into effects on the component-level performance of resistive implant welded structures and their behaviors in large deformation load cases such as crash tests

Aeronautical Engineering: A continuing bibliography, supplement 120

This bibliography contains abstracts for 297 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

Sensor-Based Machine Learning Approach for Indoor Air Quality Monitoring in an Automobile Manufacturing

The alternative control concept using emission from the machine has the potential to reduce energy consumption in HVAC systems. This paper reports on a study of alternative inputs for a control system of HVAC using machine learning algorithms, based on data that are gathered in a welding area of an automotive factory. A data set of CO2, fine dust, temperatures and air velocity was logged using continuous and gravimetric measurements during two typical production weeks. The HVAC system was reduced gradually each day to trigger fluctuations of emission. The data were used to train and test various machine learning models using different statistical indices, consequently to choose a best fit model. Different models were tested and the Long Short-Term Memory model showed the best result, with 0.821 discrepancy on R2. The gravimetric samples proved that the reduction of air exchange rate does not correlate to escalation of fine dust linearly, which means one cannot rely on just gravimetric samples for HVAC system optimization. Furthermore, by using machine learning algorithms, this study shows that by using commonly available low cost sensors in a production hall, it is possible to correlate fine dust data cost effectively and reduce electricity consumption of the HVAC

Energy Reduction of Robot Stations with Uncertainties

This thesis aims to present a practical approach to reducing the energy use of industrial robot stations. The starting point of this work is different types of robot stations and production systems found in the automotive industry, such as welding stations and human-robot collaborative stations, and the aim is to find and verify methods of reducing the energy use in such systems. Practical challenges with this include limited information about the systems, such as energy models of the robots; limited access to the stations, which complicates experiment and data collection; limitations in the robot control system; and a general reluctance by companies to make drastic changes to already tested and approved production systems. Another practical constraint is to reduce energy use without slowing down production. This is especially challenging when a robot station contains stochastic variations, which is the case in many practical applications. Motivated by these challenges, this thesis presents an offline method of reducing the energy use of a production line of welding stations in an automotive factory. The robot stations contain stochastic uncertainties in the form of variations in the robot execution times, and the energy use is reduced by limiting the robot velocities. The method involves collecting data, modeling the system, formulating and solving a nonlinear and stochastic optimization problem, and applying the results to the real robot station. Tests on real stations show that, with only small modifications, the energy use can be reduced significantly, up to 24 percent.The thesis also contains an online method of controlling a collaborative human-robot bin picking station in a robust and energy-optimal way. The problem is partly a scheduling problem to determine in which orders the operations should be executed, and a timing problem to determine the velocities of the robots. A particular challenge is that some model parameters are unknown and have to be estimated online. A multi-layered control algorithm is presented that continuously updates the operation order and tunes the robot velocities as new orders arrive in the system. Simultaneously, a reinforcement learning algorithm is used to update estimates of the unknown parameters to be used in the optimization algorithms

Book of abstracts of the 16th International Symposium of Croatian Metallurgical Society - SHMD \u272023, Materials and metallurgy

Book of abstracts of the 16th International Symposium of Croatian Metallurgical Society - SHMD \u272023, Materials and metallurgy, Zagreb, Croatia, April 20-21, 2023. Abstracts are organized into five sections: Anniversaries of Croatian Metallurgy, Materials - Section A; Process Metallurgy - Section B; Plastic Processing - Section C and Metallurgy and Related Topics - Section D