Modeling and Optimization of Micro-EDM Operation for Fabrication of Micro Holes

Based on the experimental results, an analysis was made to identify the performance of various electrodes during fabrication of micro holes considering Inconel 718 as well as titanium as workpiece materials. It was found that that platinum followed by graphite and copper as electrode material exhibited higher MRR for both the workpiece materials but on the other hand platinum showed higher values of OC, RCL and TA respectively when compared to graphite and copper. The variation of temperature distribution in radial and depth direction with different process parameters has been determined for Inconel 718 and Titanium 5. Theoretical cavity volume was calculated for different process parameter settings for both workpiece materials and it was found that Titanium 5 exhibited higher cavity volume then Inconel 718. This research work offers new insights into the performance of micro-µ-EDM of Inconel 718 and Titanium5 using different electrodes. The optimum process parameters have been identified to determine multi-objective machinability criteria such as MRR, angle of taper of micro-hole, the thickness of recast-layer and overcut for fabrication of micro-holes

Artificial neural networks for vibration based inverse parametric identifications: A review

Vibration behavior of any solid structure reveals certain dynamic characteristics and property parameters of that structure. Inverse problems dealing with vibration response utilize the response signals to find out input factors and/or certain structural properties. Due to certain drawbacks of traditional solutions to inverse problems, ANNs have gained a major popularity in this field. This paper reviews some earlier researches where ANNs were applied to solve different vibration-based inverse parametric identification problems. The adoption of different ANN algorithms, input-output schemes and required signal processing were denoted in considerable detail. In addition, a number of issues have been reported, including the factors that affect ANNs’ prediction, as well as the advantage and disadvantage of ANN approaches with respect to general inverse methods Based on the critical analysis, suggestions to potential researchers have also been provided for future scopes

Cooling of motor spindles - a review

Thermally induced loads in motor spindles can cause a number of undesired effects. As a result, the process capability of spindles, and thus, the productivity of a process can decrease. Future motor spindles will be exposed to higher mechanical and especially thermal loads due to trends aiming to increase power densities and maximum speeds. These trends are amplified by increasingly powerful drive concepts and developments in bearing technology. Therefore, researchers assume that it will not be possible to raise the performance potential of spindles due to insufficient cooling of its heat sources. A series of different cooling concepts have been researched and developed in recent decades. These developments have been made for different purposes. They also differ considerably in terms of their cooling principles and cooling performance. In this article, these cooling approaches and the motivations for their development are described. Firstly, the causes of heat development in motor spindles are described in a historical context. Subsequently, the effects of heat development on the manufacturing-relevant properties of motor spindles are revealed. Finally, current deficits in the area of spindle cooling and the need for the development and transfer into industrial practice of more efficient and cost-effective cooling concepts to overcome future challenges are discussed. © 2020, The Author(s)

Review of dynamic positioning control in maritime microgrid systems

For many offshore activities, including offshore oil and gas exploration and offshore wind farm construction, it is essential to keep the position and heading of the vessel stable. The dynamic positioning system is a progressive technology, which is extensively used in shipping and other maritime structures. To maintain the vessels or platforms from displacement, its thrusters are used automatically to control and stabilize the position and heading of vessels in sea state disturbances. The theory of dynamic positioning has been studied and developed in terms of control techniques to achieve greater accuracy and reduce ship movement caused by environmental disturbance for more than 30 years. This paper reviews the control strategies and architecture of the DPS in marine vessels. In addition, it suggests possible control principles and makes a comparison between the advantages and disadvantages of existing literature. Some details for future research on DP control challenges are discussed in this paper

Machinability of Al-20Mg2Si-Cu metal matrix composite modified with bismuth or barium

Aluminum metal matrix composites (MMC) have been receiving growing attention largely owing to their excellent properties, such as excellent castability and mechanical properties. These properties make them useful for high performance applications, especially for lightweight components, particularly in the manufacture of automotive parts. Al-based composite reinforced with particulate Mg2Si phase has recently been shown to possess certain advantages. However, the mechanical properties of normal cast Al-20Mg2Si-Cu metal matrix composite are unsatisfactory due to the natural coarse morphology of the primary Mg2Si phase. Therefore, the melt treatment method with refiner elements was chosen due to improve the morphology of the Mg2Si reinforcement and achieve better mechanical properties. Most of MMC parts require some machining. However, the machinability of Al-20Mg2Si-Cu metal matrix composite is not well understood yet. By adding refiner elements to the composite, the mechanical properties and machinability including particle emissions will be affected. Therefore, the main objective of this thesis is to study the machinability, and mechanical properties of Al-20Mg2Si-Cu metal matrix composite containing bismuth or barium as modifier elements. Results from the different sections of this thesis demonstrate that modifier elements such as bismuth or barium improve the mechanical properties and machinability of the composite. Using modifier elements decreases ultrafine particle emissions during the machining process, which is beneficial for operator health. In addition, the proposed mathematical models for predicting surface roughness and cutting force for this composite are in good agreement with experiment data

Noise Impact Assessment and Prediction in Mines Using Soft Computing Techniques

Mining of minerals necessitates use of heavy energy intensive machineries and equipment leading to miners to be exposed to high noise levels. Prolonged exposure of miners to the high levels of noise can cause noise induced hearing loss besides several non-auditory health effects. Hence, in order to improve the environmental condition in work place, it is of utmost importance to develop appropriate noise prediction model for ensuring the accurate status of noise levels from various surface mining machineries. The measurement of sound pressure level (SPL) using sound measuring devices is not accurate due to instrumental error, attenuation due to geometrical aberration, atmospheric attenuation etc. Some of the popular frequency dependent noise prediction models e.g. ISO 9613- 2, ENM, CONCAWE and non-frequency based noise prediction model e.g. VDI-2714 have been applied in mining and allied industries. These models are used to predict the machineries noise by considering all the attenuation factors. Amongst above mathematical models, VDI-2714 is simplest noise prediction model as it is independent from frequency domain. From literature review, it was found that VDI-2714 gives noise prediction in dB (A) not in 1/1 or 1/3 octave bands as compared to other prediction models e.g. ISO-9613-2, CONCAWE, OCMA, and ENM etc. Compared to VDI-2714 noise prediction model, frequency dependent models are mathematically complex to use. All the noise prediction models treat noise as a function of distance, sound power level (SWL), different forms of attenuations such as geometrical absorptions, barrier effects, ground topography, etc. Generally, these parameters are measured in the mines and best fitting models are applied to predict noise. Mathematical models are generally complex and cannot be implemented in real time systems. Additionally, they fail to predict the future parameters from current and past measurements. To overcome these limitations, in this work, soft-computing models have been used. It has been seen that noise prediction is a non-stationary process and soft-computing techniques have been tested for non-stationary time-series prediction for nearly two decades. Considering successful application of soft-computing models in complex engineering problems, in this thesis work, soft-computing system based noise prediction models were developed for predicting far field noise levels due to operation of specific set of mining machinery. Soft Computing models: Fuzzy Inference System (Mamdani and Takagi Sugeno Kang (T-S-K) fuzzy inference systems), MLP (multi layer perceptron or back propagation neural network), RBF (radial basis function) and Adaptive network-based fuzzy inference systems (ANFIS) were used to predict the machinery noise in two opencast mines. The proposed soft-computing based noise prediction models were designed for both frequency and non-frequency based noise prediction models. After successful application of all proposed soft-computing models, comparitive studies were made considering Root Mean Square Error (RMSE) as the performance parameter. It was observed that proposed soft-computing models give good prediction results with accuracy. However, ANFIS model gives better noise prediction with better accuracy than other proposed soft-computing models

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Friction Force Microscopy of Deep Drawing Made Surfaces

Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology