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This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates multiple responses while considering the process's control and noise parameters. In addition, this paper intended to develop a multidisciplinary approach by combining computational science, control engineering and statistical methodologies to ensure a resilient process with the best use of available resources.

Taguchi's robust design methodology and multi-response optimisation approaches are adopted to meet the research aims. Two-Input-Two-Output transfer function model of the distillation column system is investigated. In designing the control system, the Steady State Gain Matrix and process factors such as time constant (t) and time delay (?) are also used. The unique methodology is implemented and validated using the pilot plant's distillation column. To determine the robustness of the proposed control system, a simulation study, statistical analysis and real-time experimentation are conducted. In addition, the outcomes are compared to different control algorithms.

Research indicates that integral control parameters (K_i) affect outputs substantially more than proportional control parameters (K_p). The results of this paper show that control and noise parameters must be considered to make the control system robust. In addition, Taguchi's approach, in conjunction with multi-response optimisation, ensures robust controller design with optimal use of resources. Eventually, this research shows that the best outcomes for all the performance indices are achieved when Kp11 = 1.6859, Kp12 = −2.061, Kp21 = 3.1846, Kp22 = −1.2176, Ki11 = 1.0628, Ki12 = −1.2989, Ki21 = 2.454 and Ki22 = −0.7676.

This paper provides a step-by-step strategy for designing and validating a multi-response control system that accommodates controllable and uncontrollable parameters (noise parameters). The methodology can be used in any industrial Multi-Input-Multi-Output system to ensure process robustness. In addition, this paper proposes a multidisciplinary approach to industrial controller design that academics and industry can refine and improve.

This study aims to propose a unique algorithm-based hysteresis current control technique (HCCT) for induction motor using a single-phase voltage source inverter (SPVSI) to eliminate both sub and inter harmonics (SIH) and electromagnetic interference (EMI). The total harmonic distortion (THD) of the load current also reduces in comparison to standard HCCT and modified technique-based existing HCCT.

Matlab simulation has been carried out to develop an SPVSI model and the unique algorithm-based HCCT. The same platform has also been used to develop a few existing HCCTs such as standard, dual-band and modified. The switching frequency and harmonic analysis of load currents for all the HCCTs have been compared in the paper. The hardware implementation of the proposed algorithm-based HCCT was also verified and compared with the simulation results.

The proposed unique algorithm-based HCCT provides the benefits of both unipolar and bipolar switching techniques. It reduces the switching frequency as unipolar switching scheme and eliminates the EMI. It also reduces THD and nullifies SIH of the load current. This enables an improvement in the overall performance and efficiency of the motor.

This proposed HCCT eliminates the SIH and improves the overall efficiency of the motor, hence can prevent overheating, vibration, acoustic noise, pulsating torque and braking of the rotor shaft of the motor and increasing the reliability of the system.

It can be implemented for the motors that are used in household applications and electric vehicles through one-phase inverter.

This proposed HCCT has detected the zero crossing point of reference current, allowed samples and shifted the necessary amount of hysteresis band at zero crossing region to eliminate SIH and THD.

The direct torque control (DTC) of induction motor (IM) drive is featured by high ripples in the electromagnetic torque and stator flux profiles because they are controlled by two hysteresis regulators. Furthermore, the machine flux is not directly measurable. Hence, it is better to reconstitute it from the instantaneous electrical equations of the machine. Once the stator flux is estimated, we can guarantee a reliable sensorless DTC control. Thus, the purpose of this research work is to ensure fast response and full reference tracking of the IM under sensorless DTC strategy with desired dynamic behavior and low ripple levels.

In this work, an improved DTC strategy, which is DTC_SVM_3L, is suggested. The first step of the designed approach is to substitute the conventional inverter feeding the motor with a three-level inverter because it guarantees reduced switching losses, improved quality of voltage waveform and low-current total harmonic distortion rate. The second aim of this paper is to make the IM operate at a constant switching frequency by using the nearest three vectors-based space vector modulation (SVM) technique rather than hysteresis controllers. The third objective of this study is to conceive a sliding-mode stator flux observer, which can improve the control performances by using a sensorless algorithm to get an accurate estimation, and consequently, increase the reliability of the system and decrease the cost of using sensors. The stability of the proposed observer is demonstrated based on the Lyapunov theory. To overcome the load change disturbance in the proposed DTC control strategy, this paper exhibits a comparative assessment of four speed regulation methods: classical proportional and integral (PI) regulator, fuzzy logic PI controller, particle swarm optimization PI controller and backstepping regulator. The entire control algorithm was tested under different disturbances such as stator resistance and load torque variations.

It was ascertained that the IM, controlled with three-level inverter, exhibits good performances under the proposed DTC-SVM strategy based on a sliding-mode observer. The robustness of the suggested approach against parameter variations is also proved.

The theoretical development of the proposed control strategy is thoroughly described. Then, simulations using Matlab/Simulink software are launched to investigate the merits of the sensorless DTC-SVM command of three-level inverter-fed IM drive with different speed regulators.

The study proposed a human–robot interaction (HRI) framework to enable operators to communicate remotely with robots in a simple and intuitive way. The study focused on the situation when operators with no programming skills have to accomplish teleoperated tasks dealing with randomly localized different-sized objects in an unstructured environment. The purpose of this study is to reduce stress on operators, increase accuracy and reduce the time of task accomplishment. The special application of the proposed system is in the radioactive isotope production factories. The following approach combined the reactivity of the operator’s direct control with the powerful tools of vision-based object classification and localization.

Perceptive real-time gesture control predicated on a Kinect sensor is formulated by information fusion between human intuitiveness and an augmented reality-based vision algorithm. Objects are localized using a developed feature-based vision algorithm, where the homography is estimated and Perspective-n-Point problem is solved. The 3D object position and orientation are stored in the robot end-effector memory for the last mission adjusting and waiting for a gesture control signal to autonomously pick/place an object. Object classification process is done using a one-shot Siamese neural network (NN) to train a proposed deep NN; other well-known models are also used in a comparison. The system was contextualized in one of the nuclear industry applications: radioactive isotope production and its validation were performed through a user study where 10 participants of different backgrounds are involved.

The system was contextualized in one of the nuclear industry applications: radioactive isotope production and its validation were performed through a user study where 10 participants of different backgrounds are involved. The results revealed the effectiveness of the proposed teleoperation system and demonstrate its potential for use by robotics non-experienced users to effectively accomplish remote robot tasks.

The proposed system reduces risk and increases level of safety when applied in hazardous environment such as the nuclear one.

The contribution and uniqueness of the presented study are represented in the development of a well-integrated HRI system that can tackle the four aforementioned circumstances in an effective and user-friendly way. High operator–robot reactivity is kept by using the direct control method, while a lot of cognitive stress is removed using elective/flapped autonomous mode to manipulate randomly localized different configuration objects. This necessitates building an effective deep learning algorithm (in comparison to well-known methods) to recognize objects in different conditions: illumination levels, shadows and different postures.

The wind turbine (WT) is a complex system subjected to wind disturbances. Because the aerodynamics is nonlinear, the control is thus challenging. For the variation of wind speed when rated power is delivered at rated wind speed, the power is limited to the rate by the pitching of the blades of the turbine. This paper aims to address pitch control with the WT benchmark model. The possible use of appropriate adaptive controller design that modifies the control action automatically identifying any change in system parameters is explored.

To deal with pitch control problem when wind speed exceeds the rated wind speed of the WT, six digital self-tuning controller (STC) with different structures such as proportional integral (PI), proportional derivative (PD), Dahlin’s, pole placement, deadbeat and Takahashi has been taken herein. The system model is identified as a second-order autoregressive exogenous (ARX) model by three techniques for comparison: recursive least square method (RLS), RLS with exponential forgetting and RLS with adaptive directional forgetting identification methods. A comparative study of three identification methods, six adaptive controllers with the conventional PI controller and sliding mode controller (SMC), are shown.

As per the results, the best improvement in control of the output power by pitching in full load region of benchmark model is achieved by self-tuning PD controller based on RLS with adaptive directional forgetting method. The adaptive control design has a future in WT control applications.

A comparative study of identification methods, six adaptive controllers with the conventional PI controller and SMC, are shown here. As per the results, the best improvement in control of the output power by pitching in the full load region of the benchmark model has been achieved by self-tuning PD controller. The best identification method or the system is RLS with an adaptive directional forgetting method. Instead of a step input response design for the controllers, the controller design has been carried out for the stochastic wind and the performance is adjudged by the normalized sum of square tracking error (NSSE) index. The validation of the proposed self-tuning PD controller has been shown in comparison to the conventional controller with Monte-Carlo analysis to handle model parameter alteration and erroneous measurement issues.

Non-linear power–voltage characteristics of solar cell and frequently changing output due to variation in solar irradiance caused by movement of clouds are the major issues need to be considered in photovoltaic (PV) penetration to maintain the power quality of the grid. It is important for a PV module to always function at its maximum available power point to increase the efficiency and to maintain the grid stability. A possible solution to mitigate these generation fluctuations is the use of an electric double-layer capacitor or supercapacitor energy storage device, which is an efficient storage device for power smoothing applications. This study aims to propose a power smoothing control approach to smoothen out the output power variations of a solar PV system using a supercapacitor energy storage device.

To extract the maximum possible power from a PV panel, there are several maximum power points tracking (MPPT) algorithms developed in literature. Fuzzy logic controller-MPPT method is used in this work as it is a very efficient and popular technique which responds quickly under varying ecological conditions, reduced computational complexity and does not depend on any system constraints. Fuzzy logic-based MPPT controller by Boost DC–DC converter is developed for operating the PV panels at available maximum power point. Fuzzy logic-proportional integral (PI) charge controller is implemented by Buck–Boost converter to provide the constant current and suitable voltage for supercapacitor and to achieve better power smoothing. PI charge controller is preferred in this work as it offers better outcomes and is very easy to implement.

Simulation results conclude that the proposed power smoothing control approach can efficiently smooth out the power variations under variable irradiance and temperature situations. To confirm the accurateness of the proposed system, it is validated for poly-crystalline PV module and comparison of results is done by using different case study with and without the use of an energy storage system under change in irradiance condition. The proposed system is developed and examined on MATLAB/Simulink environment.

The performance comparison between PV power output with and without the use of a supercapacitor energy storage device under different Case Studies shows that the improved performance in smoothing of power output was achieved with the use of a supercapacitor energy storage device.

At the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs).

The method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements.

The discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO₂) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment.

Finally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO₂ based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.

In this paper, a new front end converter (FEC) topology has been proposed for the switched reluctance (SR) motor drive. This study aims to present the performance analysis of FEC-based SR motor drive using various types of control schemes like conventional proportional integral (PI) controller, fuzzy logic controller (FLC) and fuzzy-tuned proportional integral controller (Fuzzy-PI).

The proposed FEC-based SR motor drive with various control strategies is derived for the torque ripple minimization and speed control.

The steady state and the dynamic response of the FEC-based SR motor drive are analyzed using three different controllers under change in speed and loading conditions. The Fuzzy-PI-based control scheme improves the dynamic response of the system when compared with the FLC and the conventional PI controller.

The hardware prototype has been implemented for the FEC-based SR motor drive by using the Xilinx SPARTAN 6 FPGA processor. The experimental verification has been conducted and the results have been measured under steady state and dynamic conditions.

The purpose of this study is to provide the enhancement of power quality of a high power-rated voltage source inverter driven induction motor with a three-phase, three-level neutral point clamped converter placed at the front end, while a passive power filter is connected in shunt with it. The improvement in power quality can be achieved by reducing the total harmonic distortion in source current. The controllers were designed for the linearization of the high-power induction motor drive. A control method is presented for the regulation of the common DC-link voltage.

The induction motor is modeled using its dynamic equations, and a decoupling controller is designed to linearize the nonlinear dynamics of the drive through feedback. The common DC-link voltage of the proposed front-end connected converter is monitored and controlled through a control method which feeds the pulse width modulated inverter that drives the induction motor. A passive power filter is designed to meet the reactive power requirement of the system in addition to improve the power quality.

Simulations were carried out for the proposed topology of the drive mechanism, and the outcomes were analyzed by a comparative analysis of the drive system both in the presence of the passive filter as well as in the absence of the filter. The total harmonic distortion is found to be reduced enough to meet the standards with the designed filter, and the reactive power is also compensated considerably. The input power factor at the supply side is maintained almost to unity, and the DC-link voltage of the proposed circuit topology is maintained at the desired level. The overall performance of the drive system was found to be useful and economical.

A new topology of a front-end connected three-level neutral point clamped converter to a high power-rated induction motor drive is proposed. The drive is fed by a pulse width modulated inverter with a common DC-link with the front end connected converter. A passive filter is designed with respect to the reactive power requirement of the system and connected in shunt to the converter at the supply side. Control schemes are designed and used for the drive system and also for the regulation of the common DC-link voltage of the proposed front end connected converter.