Analysis of human resource management challenges in implementation of industry 4.0 in Indian automobile industry

PurposeThis research aims to propose a framework to integrate Green Lean Six Sigma (GLSS) and Industry 4.0 to improve organizational sustainability.Design/methodology/approachThe integration of GLSS and Industry 4.0 is proposed based on theoretical facets of the individual approaches. A generic, conceptual framework of an integrated GLSS-Industry 4.0 approach is then proposed using the application of different tools and techniques of GLSS and Industry 4.0 at different stages of the realization of a project.FindingsBoth approaches have common facets related to enablers and barriers, and the integrated application of tools and techniques of each approach supplements the common focus of both related to sustainability enhancement. The proposed, conceptual framework provides systematic guidelines from the project selection stage to the sustainment of the solution, with the enumerated application of different techniques and tools at each step of the framework.Originality/valueThis research is the first of its kind to propose the integration of GLSS and Industry 4.0 under the umbrella of a unified approach, including a conceptual framework of this integrated GLSS-Industry 4.0 approach.

Integrating Green Lean Six Sigma and industry 4.0: a conceptual framework

Mapping the knowledge domain of soft computing applications for emergency evacuation studies: A scientometric analysis and critical review

Industry 4.0 has revolutionized the traditional production line toward smart factories in a sustainable environment. It represents an intelligent manufacturing network, where machines and products interact without human control. Recently, there have been studies on Industry 4.0 and integrating enabling technologies, and this article aims to summarize a few of the pathbreaking studies and guide future research in this field. This paper reviews 112 journal publications related to Blockchain from 2017 to 2021 in the Science Citation Index (SCI) and Social Science Citation Index (SSCI) database. The selected papers are grouped into ten categories. This overview indicates that research of integrating Industry 4.0 enabling technologies needs to focus on developing better privacy and security mechanisms and a design framework for integrating the 5G network system into the platform. 

Industry 4.0 and Enabling Technologies: Integration Framework and Challenges

A low-cost, fast, dependable, repeatable, non-invasive, portable, and simple-to-use vascular screening tool for coronary artery diseases (CADs) is preferred. Photoplethysmography (PPG), a low-cost optical pulse wave technology, is one method with this potential. PPG signals come from changes in the amount of blood in the microvascular bed of tissue. Therefore, these signals can be used to figure out anomalies within the cardiovascular system. This work shows how to use PPG signals and feature selection-based classifiers to identify cardiorespiratory disorders based on the extraction of time-domain features. Data were collected from 360 healthy and cardiovascular disease patients. For analysis and identification, five types of cardiovascular disorders were considered. The categories of cardiovascular diseases were identified using a two-stage classification process. The first stage was utilized to differentiate between healthy and unhealthy subjects. Subjects who were found to be abnormal were then entered into the second stage classifier, which was used to determine the type of the disease. Seven different classifiers were employed to classify the dataset. Based on the subset of features found by the classifier, the Naïve Bayes classifier obtained the best test accuracy, with 94.44% for the first stage and 89.37% for the second stage. The results of this study show how vital the PPG signal is. Many time-domain parts of the PPG signal can be easily extracted and analyzed to find out if there are problems with the heart. The results were accurate and precise enough that they did not need to be looked at or analyzed further. The PPG classifier built on a simple microcontroller will work better than more expensive ones and will not make the patient nervous.

/pdf/identification-of-coronary-artery-diseases-using-2qsxybjo.pdf

Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process

A comprehensive study on current and future trends towards the characteristics and enablers of industry 4.0

Dental implants are placed in the jaw of a patient to perform the function of a missing tooth. However, before the implant is subjected to masticatory loading, sufficient osseointegration should take place to prevent its failure. Resonance Frequency Analysis is one of the methods to measure the primary stability of an implant system in terms of micromotion. This paper aims to formulate a mathematical model of the dental prosthetic for the vibroacoustic RFA technique. Vibroacoustic RFA is a novel method that excites the implant using either a loudspeaker or a buzzer and captures the subsequent displacement. In this study, a single degree of freedom mathematical model is excited using a sinusoidal force. The subsequent displacement or micro-motion of the implant system is found out by basic equations of vibrations while varying the input frequency between 1 – 50 Hz. The resonant frequency corresponding to the maximum micro-motion which lies between 1.2 – 2 µm is plotted using MATLAB. The resonant frequency values lie between 8975 – 8995 Hz with an average value of 8985 Hz. When the input frequency increases beyond 40 Hz, the micromotion shows a percentage increase of 40.62%. A higher value of resonance frequency indicates better osseointegration with decrease in micromotion. A Mathematical model and subsequent signal analysis using RFA is advantageous for implant stability measurement. Further research is needed to develop multiple degree of freedom systems to simulate the dental implant model.

A Mathematical Model for Biomechanical Evaluation of Micro-motion in Dental Prosthetics using Vibroacoustic RFA

Recently, Artificial Intelligence (AI) and Machine Learning (ML) have been significant contributors to the development of sophisticated systems and models for cardiovascular disease (CVD) management. There is a wide scope of applications available today. Electrocardiography (ECG), cardiovascular image analysis and wearables for remote monitoring are the most consistently developing domains that are beginning to be AI-driven as technology and software advance over time. Furthermore, throughout the COVID-19 pandemic, AI has been utilised for cardiovascular disease management, drug targeting and risk analysis. The aim of the review is to highlight the recent advancements related to CVD management using AI and ML-based technologies. The works of literature reviewed in the present study consider the advancements of AI and ML-based CVD management technologies developed in the past decade. The different methods and technologies were studied, the working of various AI and ML algorithms have been discussed and few research gaps have been identified. The current implementations of AI algorithms lay a foundation to reduce the time and effort required to develop newer systems for future developments. Subsequently, overcoming the limitations of data processing techniques, many parameters required, and standardized datasets will improve the accuracy, implementation and interoperability of the AI and ML-based technologies for seamless integration in healthcare.

Current Advancements in Cardiovascular Disease Management using Artificial Intelligence and Machine Learning Models: Current Scenario and Challenges

The aim of this paper is to formulate a mathematical model of dental prosthetic using single degree of freedom (SDOF) to assess the micro-displacement under electromagnetic excitation. Using Finite Element Analysis (FEA) and values from literature, stiffness and damping values of the mathematical model were estimated. For ensuring the successful implantation of dental implant system, monitoring of primary stability in terms of micro-displacement is crucial. One of the most popular techniques for the measurement of stability is the Frequency Response Analysis (FRA). This technique assesses the resonant frequency of vibration corresponding to the maximum micro-displacement (micro-mobility) of the implant. Among the different FRA techniques, the most common method is the Electromagnetic FRA. The subsequent displacement of the implant in the bone is estimated by equations of vibration. A comparison has been made to observe the variation in resonance frequency and micro-displacement due to varying input frequency ranges of 1-40 Hz. The micro-displacement and corresponding resonance frequency were plotted using MATLAB and the variation in resonance frequency is found to be negligible. The present mathematical model is a preliminary approach to understand the variation of micro-displacement with reference to electromagnetic excitation force and to obtain the resonance frequency. The present study validated the use of input frequency ranges (1-30 Hz) with negligible variation in micro-displacement and corresponding resonance frequency. However, input frequency ranges beyond 31-40 Hz is not recommended due to large variation in micromotion and corresponding resonance frequency. 

Niharika Karnik

Papers

A comprehensive study on current and future trends towards the characteristics and enablers of industry 4.0

A Mathematical Model for Biomechanical Evaluation of Micro-motion in Dental Prosthetics using Vibroacoustic RFA

Current Advancements in Cardiovascular Disease Management using Artificial Intelligence and Machine Learning Models: Current Scenario and Challenges

A mathematical approach to estimate micro-displacement of a dental implant using electromagnetic Frequency Response Analysis.