A REVIEW AND COMPARATIVE ANALYSIS OF AUTOMATED PROCESS CONTROL METHODS IN MODERN MANUFACTURING WITHIN THE CONTEXT OF INDUSTRY 5.0

A Review and Comparative Analysis (2021-2025) on Various Types of Temperature Controllers

Temperature control is one of the key processes in industrial, thermal, food, chemical, microchip manufacturing, 3D-printing, energy, and laboratory equipment. From the simplest on–off control and PID schemes to fuzzy, neural, model predictive, sliding-mode, adaptive, and reinforcement-learning control, temperature controllers are extensively studied and applied. This review article compares the main types of temperature controllers regarding their accuracy, overshoot, settling time, computational complexity, robustness, model dependency, ease of implementation, and applicability to nonlinear/variable thermal systems. Particular emphasis is placed on recently published literature reviews and journal articles from 2021 to 2025. While the transition from PID to more sophisticated approaches is evident, increased “intelligence” does not always translate into better performance due to complexities of implementation and the need for training data. In terms of accuracy, overshoot, and implementation simplicity, the review highlights PID control, fuzzy-PID, and PID-based adaptive controllers as the best performers. For multivariable systems with constraints, model predictive control (MPC) is also superior. Neural-network and reinforcement-learning methods are highlighted as the future of temperature control.

Enhancing Production Automotive Industry Performance with Ishikawa's 5M Method and Fuzzy Logic

The digitalization in the dynamic landscape of the automotive industry, achieving optimal production processes is a fundamental imperative. This article delves into a robust approach that combines Ishikawa's 5M method and fuzzy logic to significantly enhance production performance. The 5M method, encompassing Manpower, Machinery, Material, Method, and Measurement, serves as the foundational framework for dissecting factors influencing production. Meanwhile, fuzzy logic, a contemporary mathematical framework, introduces flexibility to decision-making in handling complex, uncertain variables. The assessment of Manpower Expertise, Machinery Condition, Material Quality, Method Efficiency, and Measurement Accuracy is essential. In the automotive industry, the proficiency of the workforce, machine reliability, material quality, operational efficiency, and precision of measurements collectively define success. Ishikawa's 5M method offers a structured approach to dissecting and addressing these crucial elements. Fuzzy logic, on the other hand, bridges the gap between qualitative assessments and quantifiable outputs. By assigning linguistic variables and membership functions, it allows for nuanced analysis. Rule-based decision-making in fuzzy logic draws connections between input and output variables, aiding in the interpretation of complex relationships. This adaptive system evolves with time, enhancing its accuracy and predictive capabilities. The integration of Ishikawa's 5M method and fuzzy logic results in a powerful tool for enhancing production performance. Quality, productivity, safety, and customer satisfaction—integral outcomes in the automotive industry—are no longer subjective measures. Instead, they are quantified, analyzed, and optimized through a synergistic approach. By leveraging both time-tested methodologies and modern techniques, manufacturers can navigate the challenges of the industry and usher in a new era of excellence. This article serves as a guide to harnessing the potential of Ishikawa's 5M method and fuzzy logic in achieving remarkable production performance in the automotive industry. Through a deeper understanding of the interplay between human expertise, machinery reliability, material quality, method efficiency, and measurement accuracy, coupled with the dynamic capabilities of fuzzy logic, manufacturers are empowered to elevate their production processes to unparalleled heights. Keywords: Ishikawa, 5M, Fuzzy logic, automotive industry, performance.     Received Date: June 19, 2026 Accepted Date: July 10, 2026 Published Date: August 01, 2026 Available Online at: https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/867

11 Model-Based Controlling Approaches for Manufacturing Processes

A Review of Production Line Balancing and Optimization for Manufacturing Enterprises in the Context of Industry 4.0

As global manufacturing moves toward intelligent, digital transformation, the concept of Industry 4.0 has created an enormous demand for highly flexible and responsive production systems. To meet this demand, manufacturing enterprises of all kinds are actively adopting advanced technologies such as the Internet of Things, digital twins, and artificial intelligence to optimize production processes and reduce resource waste. This paper provides a comprehensive and systematic review of the current state of research on production line balancing and its optimization strategies in the context of Industry 4.0, in order to fill the conceptual gap left by the unclear relationship between traditional line balancing theory and the dynamic requirements of modern intelligent manufacturing. The findings show that academic attention has gradually shifted from traditional mathematical programming and static heuristic algorithms toward dynamic planning theories driven by real-time data, human-machine collaboration, and cyber-physical systems. The intelligent manufacturing technologies that enterprises adopt at the strategic and tactical levels profoundly shape line balance and efficiency through distinct elements and mechanisms. This review helps managers and production engineers identify best practices in the field and thus provides effective guidance for building smart factories and making operational decisions.

Artificial intelligence for process modeling and optimization in precision machining: a review

Digital Twin-Based Predictive Control for Intelligent Manufacturing

Industry 4.0 has evolved traditional manufacturing into a highly connected, data-driven, intelligent production environment. Digital twin (DT) and predictive control have been considered as two of the enabling technologies in industrial automation that can greatly enhance the manufacturing efficiency, operational cost reduction, and product quality improvement. Digital Twin — In the context of manufacturing, a digital twin is a virtual model of a manufacturing system that continuously receives live operational data using IoT devices, cloud computing and artificial intelligence (AI). By integrating predictive control strategies, Digital Twins predict system behaviour (particularly Model Predictive Control (MPC)), and provide timely optimised actions before any faults/ inefficiencies happen. This is a survey on the state of art works related to Digital Twin-based Predictive Control for intelligent manufacturing systems, including its architecture, enabling technologies and practical applications. This discusses the role of Digital Twins in real-time monitoring, predictive maintenance, process optimization, Quality assurance and energy-efficient manufacturing. Moreover, the novel integration of machine learning algorithms enables Digital Twins to leverage big data in manufacturing (various plant operational informatics) as well as dynamically update control strategies based on changing operational environments. While researchers have made strides towards realizing Digital Twins, challenges still remain regarding abundance of computational requirements, inherent cybersecurity security risks, interoperability across applications and systems, pricey to implement solutions and absence of standardized Digital Twin frameworks. These research gaps are highlighted in this paper, and future directions towards autonomous self-optimizing manufacturing systems are discussed. The conjoining between Digital Twin technology and predictive control proposed in this article offers a paradigm of smart manufacturing that facilitates increases in production flexibility, reductions in downtime, increases in resource utilization and sustainable development. These results show that Digital Twin-based predictive control can be an important technological basis for next-generation intelligent manufacturing environments.