Industry 4.0: a supply chain innovation perspective

The Fourth Industrial Revolution – also known as Industry 4.0 (i4.0) – comprises the digitalisation of the industrial sector. This paper uses the theoretical lens of supply chain innovation (SCI) to investigate the implications of i4.0 on supply chain management. For these purposes, the method of structured content analysis is applied to more than 200 use cases of i4.0-enabled SCI introduced by both established and startup companies. i4.0-enabled SCI manifests along three dimensions: process, technology, and business architecture. The key findings of this study can be summarised as follows: first, i4.0-enabled SCI extends the initial focus on productivity improvements in SC processes towards scalability and flexibility. Second, extant i4.0 solutions rely mostly on analytics and smart things while omitting smart people technology and the human-centric approach associated with the i4.0 paradigm. Third, established companies adopt i4.0 merely to sustain their existing business architectures while startup companies radically change their operating models, relying heavily on data analytics and the platform economy. Consequently, established companies pursue a problem-driven, engineering-based approach to SCI while startup companies follow an ‘asset-light’, business-driven approach. Lastly, there are two distinct approaches to digitalising operational SC processes: platform-based crowdsourcing of standard processes and on-demand provision of customised services.     

Surface Integrity Assessment Upon Electric Discharge Machining of Die Steel Using Non-Destructive Magnetic Barkhausen Noise Technique

Surface integrity characterization of manufactured component is very important as it significantly affects the in-service performance of the component. Till now, surface integrity was evaluated using conventional measurement technique like microhardness tester, X-ray diffraction, optical microscopy and surface roughness tester. But, this technique being laboratory based cannot be used for in-service monitoring of the surface integrity. The present study focuses on the characterization of surface integrity upon electric discharge machined sample using non-destructive magnetic Barkhausen noise technique. Electric discharge machining was performed in die-sinking mode on die steel using copper–tungsten electrode (negative polarity). Experiment was performed by selecting different levels of peak current, gap voltage and pulse on time. Surface integrity characteristics like microhardness change, residual stress, microstructural alteration and surface roughness were analysed using microhardness tester, X-ray diffraction, optical microscopy and surface roughness tester, respectively, and were then correlated with magnetic parameter like root mean square value and peak value obtained from Barkhausen noise signal. The results show a good correlation between magnetic parameter (RMS and Peak value) of Barkhausen noise with the microhardness and surface roughness of the machined sample.

     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Performance Investigations and PAPR Reduction Analysis Using Very Efficient and Optimized Amended SLM Algorithm for Wireless Communication OFDM System

Wireless Personal Communications - The orthogonal frequency division multiplexing (OFDM) is the most encouraging multi-carrier modulation system chosen for the high data rates but the objective is...     

Influence of Sodium Microalloying on Rolling Contact Endurance and Mechanical Properties of Hot-Deformed Powder Steels

Russian Journal of Non-Ferrous Metals - Rolling bearing rings comprise a large reserve for expanding powder metallurgy production. This reserve is being incompletely implemented. The hot forging of...     

Related Oscillations of Plasmons in Two Similar Nanocylinders from Silver

Journal of Communications Technology and Electronics - Abstract—The two-dimensional diffraction problem of a plane polarized TM-wave on two identical solid silver nanocylinders is considered....     

Anomaly-Based Intrusion Detection System in RPL by Applying Stochastic and Evolutionary Game Models over IoT Environment

Wireless Personal Communications - The rapid development of Information and Communication Technology and the growing number of devices connected to the Internet make the Internet of Things (IoT) as...     

Chlorination of Quartz-Leucoxene Concentrate of Yarega Field

Metallurgical and Materials Transactions B - The process of obtaining titanium tetrachloride (TiCl4) from the quartz-leucoxene concentrate (QLC) from the Yaregskoe oil-titanium field was studied....     

A study on harvesting of PKL electricity

The efficiency of any electric cell or battery is very important. To keep it in mind it has been studied the columbic efficiency, voltaic efficiency and energy efficiency of a PKL (Pathor Kuchi Leaf) Quasi Voltaic Cell or Modified Voltaic Cell. It was found that the columbic efficiency data illustrated that this efficiency was lower comparing to other efficiencies may be the absence of salt bridge or separator between the electrodes. Because, our designed and fabricated PKL cell does not have any salt bridge. So that the internal resistance is lower than the traditional voltaic cell and as a result more current was found. The voltage and current changes with time and I–V characteristics for PKL unit cell, module, panel and array have also been studied. It is shown that the voltaic and energy efficiency have been studied. However, the highest efficiency was obtained for 40% PKL sap with 5% secondary salt in 55% aqueous solution, which implies that the concentration of PKL juice can play an important role regarding efficiency. It was also found that the average energy efficiency was 97.43% and it was also found that the average voltaic efficiency was 57.29%. Finally, morphological study FESEM (Field Emission Scanning Electron Microscopy) has also been performed. It is seen that the results confirmed that Zn was deposited on the Cu surface during the electro deposition process in PKL solution. Using AAS, it has been measured the concentration of [Cu²⁺] as a reactant ion and the concentration of [Zn²⁺] as a product ion those have been tabulated and graphically discussed. The variation of pH has also been studied with time and which was also tabulated and graphically discussed.

     

Multimodal sensing and active continuous closed‐loop feedback for achieving reliable manipulation in the outdoor physical world

The use of field robots can greatly decrease the amount of time, effort, and associated risk compared to if human workers were to carryout certain tasks such as disaster response. However, transportability and reliability remain two main issues for most current robot systems. To address the issue of transportability, we have developed a lightweight modularizable platform named AeroArm. To address the issue of reliability, we utilize a multimodal sensing approach, combining the use of multiple sensors and sensor types, and the use of different detection algorithms, as well as active continuous closed‐loop feedback to accurately estimate the state of the robot with respect to the environment. We used Challenge 2 of the 2017 Mohammed Bin Zayed International Robotics Competition as an example outdoor manipulation task, demonstrating the capabilities of our robot system and approach in achieving reliable performance in the fields, and ranked fifth place internationally in the competition.