Continuous improvement activities are widely deployed, applying the DMAIC cycle in the Lean Six Sigma method combined with 5s activities and industrial engineering tools such as Man-Machine chart statistics tools in DFSS (Design For Six Sigma). The results of the improvement activity must be approved and operated by the machine operator, measuring the loyalty of operators, users and system maintainers after kaizen against satisfaction criteria, technicality, usefulness and convenience are needed. This study proposes a model that com- bines the PLS - SEM method to measure user loyalty and implement a training program for users on incorrect performance results to improve CDIO stan- dards. The result is a reduction of workers at the processing line from 4 people on two shifts to 2 people, and the amount of money brought in is 10,224 USD per year. Defect of negative outside diameter decreased from 31.2% to 4.5% based on the amount of waste reduction of USD 980 per year. In terms of productivity increased from 15 units per hour per person to 30 units.

Shunt Adaptive Power Filter (SAPF) is widely used in the performance of power quality improvement activities in the power supply industry for processing industries or civil power sources in the world today based on its simplicity, transparency, high reliability, efficiency, and reliability, and their powerful compensating current-providing nature. The PI controller integrated into the SAPF operation mechanism works with extra high efficiency in selecting the current to compensate for the lost current generated in the power supply due to harmonics generated by the $K_p, K_i$ parameter values. The system operates by the PWM method for bridge rectifier circuits that perform the function of selecting the appropriate compensating current, providing correct compensation for the amount of current loss in the power supply. Adjusting the $K_p, K_i$ parameter to reach the optimal value by different methods is a promising and popular research direction at present. The $K_p, K_i$ parameter serves the right purpose for the PI controller to generate enough PWM pulses to excite the bridge rectifiers to generate just the right amount of compensating current and enough current to be compensated on the power supply. The commonly used $K_p, K_i$ parameter adjustment methods include the Ziegler Nichols closed-loop vibration method, the P-Q theoretical method, and several other methods. This study conducts a comprehensive review of the literature on modern strategies for adjusting the $K_p, K_i$ parameters in the PI controller in the SAPF suite by using the meta-heuristic optimization method. This study performs classification according to the operation mode of meta-heuristic optimization methods to adjust the $K_p, K_i$ parameter to control the PI to select the correct PWM frequency to activate bridge rectifiers to select the most optimal compensation current to compensate for the loss of current on the power supply to meet the goal of improving power quality in accordance with IEEE 519-2022 standard, leading to the total harmonic distortion (THD) value is below 5%. The study presents in detail some meta-heuristic optimization algorithms, including applications, mathematical equations, and implementation of flow charts for SAPF and provides some open problems for future research. The main objective of this study is to provide an overview of applying meta-heuristic optimization algorithms to the $K_p, K_i$ parameter tuning of PI controllers.

This study describes in detail step-by-step implementation of quality improve- ment activities in mechanical processing plants. Describe how to imple- ment quality 4.0 technologies in DMAIC (Define-Measure-Analysis-Improve- Control) phases. As well as using statistical formulas, experimental design in data analysis at each phase of DMAIC. This study proposes to use Quality 4.0 Technology in product quality improvement activities in mechanical product processing factories with the aim of becoming zero defect manufacturing. The results of the research found are repair rate reduced from 600 PPM monthly to 0 PPM, processing capacity increased from Cpk1.02 to Cpk2.56, reduce time for inspection product from 702 hours per year (calculated to save USD 2106 per year), reduce the amount of repair products by 196 products per year (calculated in terms of money is reduced by 917 USD per year) and reduce 1 roughing stage (calculated in terms of cost reduction about 171288 USD per year). The roughness dimension has reduced measurement time by about 364 hours per year (save 1092 USD per year). Processing digital signals from sen- sors in an oily environment is a big challenge for researchers. Improving the security of digital data is also a limitation of this study. This study proposes a model to apply statistical hypothesis testing methods to analyze real data collected from each machining stage and perform each job according to each corresponding DMAIC phase of the model. In addition, digital processing tech- niques and computer vision techniques are also deployed in the improve phase to complete the goal of improving the semiautomatic production stage to the automatic production stage.

Intelligent integrated production systems are always of interest to production planners. However, in order to deploy and improve from a mere processing factory to a processing factory with an integrated intelligent production system, it requires a team of employees, engineers, and managers to always have a spirit of innovation, continuously improving existing semi-automatic equipment into automatic ones, aiming to move towards a smart factory. The result of this research is to reduce waste in the process of assembling mechanical products by applying the DMAIC process (Define, Measure, Analysis, Improve, Control), lean six sigma tools, the test of variance, hypothesis testing and experimental design. IBM SPSS 2020, Matlab2019a, and Minitab 18 software are used for data analysis and Solid work software is used to design and simulate mechanical parts. This study shows a systematic approach to analysis to find the root cause of defects in the product assembly process, a method of diagnosing defective products as well as the application of charts to the analysis of waste products, and improving quality by applying basic quality management tools such as Pareto charts, fishbone diagrams, value stream mapping, man-machine chart, and failure tree analysis (FTA). Clearly identify the types of waste such as components sliding on top of each other without rust, and surface roughness of metal products that do not meet the standards. Experimental design models and statistical models, and statistical tests are applied to Lean six sigma’s DMAIC model in the process of analyzing the machining process. The results of analysis and process improvement have improved in a reduction of scrap in the assembly line of mechanical products by 59.66% per year, an increase in assembly-line productivity by 7.8% per year, and a decrease in waste costs incurred by 59.66% per year. The application of the DMAIC cycle to improve the quality of the assembly line of mechanical products, in addition to reducing waste, also reduces the quality cost of the assembly line.

Harmonic estimation is essential for mitigating or suppressing harmonic distortions in power systems. The most important idea is that spectrum analysis, waveform estimation, harmonic source classification, source location, the determination of harmonic source contributions, data clustering, and filter-based harmonic elimination capacity are also considered. The feature extraction method is a fundamental component of the optimization that improves the effectiveness of the Harmonic Mitigation method. In this study, techniques to extract fundamental frequencies and harmonics in the frequency domain, the time domain, and the spatial domain include 67 literature reviews and an overall assessment. The combinations of signal processing with artificial intelligence (AI) techniques are also reviewed and evaluated in this study. The benefit of the feature extraction methods is that the analysis extracts the powerful basic information of the feedback signals from the sensors with the most redundancy, ensuring the highest efficiency for the next sampling process of algorithms. This study provides an overview of the fundamental frequency and harmonic extraction methods of recent years, an analysis, and a presentation of their advantages and limitations.