Spindle Motor Research Articles

Life cycle cost analysis of a computerized numerical control machine tool: a case study from Indian manufacturing industry

PurposeLife cycle cost (LCC) analysis is one of the key parameters in designing a sustainable product or system. The application of life cycle costing in the manufacturing industries is still limited due to several factors. Lack of understanding of LCC analysis methodologies is one of the key barriers. This paper presents a generalized framework for LCC analysis of repairable systems using reliability and maintainability principles.Design/methodology/approachThe developed LCC analysis framework and stochastic point processes are applied for the analysis of a typical computerized numerical control turning center (CNCTC) and governing equations for acquisition cost, operation cost, failure cost, support cost and net salvage value are developed. The LCC of the CNCTC is evaluated for the renewal process (RP) and minimal repair process (MRP) approach.FindingsThe LCC analysis of the CNCTC reveals that, the acquisition cost is only 7.59% of the LCC, whereas the operation, failure and support costs dominate and contribute nearly 93% of the LCC. The LCC per day for RP requires additional US$ 1.03 than that for MRP. The detailed LCC analysis of the CNCTC identifies the critical components of CNCTC and these components are: spindle motor, spindle motor cooling fan, spindle belt, drawbar, spindle bearing, oil seals, hydraulic hose, solenoid valve, tool holder, lubrication pump motor system, lubrication hose, coolant pump motor system, coolant hose, supply cables, drive battery.Originality/valueThe developed framework of LCC of a repairable system can be applied to any other repairable systems with the appropriate modifications. LCC analysis of CNCTC reveals that the procurement decision of a product or system should be based on LCC and not only on the acquisition cost. The optimum utilization of consumables such as cutting tools, coolant, oil and lubricant can save operation cost. Thus, use of high-efficiency electric motors and the usage of recommended consumables can prolong the life of several components of a system. Therefore, due consideration and attention to these parameters at product design stage itself will decrease failure and support cost and ultimately its LCC.

ビルトインモータスピンドルの最適冷却条件に関する研究 第1報：ビルトインモータスピンドルの温度変化シミュレーション

ビルトインモータスピンドルの最適冷却条件に関する研究 第1報：ビルトインモータスピンドルの温度変化シミュレーション

Features and Characteristics of Motorized and Non Motorized Milling Spindles

The Cutting process used in milling is one of the most common type of industrial machining methods. Similar to traditional milling spindles, the motor driven spindles are fitted with an integrated motor, thereby eliminating belts and gears for the transmission of power from the motor to the cutting tools. The innovative machine tools should be highly characterized systems in order to retain the necessary precision, efficiency and reliability. To satisfy their end user's reliability and availability requirements, both the spindle system (Tool/Tool-Holder/ Spindle) and motor tool system need to be configured for their usability and output results. However, the quality of a control device in industrial practice is greatly affected by the spindle cutting output and its reliability. The motor spindles are nothing but the rotating drive shafts which acts as axes for cutting force tools or in machining process for holding cutting instrument. Hence the spindles are one of the important factor in machining tool process and productivity, as these are used to produce parts as well as machines that produce components, which in turn have a significant impact on production levels and quality of products.

Prospects of Upgrade of the Main Drives of CNC Machine Tools Using Mechatronic Modules

This article observes the principles of construction and operation of the belt drives with detached electric motor and the drives with motor spindle. It also describes the characteristics of the elements used in spindles. In choosing a spindle, one proceeds from the primary and secondary requirements for the machine tool: rotation speed, power output, hardness, workspace dimensions, design complexity, application area, option of repair and replacement, and cost. The article considers the advantages and disadvantages of the spindles with additional mechanic gears and the motor spindles. As an example, we compare variants of upgrade of the «16M30F3» machine tool’s main drive. As a result, the following conclusions were made: motor spindle has more advantages than standard drive, but it is way more expensive and its repair is more difficult. Hence, this spindle type is only applicable to machine tools with high standards of quality and performance. Belt-driven spindle is special for easy repair and a wide range of technical specifications: one can choose multiple motor variants for one drive; again, it is much cheaper than motor spindle. However, final decision on spindle should concern the main requirements for the machine tool, workplace dimensions, application of the machine tool and, possibly, financial restrictions.
 Keywords: CNC machine tool, motor spindle, mechatronics, machine building, electric drive, electric motor, motion control, built-in motor.

Effects of elastic stockings on peripheral and central nervous system.

Prospective study to investigate the effects of elastic stockings (GCS) 23-32 mmHg at ankle on Hoffmann reflex (H-reflex) from soleus muscle under rest and after a walking program. Fourteen subjects wore two types of GCS, at different times. Electrophysiological examinations were carried out at rest without and with GCS, immediately after walking with GCS and 20 minutes later after removing GCS. Peripheral nerve conduction remained unchanged after using the GCS. Conversely, walking with GCS led to changes in a spinal cord pathway expressed as a decrease of H-threshold and an increase of H-size as a function of stimulus intensity, which lasted for at least 20 minutes. GCS has no effect on the peripheral nervous system. The GCS intolerance and the discomfort sometimes reported by patients do not derive from a dysfunction of the lower limb peripheral nervous system. A positive action on spinal reflex excitability is detected after walking while wearing GCS. We suggest that nervous descending activity due to voluntary contractions, and afferent cutaneous discharge, enhanced by movement under compression, converge on inhibitory interneurons, thus impinging on presynaptic pathways. All this can lead to an enhancement of the monosynaptic responses. Higher limb oxygenation detected during walking with GCS, found by other Authors, could increase the sensitivity of the muscle spindle afferents and/or motor neuron excitability resulting in an increase in H-reflex excitability, with potential positive effects on neuromuscular activities improving proprioception and postural control of the lower limbs.

Investigations on force generation and joint properties of dissimilar thickness friction stir corner welded AA 5086 alloy

The force generation, joint mechanical and metallurgical properties of friction stir corner welded non-heat treatable AA 5086 aluminum alloy are investigated in this paper. The friction stir welding process is carried out with the plate thicknesses of 6 mm and 4 mm. The welding speed, tool rotational speed and tool plunge depth were considered as the process parameters to conduct the welding experiments. The machine spindle motor current consumption and tool down force generation during friction stir welding were analyzed. The microstructures of various joint regions were observed. The tensile samples revealed the tensile strength of 197 MPa with tool rotational and welding speeds of 1,000 rev/min and 150 mm/min respectively, which is 78 % of parent material tensile strength. A maximum micro hardness of 98 HV was observed at thermomechanically joint affected zone, which was welded with tool rotation of 1,000 rev/min and welding speed of 190 mm/min.

Energy Efficiency Modeling for Configuration-Dependent Machining via Machine Learning: A Comparative Study

Energy efficiency modeling is of great importance to energy management and conservation for machinery enterprises. To improve the generalization ability, this article combines the machining parameters and the configuration parameters into energy efficiency models, for which machine-learning (ML) algorithms are used considering the lack of theoretical formulas. Based on the three-year data collected in a shop floor, a comparative study for two different cases is conducted with a particular focus on prediction accuracy, stability, and computational efficiency. In Case 1, only cross-sectional data are used to predict energy efficiency, ignoring the deterioration of spindle motors and cutting tools. Three traditional ML algorithms, i.e., artificial neural networks, support vector regression, and Gaussian process regression, are evaluated with the help of five error metrics. In Case 2, we construct the models in a more realistic situation that considers the dynamic aspects of spindle motor aging and tool wear. A convolutional neural network, a stacked autoencoder, a deep belief network and the aforementioned traditional ML algorithms are investigated. The comparison shows that all the models in Case 1 suffer from performance degradation, while deep learning achieves the long-term improvement in accuracy. Note to Practitioners —Energy efficiency models deliver many advantages, ranging from energy-aware machine design to process optimization. Although a large amount of works in the past focused on physics-based and experimental modeling for specific machining configurations, it can be more effective to improve the applicability of the modeling methods by involving the configuration variables into the models. Due to the uncertainties in both the machine and the operation environment, machine learning is adopted to fit the high-dimensional and high-nonlinear energy system. To the best of our knowledge, this is the first article that provides a comprehensive survey on ML-based modeling in terms of data sizes, temporal granularities, feature selection, and algorithm performance. Such a survey helps engineers quickly justify the appropriate ML methods to meet the actual requirements.

Optimization and prefabrication of timber Voronoi shells

We introduce a variant of the timber grid shell, the timber Voronoi shell, whose surface is reticulated by Voronoi tessellation and whose edges are made of discrete dimensional timber. This work explores form-finding methods of the Voronoi shell as a compressive funicular shell. Two closed-form solutions to shape initialization are proposed. We develop methods for minimizing the deviation from coplanarity between timber members and adjacent surface normal in order to facilitate manufacturing. A well-defined fabrication process is important for making the physical structure consistent with the structural model. A 6-axis robot with a motor spindle is employed to prefabricate the timber so the in situ manual assembly becomes easier. A parametric model describes the joint details. We formulate the robotic toolpath as a closed-form function of the resultant mesh from form finding. Thus, a general-purpose programming language can directly implement the mesh optimization and manufacturing processes without CAD or CAM software. The physical implementations, including an exhibition pavilion, validated the approach.

ZNF367-induced transcriptional activation of KIF15 accelerates the progression of breast cancer

Breast cancer (BC) is one of the most common female cancers, and its incidence has been increasing in recent years. Although treatments are continuously improving, the prognosis of patients in the advanced stage is still unsatisfactory. Thus, an in-depth understanding of its molecular mechanisms is necessary for curing breast cancer. KIF15 is a tetrameric spindle motor which can regulate mitosis in cellular process and exert the crucial functions in several cancers. The purpose of our research was to investigate the functions of KIF15 in breast cancer. We tested the expression of KIF15 in breast cancer tissues and the survival rate of breast cancer patients with high or low level of KIF15 through TCGA data. What's more, western blot and immunohistochemistry assay were utilized to evaluate the protein level and mRNA level of KIF15 in breast cancer tissues. Then CCK-8, wound healing, transwell and flow cytometry experiments were adopted separately to test cell viability, migration, invasion and cell cycle distribution. We discovered that KIF15 was highly expressed in breast cancer tissues and high level KIF15 was associated with a low survival rate of breast cancer patients. Moreover, silence of KIF15 suppressed cell viability, migration, invasion and cell cycle distribution. Following, we discovered that ZNF367 was the upstream transcription factor of KIF15. In addition, silenced ZNF367 could also repress the growth of breast cancer cells. And rescue experiments indicated that overexpressed KIF15 could counteract the inhibition effect of silencing ZNF367 on the progression of breast cancer. Importantly, we discovered that KIF15 and ZNF367 were associated with the regulation of cell cycle. In short, ZNF367-activated KIF15 accelerated the progression of breast cancer by regulating cell cycle progress.

Small Diameter-Deep Hole Drilling for Hard-to-Machine Metals ― Drilling of ϕ1.0×400 mm for Ti6Al4V ―

Small diameter-deep hole drilling is indispensable when several main parts of which airplane, automobile, machine tool and metal mould are manufactured. Especially, medical appliances, medical implants and surgical operation tools for brain and bone invariably need small diameter-deep hole drilling for hard-to-machine metals such as stainless steel and Ti6Al4V. The authors newly designed and manufactured a small diameter-deep hole drilling machine by applying technological developments which consist of a direct drive servo spindle motor controlled more precisely, removable steady rests to prevent for a slender fluted hollow shaft of gun drill to be deflected and a highly pressure circulation system of cutting fluid to prevent being clogged with chips. By using the small diameter-deep hole drilling machine and a special gun drill (specially made to order in BOTEK) a drilling of ϕ1.0×400 mm (L/D=400) for Ti6Al4V was carried out. As a result, it was ascertained that the drilled hole has high accuracy in the items of hole diameter, concentricity, roundness and inner surface roughness.

Digital Twin Approach for Tool Wear Monitoring of Micro-Milling

This paper presents a novel implementation of digital twin (DT) technology in monitoring the wear condition of micro tool (TWM) during micro-milling process. The proposed DT is capable to predict the wear condition of micro tool using electric current data of spindle motor. This minimal setup of the system gives potential benefits for various deployment in real industrial practice. A miniaturized 5-axis micro-milling machine was used in the experiment to perform slot milling in micro-meter level. The proposed DT shows promising result of TCM in micro-milling within Industry 4.0 framework.

Noise source identification of high-speed motion mechanism of textile equipment based on equivalent source method

In order to accurately identify the noise sources of high-end textile equipment and achieve noise control, this paper takes warp knitting machine as an example to accurately locate the main voice mechanism. Firstly, the stability and accuracy of traditional equivalent source method (TRESM) and Bayesian regularization criterion method for acoustic field reconstruction in different frequency bands are simulated and verified. Secondly, the accuracy of iterative reweighted least squares (IRLS) and steepest descent iteration equivalent source method (SDIESM) in medium- and high-frequency bands is verified. The results show that TRESM and Bayesian methods are suitable for the identification of medium- and low-frequency noise sources, and IRLS and SDIESM algorithms have better adaptability to complex sound fields. Bayesian method, IRLS and SDIESM algorithm can be used to identify the noise sources of broadband warp knitting machine. The main noise sources are spindle motor, pulling roller, spindle of loop forming mechanism and push rod of comb bar transverse mechanism, which provide theoretical support for active noise reduction of loom.

Design optimization of lathe spindle system for optimum energy efficiency

Spindle system is the major mechanical component in machine tools, and its performance is responsible for a significant portion of the total consumed energy of machine tools. Conventional design optimization of spindle system is partially focused on parameter optimization of spindle motor or transmission system, contributing to an increase of the motor efficiency. Given that concurrent interactions among them is complex, very little efforts has been done to conduct integration optimization for optimum energy efficiency. To this end, a new approach of spindle system design is presented with consideration of the above two aspects adequately, to achieve the maximum energy and material efficiency. Firstly, the energy characteristic of spindle system is explicitly modelled on the basis of energy flow analysis. Then, a multi-objective optimization model for parameter optimization of spindle motor and transmission system is developed to take the both maximum energy efficiency and minimum volume as objectives, which is subjecting to a set of constraints with related to the cutting parameters boundary, processing requirements and shifting power losses. Finally, a multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm is presented to solve the developed optimization model. The performance of the proposed design approach of lathe spindle system is demonstrated through different working conditions. The experimental results indicate that the design of energy and material efficient machine tools can be achieved.

Magnetic properties of anisotropic bonded NdFeB/SmCo permanent magnets

Anisotropic bonded NdFeB permanent magnets have wide potential applications in the areas such as brushless DC motors, spindle motors, and stepping motors due to their excellent processing property and high magnetic properties. Anisotropic NdFeB bonded magnets prepared by hydrogen-disproportionation–desorption-recombination (HDDR) process have large energy product at room temperature. However, they are not stable at ∼100°C, which is the working temperature of many electric motors. In order to improve their thermal stability, we developed bonded NdFeB/SmCo magnets and investigated the effects of powder particle size and SmCo content on their magnetic properties. We proposed a model to clarify the filling effect of the powder, which played a key role in the enhanced (BH)max when reducing the SmCo particle size. As for the SmCo content, the bonded magnets showed improved (BH)max at 100°C-150°C and thermal stability with the increase of SmCo content. And through adjusting the SmCo content, the remanence temperature coefficient and coercivity temperature coefficient from 25-150°C were able to be regulated in the range of -0.14%/°C to -0.04%/°C and -0.51%/°C to -0.14%/°C, respectively. This work provides a design strategy and a facile route to improve the magnetic properties and thermal stability of the bonded magnets.

A Generalized Multisensor Real-Time Tool Condition–Monitoring Approach Using Deep Recurrent Neural Network

Abstract Tool condition monitoring (TCM) is crucial for manufacturing systems to maximize productivity, maintain part quality, and reduce waste and cost. Available TCM systems mainly depend on data-driven classical machine learning methods to analyze different sensors’ feedback signals for tool condition prediction. Despite their applicability for high process variability and part complexity, they require long development lead time and extensive expert efforts for signal feature definition, extraction, and fusion to accurately detect the tool condition. Additionally, they substantially depend on sensors whose nature is intrusive to the cutting process. Therefore, this research presents a generalized, nonintrusive multisignal fusion approach for real-time tool wear detection in milling that redefines process learning directly from raw signals. In this two-stage approach, the signals’ intrinsic mode functions (IMFs) are extracted, optimized, and directly fused in a deep long short-term memory (LSTM) recurrent neural network (RNN) for tool condition prediction. The IMF extraction and optimization mask the effect of the cutting conditions to accentuate the tool condition effect. Therefore, it generalizes and minimizes the learning process to cover a wider range of unlearned process parameters. Embedded feature architecting of the LSTM-RNN is applied to the optimized IMFs for signal fusion and tool condition prediction to standardize the learning process and significantly minimize the lead time. Spindle motor current, voltage, and power signals are used to avoid process intrusion. A systematic study is carried out to define the optimum LSTM-RNN architecture. Extensive experimental validation results have demonstrated tool wear detection accuracy >95 % at different ranges of unlearned cutting conditions.

HIGH SPEED SYNCHRONOUS RELUCTANCE DRIVES FOR MOTOR SPINDLES

This article presents a new design for setting up high speed synchronous reluctance motors (SynRM) in motor spindle applications for up to 30,000 rpm. The transversal laminated SynRM rotor package is supported by thin steel sheets and a shaft nut to exert a constant axial clamping force. Both, numerical and practical investigations are carried out using FEM-analysis and a test bench for centrifugal force load tests. The objective is to find out at which speed the elastic deformation changes into the plastic deformation. The beginning of plastic deformation is a failure criterion in electric motor construction.

INFLUENCIA DEL NIVEL DE LUBRICACIÓN EN EL TALADRADO DE APILADOS HÍBRIDOS FIBRA DE CARBONO-TITANIO APLICANDO MQL (MÍNIMA CANTIDAD DE LUBRICANTE): MONITORIZACIÓN DEL PROCESO

The purpose of this paper is to analyze the influence of MQL (minimum quantity lubrication) lubrication level on hybrid stacks (Ti/CFRP/Ti) drilling process with diamond coated carbide tools. Spindle and feed motor power consumptions have been monitored which provides valuable information about the operation in real time which can be used to control and optimize the machining process and allows the implementation of 4.0 industry concepts. Furthermore, tool wear has been characterized and hole quality assessed. The tests were done with two lubrication levels with the same machining centers, tools, materials and cutting conditions as in the drilling processes of aeronautical components. With the fresh tool, the power consumptions were very similar for both MQL levels considered. Along the carbon fiber layers, the increase of cutting power with tool wear was higher for the test done with the lower MQL level. However, during Ti layers drilling, the increase of power was smaller for the lower MQL level, probably due to a higher titanium adhesion which modifies the chip formation process and a higher influence of the thermal softening effect on the workpiece. By using a reduced MQL level it was found a smaller tool wear. However, the quality of the holes was similar for the both lubrication levels tested. Keywords: MQL, hybrid stacks drilling, hole quality, tool wear, monitoring

Sensor fusion model for defect identification in friction stir welding process

Defect identification in friction stir welding process is a challenging task as most of the defects formed at subsurface level. The available non destructive approaches are limited only to offline assessment of the defects. In automated environment defect identification methodologies with online monitoring possibilities are gaining significant attention. In this research work sensor fusion based model has been developed for identification of tunnel defect in friction stir welding samples. Real time signal information acquired from tool main spindle motor current signal, rotational speed and vertical force signal have been fused to estimate a quantitative indicator for defect detection within the welded samples. Real time signals are acquired using a force measurement system and a speed sensor with a sampling frequency of 10 kHz. Feature level fusion modeling is incorporated to develop a methodology for defect detection in friction stir welding process. The estimate can be used for identifying defective samples from defect free samples which is advantageous in automated production environment.

Milling tool wear prediction using spindle motor current signal

Milling tool wear prediction using spindle motor current signal

Optimal Design of Rotor Slot Geometry to Reduce Rotor Leakage Reactance and Increase Starting Performance for High-Speed Spindle Motors

This paper proposes optimal designs of three closed rotor slots geometries for reducing the rotor leakage reactance and increasing the starting performance of high-speed spindle motors. The optimal designs were developed using the response surface methodology and Finite Element Method (FEM). Three closed rotor slot geometries, namely closed, round, and oval were investigated through FEM calculation and validated by experimental measurement for improving starting performance parameters such as starting current, starting torque, magnetic flux linkage in starting condition, and efficiency. In this study, a starting performance analysis, including the alternative equivalent circuit method and flux linkage FEM analysis, was employed. The results show that among the three slot geometries, the optimal design of the circular closed rotor slot produces the lowest rotor leakage reactance and the highest magnetic flux linkage, starting torque, and efficiency. A comparison between the simulation and experimental results validated the accuracy of the proposed model with percentage error being approximately 8%.