Approach to Forecast Energy Consumption of Machine Tools within the Design Phase

2013 ◽  
Vol 769 ◽  
pp. 278-284 ◽  
Author(s):  
Karl Doreth ◽  
Jan Henjes ◽  
Stefan Kroening
Keyword(s):  
Energy Consumption ◽  
Machine Tool ◽  
Machine Tools ◽  
Production Systems ◽  
Design Phase ◽  
Empirical Information ◽  
Complex Simulation ◽  
Data Feedback ◽  
Acquisition Costs

For environmental and economic reasons, energy- and resource- efficient operations of cutting machines are increasingly important. The determination of properties and functions of machine tools, which affect future energy consumption in operation, essentially takes place within the design phase by combining required components. Therefore, it is necessary to develop approaches to find an efficient optimum between energy consumption, productivity, acquisition costs and operating costs within the design phase of a machine tool. However, the energy consumption of a machine tool depends on the application scenario. In addition to that, it is difficult to forecast the energy consumption of several components because of their mutual interaction. Existing approaches to forecast the energy consumption of a machine tool within design phase are based on complex simulation or mathematical models which are difficult to parameterize for the design of a machine tool and thus, for the comparison of various configuration alternatives. An alternative for forecasting energy consumption is the use of empirical information. That information can be acquired by measuring the energy consumption of machine tools in operating production systems. This paper presents an approach to forecast the energy consumption of machine tools within the design phase, which will be developed by the Institute of Production Engineering and Machine Tools. It will be based on the data feedback (empirical information) from a machine tool operating in an existing manufacturing system. For this purpose, a logger module will be developed, which continually captures the energy consumption by means of the machine integrated sensors. That information will be sent back to an energy navigator module, which processes that information in order to forecast the energy consumption of a new designed machine tool. Also, the lifecycle costs will be calculated in order to rate cost and benefits of each machines lifecycle in terms of energy consumption.

Determination of the Global Sensitivity of the Vibrostability Limit for Improving Machine Tools Dynamics

2002 ◽  
Vol 8 (4) ◽  
pp. 493-502
Author(s):  
K. Marchelek ◽  
B. Powałka
Keyword(s):  
Machine Tool ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Machine Tools ◽  
Global Sensitivity ◽  
Sensitivity Indices

The paper presents a method for determining the global sensitivity indices of the vibrostability limit to the change of mass-damping-spring parameters in machine tool models. The non-stationary character of the models is handled by the analysis of variants. The global sensitivity indices are calculated on the basis of the frequency of variant appearance and the vibrostability limit that corresponds to each variant. To compute the global sensitivity indices fuzzy set theory is applied.

A Digital Twin of Multi-Axis Machine Tool for Micro Process Planning

2020 ◽  
Author(s):  
Fumiki Tanaka
Keyword(s):  
Machine Tool ◽  
Process Planning ◽  
Machine Tools ◽  
High Performance ◽  
Production Systems ◽  
Planning System ◽  
Digital Twin ◽  
Virtual Machining ◽  
Process Planning System ◽  
Case Data

Abstract Achieving high performance of machining production systems requires the use of multi-axis machine tools. In order to maximize the performance of multi-axis machine tools, micro process planning for creating machining data is important. Many researches on micro process planning mainly focused on 3-axis machining. As promising approaches among them, a micro process planning system was proposed that reuses actual machining cases and analyzes case data to derive the necessary rules. However, it is not always effective for multi-axis machining, because enough case data are not collected for micro process planning of a specific multi-axis machine tool. In this study, a digital twin of multi-axis machine tool in cyberspace is proposed to collect real and virtual machining case data for micro process planning.

scholarly journals Regular Neural Network Based Machining Parameters Optimization Model Of NC Machine Tool

2018 ◽  
Vol 232 ◽  
pp. 01006
Author(s):  
Sanping Wang ◽  
Junwen Chen ◽  
Wei Yan
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Machine Tool ◽  
Process Parameters ◽  
Machine Tools ◽  
Machining Process ◽  
Machining Parameters ◽  
Nc Machine Tool ◽  
Nc Machine

Energy consumption process is the basis for energy efficiency improvement of machine tools. Most of the existing researches focus on the static modelling of energy consumption of a machine tool; however, there are a few studies that paid attention to that how process parameters influence the energy consumption of machine tools during processing. It is noted that the process parameters can be selected to reduce energy consumption during machining processes without additional investment. In this paper, a characteristic energy consumption model for NC machine tool was proposed. Then, the mapping rule between process parameters and energy consumption of machine tool was studied, and the model was solved with the regular neural network (RNN). Finally, the result was verified with an experiment of milling the surface of aluminium block, which can effectively improve the energy efficiency of machine tool. The experiment results are shown that regular neural network is used to optimize the process parameters and process the same machining characteristics; we analyze the in machining process of machine tool based on the three cutting parameters, and then, a model of energy consumption. We employ to learn, and use this trained model to select optimal parameters.

Genetic Optimization for the Design of a Machine Tool Slide Table for Reduced Energy Consumption

2021 ◽  
Vol 143 (10) ◽  
Author(s):  
Matthew J. Triebe ◽  
Fu Zhao ◽  
John W. Sutherland
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Energy Consumption ◽  
Machine Tool ◽  
Machine Tools ◽  
Sandwich Panel ◽  
Sustainable Manufacturing ◽  
Element Model ◽  
Genetic Optimization ◽  
Use Phase

Abstract Reducing the energy consumption of machine tools is important from a sustainable manufacturing perspective. Much of a machine tool’s environmental impact comes from the energy it consumes during its use phase. To move elements of a machine tool requires energy, and if the mass of those elements can be reduced, then the required energy would be reduced. Therefore, this paper proposes a genetic algorithm to design lightweight machine tools to reduce their energy consumption. This is specifically applied to optimize the structure of a machine tool slide table, which moves throughout the use of the machine tool, with the goal of reducing its mass without sacrificing its stiffness. The table is envisioned as a sandwich panel, and the proposed genetic algorithm optimizes the core of the sandwich structure while considering both mass and stiffness. A finite element model is used to assess the strength of the proposed designs. Finite element results indicate that the strength of the lightweight tables is comparable with a traditional table design.

Machine Tool Service for Mass-Production Machining Systems

2018 ◽  
Vol 12 (4) ◽  
pp. 507-513
Author(s):  
Makoto Fujishima ◽  
◽  
Takashi Hoshi ◽  
Hiroki Nakahira ◽  
Masafumi Takahashi ◽  
...  
Keyword(s):  
Machine Tool ◽  
Production System ◽  
Mass Production ◽  
Machine Tools ◽  
Production Systems ◽  
Production Volume ◽  
Machining Processes ◽  
In Series ◽  
Tool Service ◽  
Large Production

Mass-production machining systems that are comprised of machine tools are often configured in series by dividing the machining processes in order to manage the large production volume. This indicates that if one of the machines stops owing to a mechanical malfunction, the entire production line needs to be stopped. Thus, machine tools in mass-production systems are required to be highly reliable and easy to maintain. Predictive maintenance, which enables operators to detect any signs of failure in the machine tool components, needs to be performed for the machines as well. In this work, various approaches for the improvement of the maintainability of machine tools used in a mass-production system are reported.

Design, Modeling and Functionality Allocation in Mechatronic Production Systems

2008 ◽  
Author(s):  
Reimund Neugebauer ◽  
Welf-Guntram Drossel ◽  
Steffen Ihlenfeldt ◽  
Markus Wabner
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
Manufacturing Systems ◽  
Metal Cutting ◽  
Production Systems ◽  
Intervention Strategies ◽  
Electronic Unit ◽  
Mechatronic Systems ◽  
Thermal Errors ◽  
Damping Of Vibrations

This paper reviews current developments in mechatronic systems for metal cutting and forming machine tools. The integration of mechatronic modules to the machine tool and their interaction with manufacturing processes are presented. Sample mechatronic components for precision positioning and compensation of static, dynamic and thermal errors are presented as examples. The effect of modular integration of mechatronic systems on the reconfigurability and reliability of the machine tools is discussed along with intervention strategies during machine tool operations. The performance and functionality aspects are discussed through active and passive intervention methods. A special emphasis was placed on active and passive damping of vibrations through piezo, magnetic, and electro-hydraulic actuators. The modular integration of mechatronic components into the machine tool’s structure, electronic unit, and CNC software system is presented. The paper concludes with the current research challenges required to expand the application of mechatronics in machine tools and manufacturing systems.

scholarly journals Toward a Generalized Energy Prediction Model for Machine Tools

2016 ◽  
Vol 139 (4) ◽  
Author(s):  
Raunak Bhinge ◽  
Jinkyoo Park ◽  
Kincho H. Law ◽  
David A. Dornfeld ◽  
Moneer Helu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Machine Tool ◽  
Process Planning ◽  
Machine Tools ◽  
Prediction Models ◽  
Machining Process ◽  
Data Driven ◽  
Process Data ◽  
Energy Prediction

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian process (GP) regression, a nonparametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed by any part of the machine using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

Improving the efficiency of surface-thermal hardening of machine parts in conditions of combination of processing technologies, integrated on a single machine tool base

2021 ◽  
Vol 23 (3) ◽  
pp. 45-71
Author(s):  
Vadim Skeeba ◽  
◽  
Vladimir Ivancivsky ◽  
Keyword(s):  
Surface Layer ◽  
Energy Consumption ◽  
Machine Tool ◽  
Machine Tools ◽  
Energy Sources ◽  
Technological Equipment ◽  
Thermal Hardening ◽  
Processing Technologies ◽  
Machine Parts ◽  
Hybrid Machine

Introduction. In the manufacturing industry, there is a particular interest in the development of a new type of technological equipment, which makes it possible to implement methods for modifying the parts surface layers by processing it with concentrated energy sources. The combination of two processing technologies (mechanical and surface-thermal operations) in the conditions of integrated equipment makes it possible to neutralize the disadvantages of monotechnologies and obtain new effects that are unattainable when using technologies separately. The use of hybrid machine tools in conjunction with the developed technological recommendations will allow achieving a multiple increase in the technical and economic efficiency of production, resource and energy saving, which in turn will contribute to an increase in the competitiveness of products and the renewal of the technological paradigm. Purpose of work: increasing productivity and reducing energy consumption during surface-thermal hardening of machine parts by exposure to concentrated energy sources under conditions of integrated processing. Theory and methods: studies of the possible structural composition and layout of hybrid equipment during the integration of mechanical and surface-thermal processes are carried out taking into account the main provisions of structural synthesis and the components of metalworking systems. Theoretical studies are carried out using the basic provisions of system analysis, geometric theory of surface formation, design of metalworking machines, finite-element method, mathematical and computer simulation. Mathematical simulation of thermal fields and structural-phase transformations in the case of HEH HFC is carried out in the ANSYS and SYSWELD software packages, using numerical methods for solving the differential equations of unsteady thermal conductivity (Fourier's equation), carbon diffusion (Fick's second law), and elastoplastic behavior of the material. The verification of the simulation results is carried out by conducting field experiments using: optical and scanning microscopy; mechanical and X-ray methods for determining residual stresses. In the study, Uone JD520 and Form Talysurf Series 2 profilograph-profilometers are used to simultaneously measure shape deviations, waviness and surface roughness. Surface topography is assessed using a Zygo New View 7300 laser profilograph-profilometer. The microhardness of the hardened surface layer of parts is evaluated on a Wolpert Group 402MVD device. Results and discussion. An original method of structural-kinematic analysis for pre-design research of hybrid metalworking equipment is presented. Methodological recommendations are developed for the modernization of metal-cutting machine tools, the implementation of which will allow the implementation of high-energy heating by high-frequency currents (HEH HFC) on a standard machine-tool system and ensure the formation of high-tech technological equipment with expanded functionality. A unified integral parameter of the temperature-time effect on a structural material is proposed when the modes of hardening by concentrated heating sources are assigned, which guarantee the required set of quality indicators of the surface layer of machine parts, while ensuring energy efficiency and processing productivity in general. It is experimentally confirmed that the introduction into production of the proposed hybrid machine tool in conjunction with the developed recommendations for the purpose of the HEH HFC modes in the conditions of integral processing of a “Plunger bushing” type part in relation to the factory technology allows increasing the productivity of surface hardening by 3.5 ... 4.1 times, and reduce energy consumption by 9.5 ... 11.3 times.

Machine-Specific Estimation of Milling Energy Consumption in Detailed Design

2021 ◽  
pp. 1-24
Author(s):  
Till Boettjer ◽  
Johan Krogshave ◽  
Devarajan Ramanujan
Keyword(s):  
Energy Consumption ◽  
Machine Tool ◽  
Machine Tools ◽  
Design Stage ◽  
Total Energy Consumption ◽  
Estimation Errors ◽  
Workpiece Material ◽  
Detailed Design ◽  
Energy Estimation ◽  
Unit Process

Abstract Manufacturing is a significant contributor to global greenhouse gas emissions and there is an urgent need to reduce the energy consumption of production processes. An important step towards this goal is proactively estimating process energy consumption at the detailed design stage. This is a challenging task as variabilities in factors such as process specifications, machine tool architecture, and workpiece geometry can significantly reduce the accuracy of the estimated energy consumption. This paper discusses a methodology for machine-specific energy estimation in milling processes at the detailed design stage based on the unit process life cycle inventory (UPLCI) model. We develop an adjusted UPLCI model that includes adjustment factors for uncertainties in machine tool specifications and the specific cutting energy of a workpiece material. To validate the adjusted UPLCI model, we conducted a case study that measured the energy consumption for machining three parts made of Aluminum 6082 on two separate three-axis vertical milling machines, a Chevalier QP2040-L and a Leadwell MCV-OP. Results show that the UPLCI model consistently overestimated the total energy consumption for machining the three validation parts across both machine tools. We also found the adjusted UPLCI model significantly reduced the estimation errors for the same tests for both machine tools.

A Simplified Machine-Tool Power-Consumption Measurement Procedure and Methodology for Estimating Total Energy Consumption

2015 ◽  
Vol 138 (5) ◽  
Author(s):  
Jang-Yeob Lee ◽  
Yong-Jun Shin ◽  
Min-Soo Kim ◽  
Eun-Seob Kim ◽  
Hae-Sung Yoon ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Machine Tool ◽  
Total Energy ◽  
Machine Tools ◽  
Measurement Procedure ◽  
Single Model ◽  
Total Energy Consumption ◽  
Cutting Energy ◽  
Energy Consumption Model ◽  
Consumption Model

Various methods have been developed to describe the energy consumption of machine tools; however, it remains challenging to accommodate the wide variety of machine tools that exist using a single model. In this paper we propose a method to model the energy consumption of machine tools by decoupling the energy of the components of the machine tool from the cutting energy. A procedure is developed to describe the characteristics of the energy consumption of machine tools, which is applied to six different machines. The experimental results show that the cutting energy can be decoupled from the component energy. In this manner, a simplified energy consumption model is developed that can be applied to a wide variety of different machine tools.

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