Effect of Temperature and Pressure on the Thickness Mode Resonant Spectra of Piezoelectric Ceramic

 HELLOOO……. all my fellow interested readers, Wondering what the topic means and if will you be able to understand this topic, which I agree sounds kind of complicated but worry not this blog will help you understand what the research paper is all about.
It will help people interested in it to get an in-site in this topic and understand it with ease
 So, before we start with the topic
Let’s first understand some important terms in this paper

IMPORTANT TERMINOLOGIES

Let’s understand Piezoelectric ceramic materials

Lead zirconate titanate (PZT), barium titanate (BT), and strontium titanate (ST) are the most widely used piezoelectric ceramic materials. where ε is the relative permittivity or dielectric constant of piezoelectric ceramic materials and is defined as the dielectric displacement per unit electric field.

What is an Acoustic Transducer?

An acoustic transducer is an electrical device that converts sound wave vibrations into mechanical or electrical energy.

As many of you must’ve guessed Piezoelectric ceramics are used to make modern acoustic transducers.

Here we discuss the Effect of Temperature and Pressure on the Thickness Mode Resonant Spectra of Piezoelectric Ceramic

Thickness mode : the standing wave patterns produced are called "modes". When a piezoelectric material is excited by an ac signal two series of resonance are observed. One is in radial mode and other is in thickness mode. The frequency in thickness mode(axial) has high resonance because piezoelectric material is designed to be driven in that direction.

Resonant spectra are emission spectrums resulting from the illumination of a substance by radiation of a definite frequency or definite frequencies.

(And of course, resonance is a phenomenon when the matching vibrations of another object increase the amplitude of an object's oscillations)

The most commonly used piezoelectric material is PZT (Lead zirconate titanate)

This is the structure of PZT for reference

Now that some of the important terms used in a research paper are discussed with an understanding of what the paper discusses

THEORY

Here we use the Van Dyke circuit model that’s recommended by the IEEE standard on piezoelectricity.

Now let’s understand The van Dyke first

Van Dyke model, four real circuit parameters Co, C1, L1 and R1 represent the impedance of the piezoelectric resonator at resonance

The branch L1 , R1 , C1 represents the mechanical behaviour of the piezoelectric disc while C0 represents the electric nature.

For the above equivalent circuit

 The resonant frequency (fr)      

                   

The anti-resonant frequency(fa)

And the C1 is represented by

The characterization of piezoelectric ceramics is done using the resonance method as mentioned in the IEEE standard on piezoelectricity. A small ac electrical signal is used to excite an elastic wave in the piezoelectric via an electromechanical coupling. Depending on the sample dimensions, the frequency at which resonance occurs is observed by measuring the impedance of the sample at various frequencies.

EXPERIMENTAL ARRANGEMENTS

The discs used here had dimensions of, a diameter of 25 mm, a thickness of 2 mm, and a diameter to thickness ratio of 12.5.

And as we know scientific experiments are a little extravagant, the flat surfaces used in the sample are coated with silver electrodes to facilitate an ohmic contact for measuring electric properties

What is ohmic contact?  just think about this for a little while the answer is in the name itself Ohmic contact is a low resistance junction (non-rectifying) that provides current conduction from metal to semiconductor and vice versa. The current here increases /decreases linearly Theoretically!!!

Let’s have a look at the experiment and understand the context of the paper through the experiment conducted for the paper

As can be seen, below given fig A temperature-controlled water bath is used here for studying the effect of change in temperature on the resonant and anti-resonant frequencies. The temperature preferred is in the range of 5⁰ C to 40⁰C.

First Resonant and anti-resonant frequencies are observed at different temperatures with sufficient soaking, this process is repeated 10 times and then average values are computed, and average values are calculated to eliminate errors.

For open channel flow metering, the transducer is located at a maximum of 10m below the water surface. The head pressure acting on the transducer affects its resonant frequency. A height of 10 m of water creates a head pressure of approximate 1 kg.cm^2.

Weights calibrated in terms of pressure were used for simulating the head pressure acting on the transducers, and the weight to be applied on the disc was calibrated using the maximum pressure and the area of the disc.

The pre-calibrated weights are applied to the piezoelectric disc and the values of resonant and anti-resonant frequencies are observed using the circuit shown in the fig above.

RESULTS

Based on these practically obtained model parameters, mathematical modelling using an equivalent circuit approach was implemented in Simulink.

Check what’s Simulink after reading this blog

Below fig is what Simulink does i.e., make a relevant equivalent circuit of the piezoelectric element.

Check the below graphs to understand the results that were obtained.

The resonant frequency is inversely proportional to the thickness of the piezoelectric disc. As pressure is applied to the piezoelectric element, the thickness decreases and so there is an increase in the resonant frequency. As the pressure acting on the piezoelectric disc increases, the stiffness increases and so the resonant frequency increases. The values of resonant and anti-resonance frequencies obtained from the model response match with those obtained experimentally.

CONCLUSION

In this paper, a process condition-based equivalent circuit model of the piezoelectric disk is developed. The process conditions and their ranges considered are suitable for underwater applications. Here the required data is experimentally obtained and the model parameters were computed and a more realistic model was obtained.

 The values of resonant and anti-resonant frequencies of the model response match with the experimentally obtained values for given temperature and pressure conditions. This shows that the model parameters computed are accurate. This work can be further extended to compute other material constants which can be used to develop Finite element analysis-based models of piezoelectric discs.

Credit:

Pratik Nagre (TY Mechanical)

(Team R&D)

References:

https://www.researchgate.net/publication/319599612_Effect_of_Temperature_and_Pressure_on_the_Thickness_Mode_Resonant_Spectra_of_Piezoelectric_Ceramic

https://engineering.stackexchange.com/questions/1883/what-is-radial-and-thickness-mode-vibration-in-a-piezo-electric-ceramic-disc-tra

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COMPREHENSIVE EVALUATION OF INNOVATIVE MULTI ROTOR WIND TURBINE DESIGNS

 

INTRODUCTION:

The excessive use of petrol and diesel is depleting the Earth of its resources. These are non-renewable resources which once exhausted will pose a huge conundrum for us. To overcome this, our scientists have been working towards alternative solutions for centuries. One of the most promising course of action is using wind energy. Scientist have had positive developments in this field and are still working to improve it. Wind is a clean and inexhaustible source of energy which was sought out after World War II. Use of wind energy tackles many prominent problems such as limitation of fossil fuels, global warming, green-house effect, etc. Wind power has easily become the most effective option of energy in many countries. According to European Wind Energy Association, approximately, 56% of electrical needs of Ireland and 46.2% of Denmark’s is being met wind power. As for India, according to Hossain, the total onshore wind potential of India is around 2000GW-3000GW. Wind energy has become one of the most recognized forms of renewable source of energy. Wind turbines, especially, the three bladed turbines are very popularized today. Now, researchers are working on ways to improve the technology and make it more innovative. One such area of focus is the multi rotor wind turbines.

METHODOLOGY:

Use of conventional horizontal axis wind turbine has worked for us for a long time but newer and more innovative methods are taking up the field today. Changes made in the conventional turbines like increase in diameter of the rotor increases the blade weight which in turn reduces the power output. This affects the economy drastically. To solve such problems, multi rotor wind turbines are being sought out. This technology promises the idea of achieving higher capacities per tower as compared to the conventional turbines. Multi rotor wind turbines can be distinguished into coplanar multi rotor or array wind turbines, counter rotating wind turbines and coaxial wind turbines.

COUNTER-ROTATING WIND TURBINES:

In 2004 Jung S N, No T S and Ryu K W developed a 30kW counter-rotating wind turbine system. Counter rotating wind turbines use two rotors and thus produce higher power than a single rotor turbine, depending on the distance between the rotors. There in an increase of about 21% in the power coefficient if the distance between the rotors is halved. Contra-rotation wind energy collection with two rotors, one behind the other, can gain up to 40% more energy from a given swept area as compared with a single rotor. 

COPLANAR WIND TURBINES:

Work on co-planar wind turbines has been going on since early 1930s. German scientist Honnef worked diligently on it till he had to stop due to WWII. In mid-1980s scientist in Netherlands and Dutch collaborated to work on a 300kW turbine which worked effectively for around 15 years. Many more scientists are still working on multi rotor systems.

COAXIAL WIND TURBINES:

On the other hand, use of coaxial, multiple rotors has multiplied the output energy. Researcher Douglas Selsam from the California Energy Commission developed coaxial wind turbines and also designed the Sky Serpent-multi rotor wind turbine. In 2005, Sandip Kale and Dr. S.N. Sapali from College of Engineering Pune, developed coaxial series rotor wind turbine using four rotors of 700mm diameter. The sky serpent uses small rotors to catch more wind for less cost making it feasible and cheap to install. It uses only one-tenth of the conventionally used blade material and still provides the same power output.

          Factors involving the evaluation of multi rotor turbines are-

·      Feasibility

·      Comparison with existing wind turbines

·      Technological advantages

·      Security of expected power performance

·      Cost

·      Reliability

·      Impact of Innovation

Safety 

CONCLUSION:

Assessing the models of multi rotor turbines using the above factors showed that-

While all three multi rotor turbines are feasible, the array turbines have a better price advantage and also have the best review when compared with existing multi rotor designs. The coplanar array turbines are also reliable and have a bright technological advantage. On the other hand, unidirectional coaxial wind turbines are highly reliable and have a higher security of expected performance. Coaxial wind turbines have strong technological aspects and also have a higher innovative impact. Thus, coaxial series wind turbines show the most encouraging results. 

REFERENCES

https://www.researchgate.net/publication/337075928_COMPREHENSIVE_EVALUATION_OF_INNOVATIVE_MULTI_ROTOR_WIND_TURBINE_DESIGNS 

Credits : Bhakti Gujrati(FY Civil )

Team R&D

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Evolution of Thermoelectric beta-FeSi2 Phase by Cryo Milling and Sintering

 

Cryo milling refers to milling processes that take place at very low temperatures. In the powder metallurgical technique, milling is the top to bottom approach utilised to make tiny powder. In order to obtain such a low temperature (below 123K subzero), N2 is used, which assists in maintaining the low temperature. In milling, particles are mixed mechanically, but in the sintering process, particles are mixed chemically. Sintering is a procedure that ensures proper bonding and strength of materials.

Let's take a closer look at thermoelectric materials. This stuff is intriguing and attracting attention from all across the world. possessing the ability to convert heat energy into electrical energy Many instances of energy loss in the form of heat can be found in everyday life. We all know that conserving energy is the best approach to grow it. We can notice the energy shortage all around us in the recent circumstances. The thermoelectric effect is based on the Seebeck principle, and its advantages and applications are as follows:

·       There is no residue or disturbances in material during operation

         Light weight and high efficiency ZT value

·       High conductivity inside crystal and lattice thermal conductivity

 

Let us look at the procedure of the experiment :

 

Results of the experiment showed that the size distribution reduces as milling period increases. Specifically, after 6 h, the particle size range is from nearly 0.15–14 um.Increasing milling period to 6 h gives rise to b-FeSi2 phase formation itself. This indicates that Si atoms disperse into Fe lattice and supersaturated solid solution of BETA-FeSi2 is formed

§  In DTA analysis of sample. During the heating cycle, an exothermic peak is observed at around 596–600C in all the three powders. Broadening of exothermic peak is observed, which extends over around 50C temperature range. This temperature range is believed to indicate eutectoid transformation (a-Fe2- Si5 gives b-FeSi2 + Si) and secondary transformation of e-FeSi + Si gives b-FeSi2. This shows that actual eutectoid reaction temperature i.e. 932C then decreases to 600 C due to Cryo milling.

§  We saw very less beta-Fes₂ in CM4HT6 but in it was more in CM6HT6 with some amount of Si this show that Si atoms have been dispersed into Fe lattice and supersaturated solid solution of beta-FeSi2 has formed

the terms D10, D50, D90 represents respectively the percentage of powder particle whose diameter is smaller than the corresponding observed diameter.

The conclusion obtained from the above result was that because the milling temperature was below the ductile to brittle transition temperature, the power brittle, which made milling easier, the usual b-FeSi2 formation temperature was 932 °C, which had been reduced to roughly 600 C. In CM6, beta-FeS2 production was seen, followed by 6 hours of sintering at 800°C, which revealed significant transformation to beta-FeS2 and traces of silicon. More than 6 hours of milling resulted in agglomeration of FE particles, which acted as Si deprived zones, resulting in the creation of allied phases, which obstruct phase transformation of beta-Fes2 and alter its properties.

 

References :

https://www.researchgate.net/publication/301598459_Evolution_of_Thermoelectric_b-FeSi2_Phase_by_Cryo_Milling_and_Sintering

     

      Credits:

      Om Gawande (TY Metallurgy)

      (Team R&D)

      Note: This blog is meant for educational purposes only. We do not own any Copyrights related to images and information, all the rights go to their respective owners. The sole purpose of this blog is to Educate, Inspire, Empower, and create awareness in the viewers. The usage is non-commercial (Not for Profit) and we do not make any money from it.

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Health prediction of a Multi-point cutting tool using ML

  · INTRODUCTION:

Tool faults have attracted research interest. Tool wear is affected by several factors which influence monitoring cutting tools to track defects and avoid them.

Several approaches are considered like ‘Online/Indirect’, where acquisition of parameters like Cutting Speed, Tool Temperature, Feed Rate, Forces, using suitable Transducers & ‘Off-line/ Direct’, where vision-based techniques like Laser Scatter Pattern, Scanning Electron Microscopy are seen.

The online approach is suitable for knowledge-based ML learning schemes. Several investigations related to the study of Vibration Signals to review Tool Health were given.

· METHODOLOGY:

The diagram shows the methodology used; the tool conditions were categorized using 6 algorithms which are:

1.    Decision tree (J48) classifier- A decision tree serves as an efficient tool in the domain of judgmental investigation. It is a special algorithm that performs a dual role i.e. while constructing the classification model it assists in the selection of attributes. It exhibits a hierarchical pattern of decisions similar to tree

2.    Logistic model (LM) tree classifier- It is a dual-scheme classifier incorporating modelling logistic regression while training decision rule sets. The classic decision tree having constants located at leaves generates a piecewise-constant model, but the LMT constructs a standard structure of the tree using logistic regression (LR) at each leaf, similar to the model tree.

 

3.   Random Forest classifier- A (binary) decision tree is a series of (binary) questions that can be programmatically schematized as nested if-then-else statements and represented as an acyclic graph directed outwards from a root node and having the following properties:

a) Each node can have zero (a leaf) or two descendants (a test node)

b) Each node has one parent, except for a single node called the root which has no parent.

4.    Best First (BF) tree classifier- Best First search algorithm expands the most significant node using heuristic estimation function ‘f(n)’ based on factors like an illustration of ‘n’, the expected outcome, the information gained during the search, and any other additional information about datasets. 

5.   Functional tree classifier- The functional tree studies the influence of features’ fusion at various parts of the decision tree i.e., all types of node points, leaves, etc. 

6.  Simple CART classifier- Classification & Regression Tree is a decision tree-based supervised learning approach for building models in ML. Also, a comparative study was made for finding Best Classifier & Validation was done using k-fold test mode. Tool Configurations were considered, and 6 operations were considered, where we plotted the Number of Samples against the Acceleration.

·  EXPERIMENTATION:

1. The experimental set-up consisted of a Vertical Machining Centre (CNC-VMC), an accelerometer, a data acquisition system.

To be more specific,

• Machine tool: CNC-VMC VTEC-CCS00605 (X = 4200 mm, Y = 2600 mm, Z = 920 mm)

• Work-piece material: Mild Steel

• Work-piece shape: Rectangular hollow cube of form ‘C’

• Workpiece size: Length = 650, Breadth: 250, Height = 100 (all dimensions are in mm)

• Milling tool: Milling cutter of diameter 63 mm

• Number of inserts: 4 (carbide coated)

• Operation performed: Face milling

• Machining parameters:

§  Spindle speed = 900 revolutions per minute

§  Feed = 2000 mm/min

§  Cut Depth = 0.25 mm

§  Data acquisition (DAQ): Vibration analyser (Make DEWE 43 A)

§  Sensor: Piezoelectric accelerometer (Make PCB-J356A43, acceleration: 500 g, sensitivity: 10 mV/g)

2.      Vibration response is generally collected from the location near the dynamic component of machines. Hence, the accelerometer was fixed on the spindle frame, held in a vertical direction as the drive shaft revolves vertically.

3.      The sampler frequency was chosen as 20 kilohertz, considering the Nyquist theorem.

4.      Sampling length of 2048 was chosen since 2¹¹=2048 data points of Acceleration Amplitude Readings for 240 samples i.e., 40 samples in Tool Configuration, and a training data sheet was created.

5.      Thus, Feature Extraction and Feature Selection were implemented.

  

· RESULTS & CONCLUSION:

1)     13 Statistical attributes were extracted namely, (1) Kurtosis, (2) Standard Error, (3) Maximum value, (4) Skewness, (5) Minimum value, (6) Range, (7) Count, (8) Summation, (9) Variance, (10) Standard Deviation, (11) Mode, (12) Median (13) Mean.

2)     Now the classification accuracies were as follows:

a)      J48 Decision Tree- 96.2%

b)     Random Forest (RF)- 96.2%

c)      e Logistic Model Trees (LMT)- 96.2%

d)     Best First Tree (BFT)- 97%

e)     Functional Trees (FT)- 95.4%

f)       Simple CART- 96.2%

3)     The Best First Tree algorithm yields the highest accuracy as 97%, and the time required to build the model is lowest i.e., 0.2 s, hence, endorsing the satisfactory training of the model. The J48, LMT, RF, SC yields the same accuracy as 96.2% and, the FT offers the lowest accuracy as 95.4% amongst all tree family classifiers. The time consumed in constructing the model for LMT is 5.3 s which is the highest in all.

References:

https://www.researchgate.net/publication/345054760_A_machine_learning_approach_for_vibration-based_multipoint_tool_insert_health_prediction_on_Vertical_Machining_Centre_VMC

 https://scholar.google.co.in/citations?user=u4zim9MAAAAJ&hl=en

https://www.sciencedirect.com/science/article/abs/pii/S0263224120311659

https://www.researchgate.net/publication/345054760_A_machine_learning_approach_for_vibration-based_multipoint_tool_insert_health_prediction_on_Vertical_Machining_Centre_VMC

https://scholar.google.co.in/citations?hl=en&user=u4zim9MAAAAJ

https://www.scopus.com/authid/detail.uri?authorId=57204179235

Blog Credits: Vaishnavi Sabale (TY Mechanical) (Team R&D)

Note: This blog is meant for educational purposes only. We do not own any Copyrights related to images and information, all the rights go to their respective owners. The sole purpose of this blog is to Educate, Inspire, Empower, and create awareness in the viewers. The usage is non-commercial (Not for Profit) and we do not make any money from it.

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