Happy Thanksgiving 2022!!!

                     

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                   Wish you a very happy and blessed Thanksgiving!

Lupine Publishers| What is Quantum Computing and How it Works, Artificial Intelligence Driven by Quantum Computing

 Lupine Publishers| Modern Approaches on Material Science

What is Quantum Computing and How it Works, Artificial Intelligence Driven by Quantum Computing

Abstract

Companies such as Intel as a pioneer in chip design for computing are pushing the edge of computing from its present Classical Computing generation to the next generation of Quantum Computing. Along the side of Intel corporation, companies such as IBM, Microsoft, and Google are also playing in this domain. The race is on to build the world’s first meaningful quantum computer-one that can deliver the technology’s long-promised ability to help scientists do things like develop miraculous new materials, encrypt data with near-perfect security and accurately predict how Earth’s climate will change. Such a machine is likely more than a decade away, but IBM, Microsoft, Google, Intel, and other tech heavyweights breathlessly tout each tiny, incremental step along the way. Most of these milestones involve packing more quantum bits, or qubits-the basic unit of information in a quantum computer-onto a processor chip ever. But the path to quantum computing involves far more than wrangling subatomic particles. Such computing capabilities are opening a new area into dealing with the massive sheer volume of structured and unstructured data in the form of Big Data, is an excellent augmentation to Artificial Intelligence (AI) and would allow it to thrive to its next generation of Super Artificial Intelligence (SAI) in the near-term time frame.

Keywords: Quantum Computing and Computer, Classical Computing and Computer, Artificial Intelligence, Machine Learning, Deep Learning, Fuzzy Logic, Resilience System, Forecasting and Related Paradigm, Big Data, Commercial and Urban Demand for Electricity

Introduction

Quantum Computing (QC) is designed and structured around the usage of Quantum Mechanical (QM) concepts and phenomena such as superposition and entanglement to perform computation. Computers that perform quantum computation are known as Quantum Computers[1-5].Note that the superposition from a quantum point of view is a fundamental principle of quantum mechanics. The Quantum Superposition (QS) states that, much like waves in Classical Mechanics (CM) or Classical Physics (CP), any two or more quantum states can be added together (“superposed”), and the result will be another valid quantum state; and conversely, that every quantum state can be represented as a sum of two or more other distinct countries.Mathematically, it refers to a property of solutions to the both Schrödinger Time-Dependent and Time-Independent Wave Equations; since the Schrödinger equation is linear, any linear combination of solutions will also be a solution.An example of a physically observable manifestation of the wave nature of quantum systems is the interference peaks from an electron beam in a double-slit experiment, as illustrated in (Figure 1).The pattern is very similar to the one obtained by the diffraction of classical waves. [6]. Quantum computers are believed to be able to solve some computational issues, such as integer factorization, which underlies RSA encryption [7], significantly faster than classical computers. The study of quantum computing is a subfield of quantum information science.

Figure 1: Double-Slit Experiment Setup.

Historically, Classical Computer (CC) technology, as we know them from the past few decades to present, has involved a sequence of changes from one type of physical realization to another, and they have been evolved from main-frame of the old generation to generation of macro-computer. Now, these days, pretty much everyone owns a minicomputer in the form of a laptop, and you find these generations of computers in everyone’s house as part of their household. These mini-computers, Cemeterial Processing Units (CPUs), are based on transistors that are architected around Positive-Negative-Positive (PNP) junction.From gear to relays to valves to transistors to integrated circuits and so on we need automation and consequently augmentation of computer of some sort Today’s advanced lithographic techniques at below sub-micron innovative structure augmenting techniques such as Physical Vapor Deposition (PVD), Chemical Vapor Deposition (CVD), and Chemical Mechanical Polishing (CMP) can create chips with a feature only a fraction of micron wide. Fabricator and manufacturer these chips are pushing them to yield even smaller parts and inevitably reach a point where logic gates are so small that they are made out of only a handful of atoms size, as it is depicted in (Figure 2).

Figure 2: Today’s Chip Fabrication.

It worth mentioning that the size of the chip going way beyond sub-micron technology is limited by the wavelength of the light that is used in the lithographic technique.
On the atomic scale, matter obeys the rules of Quantum Mechanics (QM), which are quite different from Classical Mechanics (CM) or Physics Rules that determine the properties of conventional logic gates. Thus, if computers are to become smaller in the future, new, quantum technology must replace or supplement what we have new as a traditional way of computing.The point is, however, that quantum technology can offer much more than cramming more and more bits onto silicon CPU chip and multiplying the clock-speed of these traditional microprocessors. It can support an entirely new kind of computation with qualitatively new algorithms based on quantum principles! In a nutshell, in Quantum Computing, we deal with Qubits, while in Classical Computing, we deal with bits of information; thus, we need to understand “What Are Qubits?” and how it is defined, which we have presented this matter further down.Next generation of tomorrow’s computer is working based on where “Quantum Bits Compressed for the First Time.” The physicist has now shown how to encode three quantum bits, the kind of data that might be used in this new generation of computer, by just using two photons.Of course, a quantum computer is more than just its processor. These next-generation systems will also need new algorithms, software, interconnects, and several other yet-tobe- invented technologies specifically designed to take advantage of the system’s tremendous processing power-as well as allow the computer’s results to be shared or stored.

Intel introduced a 49-qubit processor code-named “Tangle Lake.” A few years ago, the company created a virtual-testing environment for quantum-computing software; it leverages the powerful “Stampede” supercomputer at The University of Texas at Austin to simulate up to a 42-qubit processor. To understand how to write software for quantum computers, however, they will need to be able to simulate hundreds or even thousands of qubits.Note that: Stampede was one of the most potent and significant supercomputers in the U.S. for open science research. Able to perform nearly ten quadrillion operations per second, Stampede offered opportunities for computational science and technology, ranging from highly parallel algorithms, highthroughput computing, scalable visualization, and next-generation programming languages, as illustrated in (Figure3) here. [8]This Dell PowerEdge cluster equipped with Intel Xeon Phi coprocessors pushed the envelope of computational capabilities, enabling breakthroughs never before imagined. Stampede was funded by the National Science Foundation (NSF) through award ACI-1134872.

Figure 3: Array of Stampede Structure.

Stampede was upgraded in 2016 with additional compute nodes built around the second generation of the Intel Xeon Phi many-core, x86 architecture, known as Knights Landing. The new Xeon Phi’s function as the primary processors in the new system. The upgrade ranked #116 on the June 2016 Top 500 and was the only KNL system on the list.Note that: Knights Landing (KNL) is 2nd Generation of Intel® Xeon Phi™ Processor

What Are Qubits?

A qubit can represent a 0 and 1 at the same time, a uniquely quantum phenomenon known in physics as a superposition. This lets qubits conduct vast numbers of calculations at once, massively increasing computing speed and capacity. But there are different types of qubits, and not all are created equal. In a programmable silicon quantum chip, for example, whether a bit is 1 or a 0 depends on the direction its electron is spinning. Yet all qubits are notoriously fragile, with some requiring temperatures of about 20 millikelvins-250 times colder than deep space-to remain stable. From a physical point of view, a bit is a physical system, which can be prepared in one of the two different states representing two logical values: based on No or Yes, False or True, or simply 0 or 1.Quantum bits, called qubits, are implemented using quantum mechanical two-state systems, as we stated above. These are not confined to their two basic states but can also exist in superposition. This means that the qubit is both in state 0 and state 1, as illustrated in (Figure 4).Any classical register composed of three bits can store in a given moment, only one out of eight different numbers, as illustrated in (Figure 5). A quantum register composed of three qubits can store in a given momentum of time all eight numbers in a quantum superposition, again as illustrated in (Figure5).Once the register is prepared in a superposition of different numbers, one would be able to perform operations on all of them, as demonstrated in (Figure 6)here. Thus, quantum computers can perform many different calculations in parallel. In other words, a system with N qubits can perform 2N calculations at once!

Figure 4: Logical Value Representation.

Figure 5: Classical Vs. Quantum Register.

Figure 6: Quantum Processor Demonstration.

This has impact on the execution time and memory required in the process of computation and determines the efficiency of algorithms. In summary, A memory consisting of N bits of information has 2N possible states. A vector representing all memory states thus has 2N entries (one for each state). This vector is viewed as a probability vector and represents the fact that the memory is to be found in a particular state level. [9]In the classical view, one entry would have a value of 1 (i.e., a 100% probability of being in this state), and all other entries would be zero. In quantum mechanics, probability vectors are generalized to density operators [10]. This is the technically rigorous mathematical foundation for quantum logic gates [11], but the intermediate quantum state vector formalism is usually introduced first because it is conceptually simpler.However, one question arises about Qubits and that is “Why are qubits so fragile?” and here is what we can say.The reality is that the coins, or qubits, eventually stop spinning and collapse into a particular state, whether it’s heads or tails. The goal with quantum computing is to keep them spinning in the superposition of multiple Furthermore, expanding on Quantum Operations, and we stated concerning this operation, the prevailing model of Quantum Computation (QC) describes the computation in terms of a network of Quantum Logic Gates[12].

Bear in mind that, a quantum computing and specifically the quantum circuit model of computation, a quantum logic gate (or merely quantum gate) is a primary quantum circuit operating on a small number of qubits. They are the building blocks of quantum circuits like classical logic gates are for conventional digital circuits [13].States for a long time. Imagine I have a coin spinning on a table, and someone is shaking that table. That might cause the coin to fall over faster. Noise, temperature change, an electrical fluctuation or vibration-all of these things can disturb a qubit’s operation and cause it to lose its data. One way to stabilize certain types of qubits is to keep them very cold. Our qubits operate in a dilution refrigerator that’s about the size of a 55-gallon drum and use a particular isotope of helium to cool them a fraction of a degree above absolute zero (roughly –273 degrees Celsius) [7].There are probably no less than six or seven different types of qubits, and probably three or four of them are being actively considered for use in quantum computing. The differences are in how you manipulate the qubits, and how you get them to talk to one another. You need two qubits to talk to one another to do large “entangled” calculations, and different qubit types have different ways of entangling.Another approach uses the oscillating charges of trapped ions-held in place in a vacuum chamber by laser beams-to function as qubits. Intel is not developing trapped ion systems because they require a deep knowledge of lasers and optics, which is not necessarily suited to our strengths.Furthermore, quantum computers being built by Google, IBM, and others. Another approach uses the oscillating charges of trapped ions-held in place in a vacuum chamber by laser beams-to function as qubits. Intel is not developing trapped ion systems because they require a deep knowledge of lasers and optics, which is not necessarily suited to our strengths.

How Powerful Are Quantum Computers?

For an algorithm to be efficient, the time it will take to execute the algorithm must increase not faster than a Polynomial Function of the size of the input. For example, let us think about the input size as the total number of bits needed to specify the input to the problem, as demonstrated in(Figure 7). The number of bits required to encode the number we want to factorize is an example of this scenario.If the best algorithm we know for a particular problem has the execution time, which can be viewed as a function of the size of the input, bounded by a polynomial, then we say that the problem belongs to Class P, as it is shown in (Figure 7).Problems outside class P are known as hard problems. Thus, we say, for example, that multiplication is in P, whereas factorization is not in P. “Hard” in this case does not mean that it is “impossible to solve” or “noncomputable”. It means that the physical resources needed to factor a large number scale up such that, for all practical purposes, it can be regarded as intractable.However, some quantum algorithms can turn hard mathematical problems into easy ones – factoring being the most striking example as far as we are concerned so far. Such a scenario can be seen in cryptographic technology to be able to decipher a code of cryptic streams or communications. One can see a huge application of it in Rivest, Shamir, and Adelman (RSA) Data Security[8].

Figure 7: Polynomial Function.

The difficulty of factorization underpins the security of what are currently the most trusted methods of public-key encryption, in particular of the RSA system, which is often used to protect electronic bank account, as depicted in (Figure 8).Once a quantum factorization engine, which is a special-purpose quantum computer for a factorizing large number, is built, all such cryptographic systems will become insecure, and they will be wide-open for smart malware to pass through the gate of cybersecurity wall of the system under the attack by this malware.The potential use of quantum factoring for code-breaking purposes has raised the apparent suggestion of building a Quantum Computer (QC), however this not the only application QC. With signs of progress in the utilization of Artificial Intelligence (AI) in the recent decade and pushing toward Super Artificial Intelligence (SAI) to deal with massive sheer of data at Big Data (BD) volume, a need for Quantum Computing versus Classical Computing is felt by the today’s market standard.A supervised AI or SAI with an integrated element of Machine Learning (ML) and Deep Learning (DL) allows us to prosses all of the historical data by comparing them with a collection of incoming data via DL and ML to collect the right information for a proper decision making of the ongoing day-to-day operation and most importantly to be able to forecast future as a paradigm model [14-17].

Figure 8: RSA Data Security Key.

The impact of quantum computing on developing a better artificial intelligence and its next-generation super artificial intelligence can be seen as ongoing technology efforts by scientists and engineers as a common denominator.Typically, the first quantum algorithms that get proposed are for security (such as cryptography) or chemistry and materials modeling. These are problems that are essentially intractable with conventional computers. That said, there are a host of papers as well as start-up companies and university groups working on things like machine learning and AI using quantum computers. Given the time frame for AI development, “I would expect conventional chips optimized specifically for AI algorithms to have more of an impact on the technology than quantum chips,” says Jim Clarke, Intel’s Head of Quantum Computing. Still, AI is undoubtedly a fair game for quantum computing.

How to Build Quantum Computers?

In principle, engines know, how to build a Quantum Computer; we start with simple quantum logic gates as it was described in general previously and connect them up into quantum networks [9] as depicted herein (Figure 9).A Quantum Logic Gate (QLG), like its cousin Classical Logic Gate (CLG), is a straightforward computing device that performs one elementary quantum operation around qubit, usually on two qubits, in a given time. Of course, quantum logic gates differ from their classical counterparts in that they can create and perform operations on quantum superposition as we stated before.As the number of quantum gates in a network increases, we quickly run into some serious practical problems. The more interacting qubits are involved, the harder it tends to be engineering the interaction that would display the quantum properties.The more components there are, the more likely it is that quantum information will spread outside the quantum computer and be lost into the environment, thus spoiling the computation. This process is called decoherence. Therefore, our task is to engineer a sub-microscopic system in which qubits affect each other but not the environment.This usually means isolating the system from its environment as interactions with the external world cause the system to decohere. However, other sources of decoherence also exist. Examples include the quantum gates, and the lattice vibrations and background thermonuclear spin of the physical system used to implement the qubits. Decoherence is irreversible, as it is effectively non-unitary, and is usually something that should be highly controlled, if not avoided.

Figure 9: A Simple Quantum Logic Gates.

Considering such constraint, then it is clear which technology will support quantum computation going forward in the future of this technology. Today simple quantum logic gates involving two qubits, as we said, are being realized and recognized in laboratories as stepping forward. Current experiments range from trapped ions via atoms in an array of potential wells created by a pattern of the crossed laser beam to electrons in semiconductors. See (Figure 10) However, the technology of the next decade should bring control over several qubits and, without any doubt, we shall already begin to benefit from our new way of harnessing nature.There are several models of quantum computing, including the quantum circuit model, quantum Turing machine, adiabatic quantum computer, one-way quantum computer, and various quantum cellular automata. The most widely used model is the quantum circuit. Quantum circuits are based on the quantum bit, or “qubit”, which is somewhat analogous to the bit in classical computation. Qubits can be in a 1 or 0 quantum state, or they can be in a superposition of the 1 and 0 states. However, when qubits are measured, the result is always either a 0 or a 1; the probabilities of these two outcomes depend on the quantum state that they were in immediately before the measurement. Computation is performed by manipulating qubits with quantum logic gates, which are somewhat analogous to classical logic gates[9].There are currently two main approaches to physically implementing a quantum computer: analog and digital. Analog methods are further divided into the quantum simulation, quantum annealing, and adiabatic quantum computation. Digital quantum computers use quantum logic gates to do computation. Both approaches use quantum bits or qubits[10] There are currently several significant obstacles in the way of constructing useful quantum computers. In particular, it is difficult to maintain the quantum states of qubits as they are prone to quantum decoherence, and quantum computers require significant error correction as they are far more prone to errors than classical computers[10].

Figure 10: Patten of Crossed Laser Beam.

Conclusion

The race is on to build the world’s first meaningful quantum computer-one that can deliver the technology’s long-promised ability to help scientists do things like develop miraculous new materials, encrypt data with near-perfect security and accurately predict how Earth’s climate will change. Such a machine is likely more than a decade away, but IBM, Microsoft, Google, Intel, and other tech heavyweights breathlessly tout each tiny, incremental step along the way. Most of these milestones involve packing more quantum bits, or qubits-the basic unit of information in a quantum computer-onto a processor chip ever. But the path to quantum computing involves far more than wrangling subatomic particles. However, the question of “When will we see working Quantum Computer solving real-world problems?” remains to be answered. The first transistor was introduced in 1947. The first integrated circuit followed in 1958. Intel’s first microprocessor-which had only about 2,500 transistors-didn’t arrive until 1971. Each of those milestones was more than a decade apart. People think quantum computers are just around the corner, but history shows these advances take time. If 10 years from now we have a quantum computer that has a few thousand qubits, that would undoubtedly change the world in the same way the first microprocessor did. We and others have been saying it’s ten years away. Some are saying it’s just three years away, and I would argue that they don’t have an understanding of how complex the technology is.
At the end, it is worthy to say that “Any computational problem that can be solved by a classical computer can also, in principle, be solved by a quantum computer. Conversely, quantum computers obey the Church–Turing thesis; that is, any computational problem that can be solved by a quantum computer can also be solved by a classical computer. While this means that quantum computers provide no additional power over classical computers in terms of computability, they do, in theory, provide extra power when it comes to the time complexity of solving certain problems.Notably, quantum computers are believed to be able to quickly solve certain problems that no classical computer could solve in any feasible amount of time-a feat known as “quantum supremacy” [10].The study of the computational complexity of problems concerning quantum computers is known as quantum complexity theory.

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Lupine Publishers| An Alternative Strategy for the Use of a Low-Cost, Age- Hard enable Fe-Si-Ti Steel for Automotive Application

 Lupine Publishers| Modern Approaches On Material Science

Abstract

For High Strength Low Alloy (HSLA) steels or for age-hard enable steels (miraging) the strengthening by precipitation is done before forming operation to increase the yield stress as much as possible. In this publication the advantages of a hardening thermal treatment after forming operation are investigated in a low-cost age-hard enable steel Fe-Si-Ti consistent with automotive application.

Keywords: Age-hard enable, Precipitation, Forming, Ductility

Introduction

The high strength low alloy (HSLA) steels are a group of low carbon steels with small amounts of alloying elements (as Ti, V, Nb, etc.) to obtain a good combination of strength, toughness, and weldability [1-3]. By the addition of micro-alloying elements (less than 0.5 wt.%), HSLA steels are strengthened by grain refinement strengthening, solid solution strengthening and precipitation hardening [4-8]. Regarding automotive applications, the entire range of HSLA steels are suitable for structural components of cars, trucks, and trailers such as suspension systems, chassis, and reinforcement parts. HSLA steels offer good fatigue and impact strengths. Given these characteristics, this class of steels offers weight reduction for reinforcement and structural components [9-11]. Despite the interest of HSLA, the precipitation hardening is about 100MPa. So the new targets concerning the CO₂ emission of vehicles push the steel-makers to develop Advanced High Strength Steels (Dual-Phase, Transformation Induced Plasticity) hardened by multiphase microstructures containing 10 to 100% of martensite [12] which offer a better combination between strength and ductility for an acceptable cost Let’s notice that the Ultimate Tensile Strength (UTS) increases more that the Yield Stress (YS). YS is crucial for anti-intrusive aspect during a crash [12]. In the other side, because there is no phase transformation in Aluminum, age-hardening by precipitation is widely used in Aluminium alloys hardened by various additive elements [see [13] for a review]. The volume fraction of precipitates is several percent’s, the hardening can be increased up to 400MPa, but the ductility decreases rapidly.
In steel, there is only miraging steels which are strengthened by a massive precipitation of a martensitic matrix [see [14] for a review]. Despite the impressive YS (up to 2.5GPa), the uniform elongation is less than 1%. Consequently, no forming operation is possible. In addition, the high contents of nickel (about 18%), of molybdenum and of cobalt make these steels very expensive and so they are never used in automotive industry. On the contrary, in the 60’s authors investigated other kind of steels suitable to be strongly hardened by massive intermetallic precipitation without extracost [15,16]. Among the different investigated system, the Fe-Si-Ti alloys are the most promising. This is the reason why a series of publications has been dedicated to this system in the 70’s by Jack&al. [17-19]. Unfortunately, hardness and compression behaviour have been only reported and a lot of discussions concerned the nature of the precipitates (Fe₂Ti, FeSi, or Fe2SiTi) have been reported. More recently a systematic study of precipitation kinetics in a Fe_-2.5%Si- 1%Ti alloy in the temperature range 723 K to 853 K (450°C to 580°C), combining complementary tools transmission electron microscopy (TEM), atom probe tomography (APT), and Small-Angle-Neutron Scattering (SANS)) have been carried out [20]. It has been shown that the Heusler phase Fe2SiTi dominates the precipitation process in the investigated time and temperature range, regardless of the details of the initial temperature history.

Figure 1: Summary of the different strategies for the use of precipitation hardened steels: The green arrow is the usual one, the orange is the strategy developed in this publication.

Considering that the ductility decreases regularly as a function of the hardening up to 1.2GPa and because it is targeted to obtain a steel suitable for deep drawing the strategy showed in (Figure 1). (orange arrow) has been investigated. The objective is to form a material as soft as possible and as ductile as possible and to obtain the hardening by a thermal treatment after forming. This publication presents the characterization of this way to manage the situation. It is noticed that as Si and Ti promote ferrite, there is no phase transformation whatever the thermal treatment. (Figure 1) Summary of the different strategies for the use of precipitation hardened steels: The green arrow is the usual one, the orange is the strategy developed in this publication.

Results

In order to assess for the first time in steel for automotive applications, the hardening have been chosen followed a thermal treatment at 500°C for two hours consistent with the kinetic determined by SANS [20]. As shown in (Figure 2). illustrating the tensile behavior of the steel consisting only in solid solution (i.e. only after recrystallization) is very ductile as for IF steels but with a higher yield stress due to solid solution hardening. After treatment and hardening of 300MPa is obtained with promising ductility. As shown in (Figure 3) the treatment has induced the expected massive precipitation of Fe2SiTi (3.8% weight percent with a radius of 4nm determined by TEM and SANS [20]). To assess the metallurgical route, the (Figure 4). shows that it is possible to severely bend the steel consisting only in solid solution without any defect up to an angle of 180°. Hardness trough the thickness has been measured before and after the thermal treatment (Figure 5). The value confirms the hardening by precipitation. It is noticed that tis precipitation hardening is not sensitive to the strain hardening induced by bending trough the thickness. Because the alloy is dedicated to automotive industry, the drawing must be investigated. This the reason why a cup obtained by deep drawing of 5cm diameter has been manufactured using the steel consisting in solid solution without any problem as illustrated in (Figure 6). It confirms the very high ductility before heat treatment. One another problem in automotive industry is the increase in spring-back with an increase in strength. In addition, it is very difficult to predict or to model this phenomenon. As shown in (Figure 7). this aspect has been studied by a standard test base the forming of a hat-shaped part. It is highlighted that the spring-back during the treatment is weak. That is probably because there is no phase transformation during precipitation and so no internal stresses.

Figure 2: Tensile curves before and after the ageing treatment.

Figure 3: TEM micrography showing the massive precipitation of Fe₂SiTi after 2 hours at 500°C (the composition have been determined by APT [20]).

Figure 4: Fully bent specimen before heat treatment (bending angle of 180°).

Figure 5: Hardness measurement trough the thickness of the fully bent specimen (i.e. angle of 180°) before and after heat treatment.

Figure 6: Cup drawing before the hardening heat treatment (5cm diameter).

Figure 7: Evaluation of the effect of heat treatment on the spring-back.

Conclusion

For the first time in steel industry for automotive industry a low cost age-hard enable steel has been studied following a strategy based on forming operations before the heat treatment. The bending, the drawability and the spring-back have been investigated highlighting promising results. In addition, the alloy exhibits a cost acceptable for automotive industry. In the future crashworthiness and weldability should be assessed after heat treatment. One of the last advantages is that a lot of parts can be treated at the same time in a furnace usually dedicated to tempering treatment.

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Lupine Publishers| Strength Improvement and Interface Characteristic of Dissimilar Metal Joints for TC4 Ti Alloy to Nitinol NiTi alloy

 Lupine Publishers| Modern Approaches On Material Science

Abstract

Laser welding of TC4 Ti alloy to NiTi alloy has been applied using pure Cu as an interlayer. Mechanical properties of the joints were evaluated by tensile tests. Based on avoiding the formation of Ti-Ni intermetallics in the joint, three welding processes for Ti alloy-NiTi alloy joint were introduced. The joint was formed while the laser was acted on the Cu interlayer. Experimental results showed that Cu interlayer was helping to decrease the Ti-Ni intermetallics by forming Ti-Cu phases in the weld. The average tensile strength of the joint was 216 MPa.

Keywords: Ti alloy; NiTi alloy; Cu interlayer; Laser welding; Microstructure; Tensile strength

Introduction

TiNi alloy has shape memory and pseudo-elastic properties, excellent corrosion resistance and good biocompatibility, it provides promising solutions to solve the problems in various applications such as aerospace, atomic energy, microelectronics, and medical equipment [1,2]. As we all know, the successful application of any advanced material depends not only on its original properties, but also on its development [3]. People are more and more interested in the combination of TiNi alloy and other materials, especially for the development of devices with different mechanical properties and corrosion resistance. Ti alloy has excellent comprehensive properties, such as high specific strength, high specific modulus, hardness, corrosion resistance and high damage resistance [4,5]. It is widely used in aerospace, marine industry, biomedical engineering, and military industry. The composite materials of TiNi alloy and Ti alloy can not only meet the requirements of heat conduction, conductivity, and corrosion resistance, but also meet the requirements of high strength but light weight [6]. Therefore, it will be widely used in aerospace, instrumentation, electronics, chemical industry, and other fields. Compared with single material property, this material can use the performance and cost advantages of each material to select the best material for each structural component [7]. However, the weldability of dissimilar materials also limits the wide application of these alloys. This leads to the formation of brittle-like intermetallic compounds (IMCs) in the weld zone. For example, Ti2Ni, NiTi, Ni3Ti [8]. The formation of Ti-Ni IMCs in the weld makes the weld brittle, and the mismatch of the thermal expansion coefficient of the two materials, it will lead to the formation of transverse cracks in the weld and the deterioration of mechanical properties [9-11]. In fact, TiNi alloy-Ti alloy joint is one of the most direct and effective methods to increase the use of TiNi alloy, Ti alloy and other lightweight materials in the field of aerospace and engineering manufacturing and to use structural lightweight design to achieve structural optimization, energy saving, environmental protection and safety [12]. Therefore, the effective connection between TiNi alloy and Ti alloy becomes an urgent problem.

At present, the most commonly used method is to insert an intermediate layer to improve the microstructure of the joint, which can improve the mechanical stability between TiNi alloy and Ti alloy and lead to the formation of other phases except for Ti-Ni IMCs [13]. This is because the addition of intermediate layer can reduce the fusion ratio of TiNi alloy and Ti alloy in the joint. This effect reduces the content of Ti and Ni in the weld metal, thus reducing the probability of the formation of Ti-Ni IMCs in the weld metal [14,15]. Elements such as niobium, zirconium, molybdenum, tantalum, and vanadium are recommended interlayers for dissimilar welding of Ti-based alloys, since they do not react with titanium [16]. However, due to the high price and unavailability of these elements, Ag, Cu and Ni are usually used as the interlayer for the welding of these two materials, among which Cu is the most widely used interlayer in the field of dissimilar materials welding [17]. These elements will react with Ti and may form new IMCs, but in a case that the hardness of the new phases are less than that of the primary intermetallic phases formed between base metals elements (Ti-Ni IMCs in here), so it is reasonable to use these metals as the interlayer. Compared with TiNi alloy and Ti alloy, Cu has higher ductility and lower melting point, so it can reduce the influence of thermal stress mismatch caused by solidification of welding pool during welding [18]. In addition, copper is much cheaper than Zr, Ta, Mo, Ni, V and other elements, and is easy to obtain. On the other hand, according to the research of Bricknell et al. [19] on ternary shape memory alloys of Ti-Cu-Ni, nickel atoms can be substituted with copper atoms in lattice structure of NiTi. This substitution leads to the formation of Ti (Ni, Cu) ternary shape alloy at different transition temperatures. Therefore, Cu has a good compatibility with NiTi.

Experimental Procedure

Materials

The base materials used in this experiment were TC4 Ti alloy and TiNi alloy. There are large differences in thermal conductivity and linear expansion coefficient between the two base materials, which would lead to large temperature gradient and thermal stress in the joint during welding process. The base materials were machined into 50 mm×40 mm×1 mm plate, and then cleaned with acetone before welding. 0.3 mm thick Cu sheet (99.99 at. %) were adopted as interlayer and placed on the contact surface of the base material fixed in fixture.

Welding Method

CW laser was used with average power of 1.20 kW, wavelength of 1080 nm and beam spot diameter of 0.1 mm. Schematic diagram of the welding process is shown in (Figure 1). Schematic diagram of the welding process is shown in (Figure 1), where a good fitup between the TC4-Cu-NiTi was required to prevent gaps and ensure adequate heat transfer to form a joint. Laser welding for joint. During welding, laser beams were focused on the centrelines of the Cu interlayer (Figure 1). According to the thickness of the Cu interlayer to adjust welding parameters. At the same time can adjust parameters to change the fusion ratio of the base material. Laser offset for weld of joint was defined as 0 mm. The welding process parameters were: laser beam power of 396W, defocusing distance of +5 mm, welding speed of 650mm/min. Argon gas with the purity of 99.99% was applied as a shielding gas with total flow of 20L/min at top of the joint. Supplementary gas protection device covering the melted zone has been used to minimize the risk of oxidation.

Figure 1: Sketch of hydro-power plant.

Characterization Methods

The cross sections of joints were polished and etched in the reagent with 2ml concentrated HNO3 and 6 ml concentrated HF. The microstructure of joints was studied by optical microscopy (Scope Axio ZEISS), scanning electron microscope SEM (S-3400) with fast energy dispersion spectrum EDS analyzer, and selected area XRD (X’Pert3 Powder) analysis. Vickers microhardness tests for the weld carried out with a 10s load time and a 200g load. Tensile strength of the joints was measured by using universal testing machine (MTS Insight 10 kN) with cross head speed of 2mm/min.

Results and Discussion

Characterization of Joint

According to the previous research results, the microstructure, and mechanical properties of NiTi alloy/Ti alloy joint can be improved by adding appropriate interlayer materials, but the formation of brittle and hard Ti-Ni intermetallic compounds in the weld cannot be avoided. To further improve the mechanical properties of NiTi alloy/Ti alloy joint, the design idea of laser welding of NiTi alloy and Ti alloy assisted by metal transition layer is proposed in this paper. The purpose is to avoid the metallurgical reaction between Ti and Ni and improve the microstructure and mechanical properties of NiTi alloy/Ti alloy joint.

Macro-Characteristics

The optical microscopy image of the cross section of the joint is shown in (Figure 2a). The joint can fall into three parts: the fusion weld formed at the Ti alloy side, unmelted Ti alloy and the diffusion weld formed at the TiNi-Ti alloy interface. The fusion weld did not form Ti-Fe intermetallics due to the presence of unmelted Ti alloy. The average width of fusion weld, unmelted Ti alloy and diffusion weld was 1.8 mm, 0.35 mm and 0.17 mm, respectively. Because the microstructure of the fusion weld is quite different from that of the diffusion weld, the diffusion weld becomes black after corrosion. (Figure 2b) presents the optical image before corrosion of the diffusion weld. It does not present such defects as pores and macro-cracks. The unmelted part of Ti alloy acted as a heat sink absorbing a significant amount of energy from the welding pool and transferring it to the TiNi alloy side [20]. Hence, the filler metal of TiNi-Ti alloy interface had a high temperature during welding although it was not subjected to laser radiation. The temperature was high enough to promote atomic interdiffusion. This meets the temperature requirement for diffusion welding. Moreover, the local heating of the Ti alloy side caused uneven volume expansion and thermal stress was produced, which helped to obtain an intimate contact between the TiNi alloy, Cu-based fillers and Ti alloy surface. The high temperature and the intimate contact at the TiNi-Ti alloy interface provided favourable conditions for atomic (Cu, Zn, Ti, Ni) interdiffusion. Therefore, a diffusion weld was formed originated from atomic (Cu, Zn, Ti, Ni) interdiffusion at the Ti alloy-filler metal and filler metal-TiNi alloy interface. Additionally, the unmelted Ti alloy was beneficial to relieve and accommodate the thermal stress in the joint, which could help to improve the mechanical properties of the joints.

Figure 2: Macroscopic feature of the joint: (a) optical image of the cross section of the joint; (b) optical image before corrosion of the Ti alloy-TiNi alloy interface.

Microstructure Analysis

The optical image of the fusion weld is shown in (Figure 3a), and no defects were observed in it. SEM image of the fusion weld is shown in (Figure 3b). The fusion weld mainly consists of acicular structure. The optical image of the diffusion weld at NiTi-Ti alloy interface is shown in (Figure 3c). It can be observed that, the diffusion weld contained three zones marked as Ⅰ, Ⅱ and Ⅲ sorted by their morphologies and colours. (Figures 3d, 3e and 3f)correspond to the three zones in (Figure 3c), respectively. The compositions of each zone (denoted by letter A-C in (Figure 3)) were studied using SEM-EDS. EDS analysis was applied to these zones to measure the compositions of the reaction products and the results are listed in Table 1. Based on the previous analysis, the microstructure of the diffusion weld was mainly composed of Cu-based fillers. The chemical composition of zone Ⅰ was consistent with the Cu-based fillers. Based on the EDS analyses results and Cu-Zn phase diagram, the main microstructure of zone Ⅰ was defined as β-CuZn phase. When the laser beam was focused near the Ti alloy-filler metal interface, the element diffusion occurs immediately between the base materials and filler metal and causes its component to deviate from the original component. The interdiffusion of Cu, Zn, Ti and Ni elements occurred at diffusion welding interface (Ti alloy-filler metal and filler metal-NiTi alloy). At this moment, the dissolution of Ti and Ni into the filler metal occurred under the high concentration gradient, which formed solid-phase reaction layer, and this reaction layer exists only in the smaller region of the NiTi-Ti alloy interface. As shown in, zone Ⅱ and zone Ⅲ were reaction layers formed by element diffusion. Based on Ti-Cu-Ni phase diagram, the microstructure of zone Ⅱ was defined as TiCu2+NiZn. Based on Cu-Ti-Zn phase diagram, the microstructure of zone Ⅲ was defined as Ti3Cu4+Ti2Zn3. Therefore, the main microstructures of diffusion weld were TiCu2+NiZn, β-CuZn and Ti3Cu4+Ti2Zn3.

Table 1: The chemical composition of each phase in joint C (wt.%).

Figure 3: Microstructures of the joint : (a) optical image of fusion zone; (b) SEM image of fusion zone; (c) optical image of the diffusion weld; (b) SEM image of the zone I in Fig. 3c; (c) SEM image of the zone II in Fig. 3c; (d) SEM image of the zone III in Fig. 3c.

Figure 4: Vickers microhardness measurements at semi-height of joint (zero point situated in the center of joint).

Tensile Tests and Fracture Analysis

The maximum tensile strength of the joint was about 256 MPa (Figure 5a). The joint fractured in Ti alloy side of the diffusion weld during tensile tests (Figures 5b, 5c)shows fracture surface of the joint exhibiting typical brittle characteristics. Moreover, as shown in (Figure 5d), XRD analyses of fracture surface detected Ti3Cu4 and Ti2Zn3 phases. This confirmed the presence of Ti-Cu and Ti-Zn intermetallics at fracture surfaces. It should be noted that there was no Ti-Ni intermetallics in the brazed weld. Reaction layer at Ti alloy side in diffusion weld became the weak zone of the joint, which led to the failure in the tensile test.Based on the above results, the formation of Ti-Ni intermetallic compounds is avoided due to the presence of unmelted Ti alloy in the joint. Only a small amount of Ti-Cu intermetallic compounds is formed in the reaction layer at the NiTi-Ti alloy interface. Due to the rapid heating and cooling speed of laser welding, the holding time at high temperature is short, and it is easy to form a narrow reaction zone at the NiTi-Ti alloy interface. In addition, higher cooling rate inhibited the growth of dendrite structure in the reaction zone. Therefore, it is easy to obtain fine microstructure in the reaction zone, which is conducive to reducing the brittleness of the reaction layer. The results show that the formation of narrow reaction layer and fine metallurgical structure at the interface is one of the main reasons to improve the joint strength.

Figure 5: Tensile test results of joint: (a) Tensile test curve; (b) Fracture location; (c) SEM image of fracture surface; (d) XRD analysis results of fracture surface.

Conclusion

The possibility of welding processes for connect TC4 Ti alloy to NiTi alloy with Cu-base filler metal was studied. The main conclusions are presented below. without filler metal, For joint with a laser beam offset of 1.2 mm for Ti alloy, the unmelted Ti alloy was selected as an barrier to avoid mixing of the NiTi alloy and Ti alloy which eliminated the formation of brittle Ti-Ni intermetallic in the joint . A diffusion weld was formed at the NiTi alloy-Ti alloy interface with the main microstructure of TiCu2+NiZn, β-CuZn and Ti3Cu4+Ti2Zn3. A great amount of atomic diffusion occurs at the NiTi-Ti alloy interface during welding, and the thickness of diffusion weld can reach hundreds of micrometres. The tensile resistance of the joint was determined by diffusion weld. The maximum tensile strength of joint was 256 MPa.

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