Cutting Machine Tool Research Articles

Energy-exergy analysis and co-optimization of compressed cold air-cooling system for dry cutting machine tools

Energy-exergy analysis and co-optimization of compressed cold air-cooling system for dry cutting machine tools

Design and Analysis of 5-DOF Compact Electromagnetic Levitation Actuator for Lens Control of Laser Cutting Machine.

In laser beam processing, the angle or offset between the auxiliary gas and the laser beam axis have been proved to be two new process optimization parameters for improving cutting speed and quality. However, a traditional electromechanical actuator cannot achieve high-speed and high-precision motion control with a compact structure. This paper proposes a magnetic levitation actuator which could realize the 5-DOF motion control of a lens using six groups of differential electromagnets. At first, the nonlinear characteristic of a magnetic driving force was analyzed by establishing an analytical model and finite element calculation. Then, the dynamic model of the magnetic levitation actuator was established using the Taylor series. And the mathematical relationship between the detected distance and five-degree-of-freedom was determined. Next, the centralized control system based on PID control was designed. Finally, a driving test was carried out to verify the five-degrees-of-freedom motion of the proposed electromagnetic levitation actuator. The results show it can achieve a stable levitation and precision positioning with a desired command motion. It also proves that the proposed magnetic levitation actuator has the potential application in an off-axis laser cutting machine tool.

The machine tool industry of Russia at a time of war and sanctions

ABSTRACT The article is devoted to an analysis of the development of the Russian machine tool industry before and after Russia’ invasion of Ukraine in February 2022. It reviews the history of the industry in the USSR, its collapse in the 1990s and stabilisation in the early 2000s. Prior to Russian’s annexation of Crimea in 2014 and the imposition of sanctions by Western countries the government had already adopted policies to secure a revival of the industry on a more independent basis and some success was achieved. After 2014 the government’s efforts to restore the industry intensified and the scale of output of metal cutting machine tools steadily increased. However, there was still a significant dependence on imports, especially of the most advanced types of equipment. After the start of the war in 2022 sanctions were rapidly intensified and more countries participated. Quite rapidly, imports from Western countries were replaced by imports from China and other non-sanctioning countries. The development of the domestic industry received new priority and output increased. By the end of 2023 the situation had to some extent stabilised. Notwithstanding sanctions, the Russian defence industry has been able to acquire the machine tools it needs, although not always of the highest quality.

CONSIDERATIONS FOR THE PARAMETERISATION OF A CHATTER DETECTION ALGORITHM

Process instabilites due to the regenerative effect limit the material removal rate of cutting machine tools. A method developed at the Laboratory for Machine Tools and Production Engineering (WZL) at RWTH Aachen University reduces the experimental effort of determining stability limits for milling. The method bears on a time-domain chatter detection algorithm, which detects process instability based on predefined threshold values. Up to now, the definition of the threshold values has been derived from a selective number of sample tests. To investigate the transferability of the chatter detection algorithm to various boundary conditions cutting tests are carried out. In addition, a new stability criterion for high frequency chatter oscillations is introduced. The results are analysed with regard to factors that require an adjustment of the threshold values. Based on the results, a parameterisation of the chatter detection algorithm is presented.

Algorithm design and optimization of profile cutting problem based on 0-1 knapsack algorithm

According to a one-dimensional multi-objective profile cutting machine tool and based on the principle of 0-1 knapsack algorithm and MATLAB software programming, the length of the two kinds of type and a limited number of profiles are processed to solve the maximum utilization of the profile cutting scheme, and optimize the programming algorithm for solving profile maximum cutting length, from two aspects of time complexity and space complexity analysis and comparison. Firstly, the principle of the 0-1 knapsack algorithm is used to design the algorithm to get the maximum cutting length of the corresponding two lengths. Secondly, according to the number of profiles, cutting margin and other parameters are used to select the length type of profiles. Then, the cutting size sequence of each selected profile is introduced, and the length type, quantity, cutting size sequence group, total cutting margin, profile utilization rate, and other parameters are calculated when the processing contract is completed. Finally, based on the recursive formula of jump point sequence, the algorithm is optimized to eliminate the restriction of 0-1 knapsack algorithm by integer constraints, reduce the time and space complexity of the algorithm, reduce the calculation amount and storage space of the algorithm, and improve the efficiency of the programming algorithm.

INFLUENCE OF HEAT GENERATED IN A ROLLING BEARING ON ITS MOTION RESISTANCE

The heat generated in a bearing may affect its resistance to motion. Therefore, the bearing seat designer should take this into account in the design process. The article presents the results of simulation tests which show how the heat in the bearing affects its resistance to motion. A methodology for determining the resistance to motion and power losses in the bearing has been proposed, which consists of two stages: identification of the temperature distribution in the bearing and identification of thermal deformation and contact loads. The main results of the research are the following statements: that the thermal deformation of the bearing elements is much greater than that due to mechanical loads (centrifugal forces or pre-load) and the contact loads, thus the motion resistance and power losses in the transient state may be much greater than in the state fixed. The conducted experimental studies show a relatively good qualitative agreement with the results of simulation tests, while the quantitative results do not always provide a sufficient agreement. The most important conclusion from the research is as follows: for high-speed assemblies, e.g. spindles of High Speed Cutting machine tools, the failure to take into account the motion resistance due to heat generated in the bearing may lead to a reduction in bearing life.

Optimal design of the dynamic performance of the ultra-precision fly cutting machine tool

Optimal design of the dynamic performance of the ultra-precision fly cutting machine tool

Virtual Sensor for Accuracy Monitoring in CNC Machines

Vibrations are limiting the productivity and the process quality of cutting machine tools. For the monitoring of these vibrations, often external sensors, such as acceleration sensors, are used. These external systems require additional cost and maintenance effort. This paper presents a virtual sensor, which is capable of detecting vibrations at the tool center point, based on internal machine data. External sensors are only necessary once for model identification. This reduces the overall cost of the system significantly. The virtual sensor uses the high-quality data of the linear position encoder near the ball screw nut and calculates the vibrations at the tool tip by using transmissibility functions. This paper explains the theory behind the used transmissibility functions and describes how they are measured, by comparing different experimental approaches to identify the modal parameters of cutting machine tools. After the identification of the sensor, a dynamical test cycle is used to prove the physical correctness.

A Comparative Analysis of the Dynamic Strength Properties of the Long Guides of Intelligent Machines for a New Method of the Thermal Spraying of Polymer Concrete

The possibility of using polymer concrete for metal cutting machine beds is analyzed. A comparison of the structures of the machine beds made of polymer concrete and cast iron is made. The frequency of the body of the machine beds made of polymer concrete and cast iron is determined. An analysis of the stress–strain state under static loads is carried out. To increase resistance to wear, it is proposed to spray polymer concrete frames are proposed by the gas–thermal method. The installation of thermal spraying for guide machine beds is given. The optimal parameters for spraying the guides of the machine beds made of polymer concrete are established calculated using the finite element method for the guide beds of metal cutting machine tools using polymer concrete with gaseous coating. The manufacture of the foundation bed from polymer concrete increases the vibration resistance of the machine by 1.4 times. At the same time, the metal consumption of metal cutting machines will decrease by 60%. To increase the wear resistance of the frame guides, it is proposed to use thermal spraying with certain modes typical for polymer concrete. The installation of thermal spraying for bed guides is given. Calculating using the finite element method for the guide beds of metal cutting machines showed that the use of polymer concrete with a wear-resistant coating is justified.

Optimization research based on local exhaust of oil mist particles in cutting machine tool

Oil particles generated from metalworking fluids(MWFs) in machining process can lead serious health problem to operator. Local exhaust hood is an effective engineering method to capture oil particles and other contamination which is wildly used in manufacture workshop. In this paper, exhaust hood capture efficiency with various height, air volume and particle size was gotten by Computational Fluid Dynamic(CFD) technology. Though further analysis of the CFD result, feature air velocity was introduced. Then an equation of feature velocity and capture efficiency was established by multi regression method. According to this equation one improvement solution was studied: Set to flexible enclosure for up exhaust hood. The solution raised particle capture efficiency on each size significant, the result is equivalent to low down up exhaust hood for 60cm.

Software-based setpoint optimization methods for laser cutting machine tools

The demands on laser cutting machine tools are increasing constantly. In this paper, we focus on increasing productivity while minimizing the deterioration of part quality. Without specific countermeasures, increasing the dynamic settings leads to larger dynamic-induced contour errors of the workpiece. Software-based methods offer potential to improve the contour tracking under varying dynamic settings without the need for a mechanical redesign of the machine tool. Four such methods, which rely on different combinations of model- and/or sensor-based setpoint compensation, are implemented and tested on a laser cutting machine tool. A comparison with respect to productivity and contour accuracy of cut parts is presented, and the complexity of development as well as deployment for production are discussed.

MACHINE LEARNING BASED IDENTIFICATION OF ENERGY EFFICIENCY MEASURES FOR MACHINE TOOLS USING LOAD PROFILES AND MACHINE SPECIFIC META DATA

Approaches to detect energy efficiency measures are associated with time consuming analysis requiring expertise. Against this background, this paper presents an expert system to identify potentials for improving the energy efficiency of metal cutting machine tools based on measurement and meta data of 35 machines. For this purpose, it is necessary to determine energy states of machine tools and control strategies of their support units. Therefore, unsupervised and supervised learning algorithms are applied and evaluated. Based on energy states, control strategies and descriptive statistics, performance indicators are developed for enabling automatic selection and prioritization of application-dependent efficiency measures.

MACHINE LEARNING BASED IDENTIFICATION OF ENERGY EFFICIENCY MEASURES FOR MACHINE TOOLS USING LOAD PROFILES AND MACHINE SPECIFIC META DATA

Approaches to detect energy efficiency measures are associated with time consuming analysis requiring expertise. Against this background, this paper presents an expert system to identify potentials for improving the energy efficiency of metal cutting machine tools based on measurement and meta data of 35 machines. For this purpose, it is necessary to determine energy states of machine tools and control strategies of their support units. Therefore, unsupervised and supervised learning algorithms are applied and evaluated. Based on energy states, control strategies and descriptive statistics, performance indicators are developed for enabling automatic selection and prioritization of application-dependent efficiency measures.

A DEEP LEARNING APPROACH TO ELECTRIC LOAD FORECASTING OF MACHINE TOOLS

The ongoing climate change and increasingly strict climate goals of the European Union demand decisive action in all sectors. Especially in manufacturing industry, demand response measures have a high potential to balance the industrial electricity consumption with the increasingly volatile electricity supply from renewable sources. This work aims to develop a method to forecast the electrical energy demand of metal cutting machine tools as a necessary input for implementing demand response measures in factories. Building on the results of a previous study, long short-term memory networks (LSTM) and convolutional neural networks (CNN) are examined in their performance for forecasting the electric load of a machine tool for a 100 second time horizon. The results show that especially the combination of CNN and LSTM in a deep learning approach generates accurate and robust time series forecasts with reduced feature preparation effort. To further improve the forecasting accuracy, different network architectures including an attention mechanism for the LSTMs and different hyperparameter combinations are evaluated. The results are validated on real production data obtained in the ETA Research Factory.

Research and Design of Automatic Chip Removal Model Based on Cutting Machine

Aiming at the widespread environmental pollution problem of cutting machine tools, an automatic chip removal model platform is designed based on solid works. The model platform has the functions of automatic collection of waste chips and automatic chip removal through the air guide groove. A new type of cutting seam is designed to prevent the scraps from being taken out by the cutter, and remove the scraps adhered on the cutter due to high temperature. The inner loop gas path is designed, which is composed of two air inlets and 180 degree evenly distributed air outlets, forming a unique annular gas path. The gas path relies on the annular gas path, which blows away the waste chips during the cutting work of the machine tool, and enters the inner cavity of the machine tool through the new cutting seam. The automatic chip removal model platform is based on the chip removal of the machine tool itself, which can improve the chip removal efficiency of the waste and reduce the scratches on the surface of the material.

Spindle Error Movements and Their Measurement

The spindle of a machine tool is an important component of the machine. Its condition affects not only operation of the machine tool but also, above all, the quality of the workpiece. This paper explains the terminology and describes the research methods used to measure the spindle error movements of cutting machine tools. This article explains the impact of spindle motion inaccuracies on the quality of workpieces, quotes the requirements of European and American standards, and describes the current state and directions of development of the presented methods. Our own research work in this field is also presented.

Coaching Small Industries Kerupuk Kawasan Wisata Ujungpangkah Kabupaten Gresik

Raw materials for making crackers in the form of fish are very abundant in this village and most people make a living as fishermen and fish pond farming. Fish used as raw material for making crackers are payus fish and laosan. The cracker industry produced by most of the craftsmen in Ujung Pangkah sub-district, Gresik Regency, the majority are sold in the form of 'raw crackers' at very cheap prices, simple packaging, less attractive shapes and marketed in the local area, so the product quality must be improved. Fish crackers are crackers whose ingredients consist of a mixture of flour and fish meat. Many types of fish can be used as a basis for making crackers. Payus fish (Elops hawaiensis) is one of the predatory fish species of small fish and crustaceans in ponds. The existence of this fish is quite abundant so that it can be used as a product that can be consumed by the community. Coaching assistance and in the form of providing manual dough cutting machine tools accelerate the production process and production cost efficiency. Assistance with the production process has been carried out through quality control, micro marketing (Instragram and Facebook), financial management reporting through microfinance accounting processes.

Pick capacity model for cutting machine tools stored in a next generation Vertical Storage Machine

Tools for a group of cutting machine tools in a workshop are often centrally stored, maintained and prepared for use. Using a vertical lift machine (VLM) allows orderly and space-saving storage. It is expected that the total time to prepare tools for delivery in the workshop depends on the chosen VLM architecture and on the strategy for organising the tools in the VLM.This research proposes a mathematical model for determining the total preparation time as a function of the number of items ordered for a series of jobs in the workshop. The model is specific applied for machine cutting tools. While previous research focussed on storage strategies for carrousel storage machines or the basic VLM architecture, this research focusses on five VLM architectures that can be combined with three storage strategies in the mathematical model. The model is largely parametric, allowing for specific numerical values as applicable to the workshop being calculated and allowing to use the technical performance parameters of the VLM.The model illustrates that both the choice of the designated VLM architecture and the choice of storage strategy can have a significant impact on the total time needed to prepare the tools. For both choices to be made, no general rule emerges: a calculation with the specific numerical values for the workshop concerned is appropriate.

Machine learning based identification of energy states of metal cutting machine tools using load profiles

In order to quantify energy efficiency potentials of metal cutting machine tools, it is necessary to determine the time shares of different energy states. This paper presents a machine learning approach analyzing energy states, developed according to the acrfullCRISPDM, to improve the accuracy of the time study compared to static approaches. Different concepts, such as Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM), are deployed and evaluated based on electrical load profiles from an industrial use case with 35 metal cutting machine tools, where both approaches achieve an accuracy of over 95 %.

Hybrid virtual metering points – a low-cost, near real-time energy and resource flow monitoring approach for production machines without PLC data connection

Transparent energy flows within a factory are the prerequisite for energetic improvements of the involved production machines. With the ongoing digitalization of industrial production, innovative ways of creating energy transparency on the shop floor are emerging. Virtual energy metering points predict the power consumption of a regarded entity and can therefore enable a cost-effective increase in energy transparency on machine level. However, many machines, especially in small and medium-sized enterprises (SMEs), have no external data connection, which prevents the use of data-based energy prediction models. In this paper, a near real-time deployable approach to predict the current energy consumption of production machines without a programmable logic controller (PLC) data connection is presented. By using a Raspberry Pi as low-cost edge analytics device, its integrated camera films the optical signals from light-emitting diodes (LEDs) of different PLC modules, which display the switching state signals of various machine sub-units. In a next step, the filmed PLC information is translated into state signals, which are correlated with temporarily measured electric energy data of the production machine as well as its principal sub-units. After an automated model training and hyperparameter optimization process, the empirical black box model is deployed in a near real-time environment on the Raspberry Pi. Thus, a hybrid virtual energy and resource flow metering point of the production machine as well as its sub-units is generated. In addition, challenges like model training for predicting different production processes as well as the necessary data set size for VMP model generation are addressed. The approach is tested and validated for a metal cutting machine tool and a cleaning machine of the ETA Research Factory at the Technical University of Darmstadt.