Using the Graphical-Analytical Principle to Use Multi-Serving in Operational Management

2012 ◽  
pp. 423-443
Author(s):  
I. C. Dima
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
Analytical Procedure ◽  
Optimal Number ◽  
Cycle Duration ◽  
Minimum Duration ◽  
Working Cycle ◽  
Operational Management ◽  
The Way

When organising the system of polyservicing the machines and equipment, the graphical-analytical procedure is successfully used, which basically emphasises those factors that condition the simultaneous service of several jobs, factors that can be production substitutable and production limitative factors. When the technological operations are identical, they have the same duration and structure, the organisation of polyservice is based on establishing the cyclogram for servicing all machine tools that have been taken into account. If the operations are different, but have the same manufacturing cycle duration, the use of polyservice implies the determination of the optimal number of machines that are to be simultaneously serviced and the preparation of the servicing cyclogram for all simultaneously serviced machines. Should the operations be multiple, but some of them have the same duration and the same cycle, each machine has a different cycle, but in between these cycles, there is a ratio established between the maximum and the minimum duration influencing the polyservice cycle and the number of machines to be possibly serviced. When the machine tools perform different operations, the way to achieve polyservicing is based on determining the polyservice working cycle, taking into account the longest working cycle of the machines and the amount of servicing times of every machine tool. When the machine tools are grouped, by duration of processing operations for various machines, polyservicing several machine-tools at the same, polyservicing cycle is achieved. Regardless of how polyservicing is done, a system of aggregate indicators whose level is calculated based on mathematical formulae is used in order to assess polyservicing.

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.

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.

Considerations on Multi-Service on Workstations

2012 ◽  
pp. 371-397
Author(s):  
I. C. Dima
Keyword(s):  
Mass Production ◽  
Machine Tools ◽  
Optimal Number ◽  
Working Time ◽  
General Distribution ◽  
Serial Production ◽  
Working Cycle ◽  
Technological Structure ◽  
Effective Use ◽  
Production Conditions

Polyservicing the workplaces takes into account the cycle of processing the benchmarks by machine tools and their features and implies a thorough analysis of the technical, organisational, and economic aspects. It is thus intended to efficiently use the machines and machine tools including the worker’s working time. Grouping the processing operations by machine tools will be done depending on the technological structure of each operation, given the use index of the machines, the effective use index of labour, the structure and duration of the operations necessary to make the product, the type of the machine tools used. Polyservicing the machine-tools is featured by a series of parameters: the duration of the working cycle of a machine, the duration of the polyservicing cycle, optimal number of machines that can be services, the coefficient to use the performer’s working time, etc. Combining the operations to be done on various machine tools is based on the types of technological processes and is done separately for manufacturing unique products, serial production, and mass production. Establishing the optimal production conditions for polyservicing can be done by using the theory of “waiting queues” and “Markov chain,” which is based on three elements, namely: input into the process, the servicing mechanism, and the type and way of servicing. Optimising the polyservice of machines can be done using the “Takacs and Runnenburg model,” which basically solves the issue of the general distribution of servicing times and the “method of the mechanisation coefficient,” which takes into account the influence of cost on the number of polyserviced machines.

Experimental Evaluation of the Thermal Machine Tool Behavior for Model Updating

2012 ◽  
Vol 6 (2) ◽  
pp. 125-136 ◽  
Author(s):  
Jörg E. Franke ◽  
◽  
Tobias Maier ◽  
Franziska Schäfer ◽  
Michael F. Zaeh ◽  
...  
Keyword(s):  
Machine Tool ◽  
Experimental Evaluation ◽  
Machine Tools ◽  
Model Updating ◽  
Environmental Parameters ◽  
Simulation Models ◽  
Feed Drives ◽  
Thermal Displacements ◽  
Thermal Influence

Thermally induced deviations are one of the most important issues for modern machine tools’ accuracy. Therefore, the numerical determination of the thermal machine behavior is becoming an essential part of the development process. The thermal models are highly dependent on the applied loads and boundary conditions. The experimental determination of the thermal machine tool behavior is therefore a critical point. Consequently, this paper presents an experimental evaluation of the thermal behavior of machine tools for model updating. In order to identify the thermal machine properties, temperature distributions as well as thermal displacements were detected. The experiments addressed the thermal influence of environmental parameters, the heat generation of main and feed drives and the cutting process. The tests were carried out on two different machine types, a lathe and a milling machine. Specific machining tasks were developed for each analysis to assure realistic load cases. The temperature and displacement measurements presented in this paper provide a strong parameter base for future thermal simulation models.

scholarly journals Universal Synthesizer of Mueller Matrices Based on the Symmetry Properties of the Enpolarizing Ellipsoid

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 983
Author(s):  
José J. Gil ◽  
Ignacio San José
Keyword(s):  
Mathematical Structure ◽  
Mueller Matrix ◽  
Morphological Properties ◽  
Nondestructive Analysis ◽  
Structural Decomposition ◽  
Symmetry Properties ◽  
Mueller Matrices ◽  
Scattering Measurement ◽  
The Way

Polarimetry is today a widely used and powerful tool for nondestructive analysis of the structural and morphological properties of a great variety of material samples, including aerosols and hydrosols, among many others. For each given scattering measurement configuration, absolute Mueller polarimeters provide the most complete polarimetric information, intricately encoded in the 16 parameters of the corresponding Mueller matrix. Thus, the determination of the mathematical structure of the polarimetric information contained in a Mueller matrix constitutes a topic of great interest. In this work, besides a structural decomposition that makes explicit the role played by the diattenuation-polarizance of a general depolarizing medium, a universal synthesizer of Muller matrices is developed. This is based on the concept of an enpolarizing ellipsoid, whose symmetry features are directly linked to the way in which the polarimetric information is organized.

Improved Method for the Determination of Ethylenebisdithiocarbamate Residues in Plants, Fruits, and Vegetables

1967 ◽  
Vol 50 (5) ◽  
pp. 1102-1108
Author(s):  
Charles F Gordon ◽  
Richard J Schuckert ◽  
William E Bornak
Keyword(s):  
Sulfuric Acid ◽  
Fruits And Vegetables ◽  
Analytical Procedure ◽  
Improved Method ◽  
Modified Method ◽  
Fungicide Residues ◽  
Crop Types ◽  
Large Representative

Abstract A modified method for the determination of dithiocarbamate fungicide residues on crops is presented. A large representative subsample of the frozen crop is blended in ice-cold deaerated water and an aliquot of the homogenate is added to the analytical apparatus containing hot 5 0% sulfuric acid. Dithiocarbamates are decomposed to evolve CS2 which is removed by a continuous gentle air-sweep from the digestion flask. Variations in technique allow the analysis of dithiocarbamate fungicide residues in several ranges, 1-10, 10-200, and 200-1000 /ig maneb. Recoveries from a wide variety of crops averaged 70 to 103%. Certain crop types present low recoveries and/or high apparent control values, but modifications in the analytical procedure are successful in solving these problems.

scholarly journals Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Jianlei Zhang ◽  
Yukun Zeng ◽  
Binil Starly
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Recurrent Neural Network ◽  
Machine Tools ◽  
Prediction Performance ◽  
Sequential Data ◽  
Diagnosis And Prognosis ◽  
Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

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