Journals of Mechatronics Machine Design and Manufacturing

Industry 4.0 - Driven Production Systems: A Comprehensive Review of Process Optimization, Productivity, and Sustainability

Azazullah — 2026-08-27

Modern manufacturing systems are under increasing pressure to achieve higher productivity while reducing production losses, resource consumption, and environmental impacts. Industry 4.0 offers a new approach to these challenges by enabling production systems to use real-time data, intelligent decision-making, and digitally integrated operations. This review examines Industry 4.0-driven production systems from three interconnected perspectives: process optimization, manufacturing productivity, and sustainability. Rather than considering digital technologies as isolated solutions, the study evaluates their role in improving production monitoring, maintenance planning, scheduling, quality control, process parameters, machine utilization, and resource management. Manufacturing performance is assessed through key indicators including Overall Equipment Effectiveness (OEE), throughput, cycle time, downtime, quality performance, energy efficiency, material utilization, and waste generation. The review further compares the applicability of digital production approaches across mass, batch, flexible, process, and small-scale manufacturing environments. The findings indicate that effective integration of digital capabilities with production processes can improve operational responsiveness, reduce production losses, enhance resource utilization, and support sustainable manufacturing objectives. However, implementation effectiveness is influenced by investment requirements, interoperability, data availability, cybersecurity, workforce competence, legacy infrastructure, and organizational readiness. Based on the reviewed evidence, an integrated framework linking digitalization, process optimization, productivity improvement, and sustainability performance is proposed. The review provides a production-oriented perspective for evaluating Industry 4.0 implementation and identifying suitable digital interventions according to manufacturing-system characteristics and performance objectives.

Dynamic Modelling of Automated Weight-Based Fish Sorting Machine

Kokoro Abraham Abraham — 2026-09-22

This paper presents the dynamic modelling and performance analysis of an automated weight-based fish sorting machine developed for small-scale aquaculture and fish processing operations. The machine integrates a motorised conveyor belt subsystem, a load cell weighing station modelled as a second-order dynamic system, and pneumatically actuated gate mechanisms for three-category weight-based classification of fish (small: <300 g; medium: 300–600 g; large: >600 g). Mathematical models were developed for the conveyor belt drive dynamics using Newton's second law, the load cell response using a damped harmonic oscillator formulation, and the pneumatic actuator using a first-order transfer function. Simulation results demonstrate a load cell settling time of approximately 0.65 s with 8.4% overshoot at a natural frequency of 12 rad/s and a damping ratio of 0.55. The optimal belt speed of 0.30 m/s was identified, yielding a maximum throughput of 60 fish per minute with 92.3% sorting accuracy. The design and dynamic behaviour of each subsystem are presented alongside a complete bill of materials and performance characterisation tables, providing a validated framework for the realisation of the machine.

Intelligent Vision-Based Inspection Techniques for Modern Manufacturing

Rahul Kumar — 2026-09-24

As artificial intelligence becomes increasingly integrated into vision-based industrial automation equipment, a new generation of inspection systems has emerged, enabling faster and more accurate quality control to be performed simultaneously with the manufacturing process. The current study highlights the use of state-of-the-art sensors, artificial intelligence, robotic manipulators, and Internet of Things-based data mining to identify imperfections and measure dimensions across different production sectors. The use of machine vision, high-precision lenses, and defect detection/classification deep learning modules leads to fully automatized quality checking with minimal human involvement. In addition, by integrating advanced manufacturing and operations techniques like 3D scanning, edge computing, and predictive maintenance, the system efficiency is further enhanced. The sectors of electronics, automotive, and precision production products were chosen for demonstration purposes to illustrate how these methods improved the time for one control step, the percentage of defects found, as well as total throughput. The findings are evidence of this technology’s capacity not only to assist with quality control but also to function as an important component of smart manufacturing and Fourth Industrial Revolution plans.