A Study on Evolution of Splat Radius and Temperature in Thermal Spray Process

The impact process of a molten metal droplet impinging on a solid substrate surface is encountered in several technological applications such as ink-jet printing, spray cooling, coating processes, spray deposition of metal alloys, thermal spray coatings, manufacturing processes and fabrication and in industrial applications concerning thermal spray processes. Deposition of a molten material or metal in form of a droplet on a substrate surface by propelling it towards it forms the core of the spraying process. During the impact process, the molten metal droplet spreads radially and simultaneously starts losing heat due to heat transfer to the substrate surface. The associated heat transfer influences impingement behavior. The physics of droplet impingement is not only related to the fluid dynamics, but also to the respective interfacial properties of solid and liquid. For most applications, maximum spreading diameter of the splat is considered to be an important factor for droplet impingement on solid surfaces. In the present study, we have developed a model for droplet impingement based on energy conservation principle to predict the maximum spreading radius and the radius as a function of time. Further, we have used the radius as a function of time in the heat transfer equations and to study the evolution of splat-temperature and predict the spreading factor and the spreading time and mathematically correlate them to the spraying parameters and material properties.

Experimental and Theoretical Investigations on Tool Wear and Surface Quality in Micro Milling of SiCp/Al Composites Under Dry and MQL Conditions

During the machining processes of ceramic particle reinforced metal matrix composites, the severe tool wear constrains the quality and cost of the parts. This paper presents the experimental and theoretical investigations of the tool wear behavior and surface quality when micro milling the 45vol% SiCp/Al composites under dry and minimum quantity lubrication (MQL) conditions. The results of scanning electron microscope (SEM) and energy dispersive spectrometer (EDS) show that the wear mechanism of diamond coated micro mills are adhesive, abrasion, oxidization, chipping and tipping, even though it has been reported that abrasion is the most important tool wear mechanism when machining particle reinforced metal matrix composites. Compared with dry lubrication condition, the environmentally friendly MQL technique can enhance the tool life and surface roughness, and reduce the cutting force significantly under given cutting parameters. Then, finite element (FE) simulations are employed to investigate chip formation process in micro orthogonal cutting to reveal the effects of reinforced particle on tool wear and surface quality. The FE simulations shows the local high stress, hard reinforced particles in metal matrix, debonded and cracked particles are the key factors leading to the severe tool wear and the unsmoothed surface morphology.

A Mathematical Functional Decomposition Approach Through Granularity Partition Process in Quotient Space

Over the past decades, several methodologies have coalesced around the functional decomposition and partial solution manipulation techniques. These methodologies take designers through steps that help decompose a design problem and build conceptual solutions based on the intended, product functionality. However, this kind of subjective decomposition restricts solutions of conceptual design within designers’ intended the local, rather the whole, solution space. In such cases, the ability for AI-based functional reasoning systems to obtain creative conceptual design solutions is weakened. In this paper, a functional decomposition model based on the domain decomposition theory in quotient space is proposed for carrying out functional decomposition without needing functional reasoning knowledge to support. In this model, the functional decomposition is treated as a granularity partition process in quotient space composed of three variables: the domain granularities, the attribute properties, and the topological structures. The closeness degrees and the attribute properties in fuzzy mathematics are utilized to describe the fuzzy equivalence relations between the granularities in the up-layer and in the lower-layer of the functional hierarchies. According to the order characteristics in the partially sequential quotient space, based on the homomorphism principle, the attribute properties and the topological structures corresponding to the lower-layer of the functional hierarchies are constructed then. Here, the attribute properties are expressed with membership functions pointed to the lower-layer from the up-layer of the functional hierarchies, and the topological structures are expressed with matrixes and the directed function network represent the topological connections among the subfunctions in the lower-layer of the functional hierarchies. Through refining the functional decomposition process step by step, and traversing all tree branches and leaf nodes in the functional decomposition tree, the functional hierarchies are obtained. Since the functional decomposition process not need the user to indicate or manage desired functionality, the model presented in this paper can reduce designers’ prejudices or preconceptions on the functional hierarchies, as well as extend the solution space of conceptual design.

Springback Analysis in Hybrid Material Deformation

In the automotive and aerospace industries, cost and overall weight are major opponents that are affecting design opportunities. Research to investigate possible cost and weight reduction methods is continuously being performed focusing especially on the hybrid materials being used to manufacture parts. Currently, different types of metals with polymers are being chosen to make punched parts, but the deformation of the materials has not been fully investigated. The way that the material deforms will dictate the material properties held by the subsequent parts. Without knowing these material properties, it is difficult to prevent manufacturing problems during various processes. One major problem encountered when forming solid metal parts is that when the die is removed, the deformed parts change shape due to the elastic properties of the material. This shape change is called springback. This undesirable result causes the parts to be the incorrect shape and to not align correctly during assembly. One possible solution would be to investigate the material properties of trilayer hybrid materials consisting of metal and composite layers adjoined by adhesive. Trilayer channels will be tested by punching and measuring the resulting springback. Two different trilayer design setups will be tested, composite metal composite sandwich and metal composite metal sandwich, and will be compared with the deformation in a single layer metal channel. The outcome of these tests will determine which trilayer design will have the greatest success in reducing the undesirable springback effects.

A New Design of Cycloidal Planetary Reducer With Internal Cycloidal Profile

Compared with other speed reducers, the two-stage cycloidal planetary one also known as RV reducer has higher precision, higher mechanical efficiency, higher loading capacity as well as long service life. These characteristics make it attractive for industrial applications, especially for robotics applications and machine tools, where high precision and large torque transmission are required. The traditional RV reducer uses cycloidal drive which is comprised of the cycloidal wheel and the pins. It has some disadvantages in the pin design, because of small clearance between the pin and the cycloidal wheel, the collision between the pin and the cycloidal wheel may lead to unstable stress in the key parts and output velocity fluctuation. This paper presents an innovative cycloidal planetary reducer using internal meshing principle instead of external meshing between cycloidal wheel and pins in traditional RV reducer. In the new design of the reducer, the internal teeth with cycloidal profile are processed inside the reducer housing, meshing with two pin holders which are placed at the inner side of the cycloidal teeth to achieve transmission. The pin holder is a new integral structure of pins integrated on a round plate. Then a comparison study is conducted through establishment of system dynamics analysis. The transmission characteristics and meshing force of both the new type of reducer and the traditional RV reducer are analyzed under the same condition of reduction ratio. The results show the new reducer improves on these shortcomings, its transmission performance is competitive as compared to traditional RV reducer. What’s even better is that its output speed is more stable, and the contact force between the pin position on the pin holder and the internal cycloidal teeth inside the reducer housing is smaller, as well as the contact frequency is obviously decreased.

Thermal-Mechanical Study of 3D Printing Technology for Rail Repair

The complicated steel wheel and rail interaction on curve causes side wear on rail head. Thus, the cost of maintenance for the track on curve is significantly higher than that for track on a tangent. The objective of this research is to develop 3D printing technology for repairing the side wear. In this paper, the study examines induced residual thermal stresses on a rail during the cooling down process after 3D printing procedure using the coupled finite volume and finite element method for thermal and mechanical analysis respectively. The interface of the railhead and additive materials should conserve high stresses to prevent any crack initiation. Otherwise, the additive layer would likely shear off the rail due to crack propagation at the rail/additive interface. In the numerical analysis, a cut of 75-lb ASCE (American Society of Civil Engineers) worn rail is used as a specimen, for which a three-dimensional model is developed. The applied residual stresses, as a result of temperature gradient and thermal expansion coefficient mismatch between additive and rail materials, are investigated. At the beginning, the worn rail is at room temperature while the additive part is at a high initial temperature. Then, additive materials start to flow thermal energy into the worn rail and the ambient. The thermal distribution results from thermal analysis are then employed as thermal loads in the mechanical analysis to determine the von-Mises stress distribution as the decisive component. Then, the effect of preheating on residual stress distribution is studied. In this way, the thermo-mechanical analysis is repeated with an increase in railhead’s initial temperature. In thermal analysis, the temperature contours at different time steps for both the non-preheated and preheated cases indicate that preheating presents remarkably lower temperature gradient between rail and additive part and also represents a more gradual cooling down process to allow enough time for thermal expansion mismatch alignment. In mechanical analysis, the transversal von-Mises stress distribution at rail/additive interface is developed for all cases for comparison purposes. It is shown that preheating is a key factor to significantly reduce residual stresses by about 40% at all points along transversal direction of interface.

Towards a Digital Twin for Cloud Manufacturing: Case Study

Cloud manufacturing (CMfg) is a new manufacturing paradigm designed to enable manufacturing enterprise to share their resources and capabilities. Prior to any real-life change in the system, for CMfg it is important to anticipate and optimize the response of the system through simulation. Digital Twins (DT) is a simulation method for this paradigm that is different from existing simulation methods in two ways. It is a virtual copy of the system containing all the components and can connect to the controller in real time. The goal of this work is to develop a DT for an educational manufacturing cell. The educational manufacturing cell is a FESTO Reconfigurable Mechatronics System (RMS). The cell has four stations that uses pallets to transport the product on the conveyor belt and assembles a part of the product. The Siemens Process Simulate: TECNOMATIX, was used to create the DT of the system. The system is modeled in a CAD program and then imported into TECNOMATIX Process Simulate, where it is programmed to replicate the processes.

Flexible Strain Sensor Using Additive Manufacturing and Conductive Liquid Metal: Design, Fabrication, and Characterization

In this work a flexible strain sensor is fabricated using Fused Deposition Modeling (FDM) 3D printing technique. The strain sensor is fabricated using commercially available flexible Thermoplastic Polyurethane (TPU) filaments and liquid metal Galinstan Ga 68.5% In 21% Sn 10%. The strain sensor consists of U-shape 2.34mm long and 0.2mm deep channels embedded inside a TPU 3D printed structure. The performance of the strain sensor is measured experimentally. Gauge Factor is estimated by measuring change in electric resistance when the sensor is subject to 13.2% – 38.6% strain. Upon straining and unstraining, results from characterization tests show high linearity in the range of 13.2% to 38.6% strain with very little hysteresis. However, changes due to permanent deformations are a limiting factor in the usefulness of these sensors because these changes limit the consistency of the device. FDM 3D printing shows promise as a method for fabricating flexible strain sensors. However, more investigation is needed to look at the effects of geometries and 3D printing process parameters on the yield elongation of the flexible filaments. Additionally, more investigation is needed to observe the effect of distorted dimensions of the 3D printed channels on the sensitivity of the strain sensor. It is anticipated that successful implementation of these commercially available filaments and FDM 3D printers will lead to reduction in cost and complexity of developing these flexible sensors.

Adaptive Industrial Robots Using Machine Vision

The use of industrial robots in modern manufacturing scenarios is a rising trend in the engineering industry. Currently, industrial robots are able to perform pre-programmed tasks very efficiently irrespective of time and complexity. However, often robots encounter unknown scenarios and to solve those, they need to cooperate with humans, leading to unnecessary downtime of the machine and the need for human intervention. The main aim of this study is to propose a method to develop adaptive industrial robots using Machine Learning (ML)/Machine Vision (MV) tools. The proposed method aims to reduce the effort of re-programming and enable self-learning in industrial robots. The elaborated online programming method can lead to fully automated industrial robotic cells in accordance with the human-robot collaboration standard and provide multiple usage options of this approach in the manufacturing industry. Machine Vision (MV) tools used for online programming allow industrial robots to make autonomous decisions during sorting or assembling operations based on the color and/or shape of the test object. The test setup consisted of an industrial robot cell, cameras and LIDAR connected to MATLAB through a Robot Operation System (ROS). The online programming tests and simulations were performed using Virtual/Augmented Reality (VR/AR) toolkits together with a Digital Twin (DT) concept, to test the industrial robot program on a digital object before executing it on the real object, thus creating a safe and secure test environment.

Visualisation and Detection of Road Traffic Events Using Complex Event Processing

In the recent decades, we have witnessed an increase in the number of vehicles using the road infrastructure, resulting in an increased overload of the road network. To mitigate such problems, caused by the increasing number of vehicles and increasing the efficiency and safety of transport systems has been integrated applications of advanced technology, denominated Intelligent Transport Systems (ITS). However, one problem still unsolved in current road networks is the automatic identification of road events such as accidents or traffic jams, being inhibitor to efficient road management. In order to mitigate this problematic, this paper proposes the development of a technological platform able to detect anomalies (abnormal traffic events) to typical road network status and categorize such anomalies. The proposed work, adopts a complex event processing (CEP) engine able to monitor streams of events and detect specified patterns of events in real time. Data is collectively collected and analysed in real-time from loop sensors deployed in Slovenian highways and national roads, providing traffic flows. This prototype will work with a large number of data, being used to process all data, complex event processing tools. All the data used to validate the present study is based on the Slovenian road network. This work has been carried out in the context of the OPTIMUM Project, funded by the H2020 European Research Framework Program.