NURBS Interpolator Based on Dynamic Property of the Machine Tool

2008 ◽  
Vol 392-394 ◽  
pp. 355-360
Author(s):  
H.Y. Shen ◽  
Jian Zhong Fu ◽  
Zi Chen Chen
Keyword(s):  
Machine Tool ◽  
Dynamic Property ◽  
Machine Tools ◽  
High Efficiency ◽  
Simulation Experiment ◽  
Machining Parameters ◽  
Nurbs Interpolator ◽  
Look Ahead ◽  
Trace Back

The proposed Axis-based Look-ahead NURBS Interpolator (ALANI) strictly confines the component acceleration and jerk at each axis during every interpolating period based on the mechanics of the machine tools, so that the acceleration and jerk at axes can not exceed the limit in order to avoid immoderate vibrations and shocks. Contour precision is also strictly guaranteed while interpolating. The interpolator can trace back and recalculate previous data if necessary after forwards calculation and verification. And the recalculation algorithm is able to choose optimized machining parameters to attain high efficiency. The introduction of ALANI and simulation experiment is present in this paper.

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.

scholarly journals A Study on Proposal of Flank Wear Criterion by Using a Built-in Current Sensor when Manufacturing the Mold Materials in a Smart Machine Tool

2018 ◽  
Vol 207 ◽  
pp. 03007
Author(s):  
Seung-Yub Baek ◽  
Sung-Taek Jung ◽  
Dae-Yu Park
Keyword(s):  
Tool Wear ◽  
Machine Tool ◽  
Machine Tools ◽  
High Efficiency ◽  
Flank Wear ◽  
Acoustic Emissions ◽  
Measuring Method ◽  
Current Signal ◽  
Flank Face ◽  
Smart Machine

Recently, it has been increased with respect to the safe and reliable operations in industry of machine tools and intelligent of the machine tool has consistently been developing in term of an unmanned manufacturing. For such realization, diagnosis monitoring of machining must be carried out while being processed in real-time. When tool wear is reached to criteria of flank wear and crater wear, the tools must be changed to new tools for improving the manless rate of operation. However, time of tool change was when spark generated because of wear about 0.3 mm on a flank face during manufacturing in the field. So, built-in sensor system in a smart machine tool must be necessary for high efficiency unmanned of manufacturing. As mentioned earlier, the various technique for measuring the tool wear was already defined such as sensing of acoustic emissions, vibrations, sounds, currents, cutting force, and other. The representative one of measuring method is current signal, which is used as a representative index of tool state. In this study, we carried out the proposal of tool wear criterion by using built-in wireless current signal system when manufacturing the mold materials of KP-4M and it was investigated via smart machine tools.

scholarly journals An Efficient Ultraprecision Machining System Automating Setting Operations of Roughly Machined Workpiece

2021 ◽  
Vol 5 (1) ◽  
pp. 11
Author(s):  
Meng Xu ◽  
Keiichi Nakamoto ◽  
Yoshimi Takeuchi
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
High Efficiency ◽  
Industrial Robot ◽  
Numerical Control ◽  
Machining Process ◽  
Machining Accuracy ◽  
Ultraprecision Machining ◽  
Target Shape ◽  
Machining System

Ultraprecision machining is required in many advanced fields. To create precise parts for realizing their high performance, the whole machining process is usually conducted on the same ultraprecision machine tool to avoid setting errors by reducing setting operations. However, feed rate is relatively slow and machining efficiency is not so high compared to ordinary machine tools. Thus, the study aims to develop an efficient ultraprecision machining system including an industrial robot to avoid manual setting and to automate the setting operations. In this system, ultraprecision machining is conducted for the workpiece having a shape near the target shape, which is beforehand prepared by ordinary machine tools and is located on the machine table by means of an industrial robot. Since the setting errors of the roughly machined workpiece deteriorate machining accuracy, the differences from the ideal position and attitude are detected with a contact type of on-machine measurement device. Numerical control (NC) data is finally modified to compensate the identified workpiece setting errors to machine the target shape on an ultraprecision machine tool. From the experimental results, it is confirmed that the proposed system has the possibility to reduce time required in ultraprecision machining to create precise parts with high efficiency.

Legacy Machine Monitoring Using Power Signal Analysis

2011 ◽  
Author(s):  
Amit Deshpande ◽  
Ron Pieper
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
Signal Analysis ◽  
Material Handling ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Machining Parameters ◽  
Software Application ◽  
Tool Monitoring ◽  
Future Work

A typical manufacturing job shop comprises of legacy machine tools, new (modern) machine tools, material handling devices, and peripheral manufacturing equipments. Automated monitoring of legacy machine tools has been a long-standing issue for the manufacturing industry primarily because of the computer numeric controller (CNC) closed architecture and limited external communication functionality. This paper describes a non-invasive methodology and development of a software application to monitor real-time machine status, energy usage, and other machining parameters for a legacy machine tool using power signal analysis. State machine algorithm is implemented to detect tool changes and part count. The system architecture, implementation, benefits, limitations, and future work needed for the legacy machine tool monitoring application is explained in detail.

scholarly journals The look ahead trace back optimizer for genomic selection under transparent and opaque simulators

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fatemeh Amini ◽  
Felipe Restrepo Franco ◽  
Guiping Hu ◽  
Lizhi Wang
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Genomic Prediction ◽  
In Planta ◽  
Data Set ◽  
Look Ahead ◽  
Trace Back ◽  
New Variant ◽  
The Look ◽  
Partially Observable

AbstractRecent advances in genomic selection (GS) have demonstrated the importance of not only the accuracy of genomic prediction but also the intelligence of selection strategies. The look ahead selection algorithm, for example, has been found to significantly outperform the widely used truncation selection approach in terms of genetic gain, thanks to its strategy of selecting breeding parents that may not necessarily be elite themselves but have the best chance of producing elite progeny in the future. This paper presents the look ahead trace back algorithm as a new variant of the look ahead approach, which introduces several improvements to further accelerate genetic gain especially under imperfect genomic prediction. Perhaps an even more significant contribution of this paper is the design of opaque simulators for evaluating the performance of GS algorithms. These simulators are partially observable, explicitly capture both additive and non-additive genetic effects, and simulate uncertain recombination events more realistically. In contrast, most existing GS simulation settings are transparent, either explicitly or implicitly allowing the GS algorithm to exploit certain critical information that may not be possible in actual breeding programs. Comprehensive computational experiments were carried out using a maize data set to compare a variety of GS algorithms under four simulators with different levels of opacity. These results reveal how differently a same GS algorithm would interact with different simulators, suggesting the need for continued research in the design of more realistic simulators. As long as GS algorithms continue to be trained in silico rather than in planta, the best way to avoid disappointing discrepancy between their simulated and actual performances may be to make the simulator as akin to the complex and opaque nature as possible.

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.

Application of Ubiquitous Sensor Network in Collection and Analysis of CNC Machine Tool Processing Status Information

2010 ◽  
Vol 455 ◽  
pp. 621-624
Author(s):  
X. Li ◽  
Y.Y. Yu
Keyword(s):  
Machine Tool ◽  
Sensor Network ◽  
Machine Tools ◽  
Thermal Error ◽  
Development Trend ◽  
Cnc Machine Tools ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Status Information ◽  
The Development Trend

Because of the practical requirement of real-time collection and analysis of CNC machine tool processing status information, we discuss the necessity and feasibility of applying ubiquitous sensor network(USN) in CNC machine tools by analyzing the characteristics of ubiquitous sensor network and the development trend of CNC machine tools, and application of machine tool thermal error compensation based on USN is presented.

Efficiency Improvement of Metal Lathing by Using of an Evaluation Technique of Assembly Machine Tools Quality

2016 ◽  
Vol 684 ◽  
pp. 421-428 ◽  
Author(s):  
D.S. Vasilega ◽  
M.S. Ostapenko
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
Quality Evaluation ◽  
Composite Index ◽  
Production Operation ◽  
Evaluation Technique ◽  
Efficiency Improvement ◽  
Index Of Quality

They defined conditions of use, calculated a composite index of quality for different tools, chose a machine tool according to its quality evaluation, calculated efficiency of processing by tools with different parameters for a certain production operation.

New Development in High Efficiency Deep Grinding

2012 ◽  
Author(s):  
Andre D. L. Batako ◽  
Valery V. Kuzin ◽  
Brian Rowe
Keyword(s):  
Machine Tool ◽  
High Efficiency ◽  
Machining Process ◽  
Depth Of Cut ◽  
Removal Rates ◽  
Workpiece Surface ◽  
New Development ◽  
Cutting Processes ◽  
Selection Of ◽  
Made In

High Efficiency Deep Grinding (HEDG) has been known to secure high removal rates in grinding processes at high wheel speed, relatively large depth of cut and moderately high work speed. High removal rates in HEDG are associated with very efficient grinding and secure very low specific energy comparable to conventional cutting processes. Though there exist HEDG-enabled machine tools, the wide spread of HEDG has been very limited due to the requirement for the machine tool and process design to ensure workpiece surface integrity. HEDG is an aggressive machining process that requires an adequate selection of grinding parameters in order to be successful within a given machine tool and workpiece configuration. This paper presents progress made in the development of a specialised HEDG machine. Results of HEDG processes obtained from the designed machine tool are presented to illustrate achievable high specific removal rates. Specific grinding energies are shown alongside with measured contact arc temperatures. An enhanced single-pole thermocouple technique was used to measure the actual contact temperatures in deep cutting. The performance of conventional wheels is depicted together with the performance of a CBN wheel obtained from actual industrial tests.

A conceptual framework for product service system design for machine tools

2016 ◽  
Vol 23 (5) ◽  
pp. 1227-1248 ◽  
Author(s):  
Pankaj U. Zine ◽  
Makarand S Kulkarni ◽  
Arun K. Ray ◽  
Rakesh Chawla
Keyword(s):  
Machine Tool ◽  
Conceptual Framework ◽  
Machine Tools ◽  
Business Models ◽  
Service System ◽  
Product Service System ◽  
Content Type ◽  
Business Context ◽  
Product Service ◽  
Key Issues

Purpose – The purpose of this paper is to propose a conceptual framework for product service system (PSS) design for machine tools and discuss the PSS implementation issues focusing on the Indian machine tool business sector. Design/methodology/approach – The paper opted for an exploratory survey conducted in the Indian machine tool sector including 39 in-depth interviews with employees of different organizations representing middle and senior management having decision-making authority. It also involves proposing a framework to address the stakeholder’s requirements for services that offers foundation for PSS designers. Findings – The paper helps get an insights about key issues for PSS implementation by the Indian machine tool sector. The hybrid PSS model proposed in the paper can address the stakeholder’s requirements for flexibility in business models through different business phases. Practical implications – The paper offers suggestions for the development of PSS for machine tools for designers and identify issues to be considered particularly in Indian machine tools business context. Originality/value – This paper provides an insight to judge the feasibility of PSS concept for machine tools in Indian context and offers framework for PSS designers.

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