Search Results (352)

Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these conditions, requiring extensive retuning and dense instrumentation. Recent safe multi-agent reinforcement learning (MARL) frameworks with control barrier functions (CBFs) achieve real-time constraint satisfaction in robotics and power systems, yet assume comprehensive state observability—incompatible with sensor-hostile industrial environments where instrumentation degradation and contamination risks dominate design constraints. This work presents a safety-aware multi-agent deep reinforcement learning framework for adaptive fault-tolerant control in sensor-lean industrial environments, achieving formal safety through learned implicit barriers under partial observability. The framework integrates four synergistic mechanisms: (1) multi-layer safety architecture combining constrained action projection, prioritized experience replay, conservative training margins, and curriculum-embedded verification achieving zero constraint violations; (2) multi-agent coordination via decentralized execution with learned complementary policies. Additional components include (3) curriculum-driven sim-to-real transfer through progressive four-stage learning achieving 85–92% performance retention without fine-tuning; (4) offline extended Kalman filter validation enabling 70% instrumentation reduction (91–96% reconstruction accuracy) for regulatory auditing without real-time estimation dependencies. Validated through sustained deployment in commercial beverage manufacturing clean-in-place (CIP) systems—a representative safety-critical testbed with hard flow constraints (≥1.5 L/s), harsh chemical environments, and zero-tolerance contamination requirements—the framework demonstrates superior control precision (coefficient of variation: 2.9–5.3% versus 10% industrial standard) across three hydraulic configurations spanning complexity range 2.1–8.2/10. Comprehensive validation comprising 37+ controlled stress-test campaigns and hundreds of production cycles (accumulated over 6 months) confirms zero safety violations, high reproducibility (CV variation < 0.3% across replicates), predictable complexity–performance scaling ($R^2=0.89$), and zero-retuning cross-topology transferability. The system has operated autonomously in active production for over 6 months, establishing reproducible methodology for safe MARL deployment in partially-observable, sensor-hostile manufacturing environments where analytical CBF approaches are structurally infeasible. Full article

Peru’s highly competitive garment sector, where microenterprises account for 88.4% of all enterprises, the main barrier to sustainability is low productivity, driven by high rework rates and operational errors. To address this problem, this research proposes an improvement model that combines Standardized Work to unify processes with a Poka Yoke technological solution using artificial vision for real-time defect prevention. This dual approach addresses the root causes of inefficiency, merging a core principle of Lean Manufacturing with an accessible Industry 4.0 tool designed for implementation in resource-constrained environments. The validation of the model demonstrated its remarkable effectiveness, achieving transformative results: the sewing rework rate was drastically reduced from 28.43% to 8.94%, the labeling rework rate were reduced from 18.02% to 3.88%, the production cycle time was optimized from 23.74 to 16.54 min per garment, with a 173.74% increase in productivity. These results not only confirm the validity of the model, but, due to its basis in universal principles and scalable technology, they also guarantee high applicability and replicability in other micro and small companies in the sector, offering a clear path towards a qualitative leap in productivity and competitiveness. Full article

This study applies to a virtual commissioning (VC) workflow with discrete-event simulation in WITNESS Horizon to diagnose and improve an automated brushless stator assembly line. A validated model of the full route—Stator Assembly Machine (SAM), Linear Transport System (LTS), Winding Machine (WM), Terminal Welding Machine (TWM), Inspection Machine (IM) and Electric Tester (ET)—was executed over a one-shift horizon (28,800 s). We compared the baseline configuration with an optimized scenario that retrieved robot tasks and refined LTS routing. Key performance indicators (KPIs) were resource utilization (Busy/Idle/Blocked) and completed operations. The results are quantitative and specific. Blocking at the SAM interface collapsed from 73.32% to 0% at PressPosition and from 80.64% to 0% at Robot2. LTS transitioned from 97.46% Blocked to 0%, with the share of Move/Running increasing to 14.76% (from ~0%). Line output—measured as completed assemblies at SAM—increased from 368 to 425 units per shift (+15.5%). Similar gains were recorded at other stations (e.g., WM1: 351 → 424 operations, +20.8%). These changes reflect the removal of the primary transfer bottleneck and a more balanced utilization across stations. The study demonstrates that VC can deliver actionable commissioning guidance. By quantifying where blocking occurs and testing alternative control strategies in a virtual environment, it is possible to raise throughput while maintaining stable operation. The modeling approach and metrics are reusable for related electromechanical assembly lines. Full article

Enterprises play a key role in achieving sustainable development goals because they affect them to a greater or lesser extent, both in a positive and negative way. The aim of the study presented in the article is to answer the question concerning the level of application of the best practices in enterprises related to the implementation of sustainable development strategy. As recognized means of operational activities in organizations, they are a guarantee of the effective achievement of their goals. The method employed in the research procedure was the Human Lean Green method. Thus, best practices applied in the enterprises analyzed became the basis for measuring their organizational maturity in three areas of sustainable development, i.e., social (Human), economic (Lean) and environmental (Green). The study was conducted in 20 enterprises (manufacturing or service enterprises). The results of the research show, among others, that the popularity of using practices from the Human area is greater than Lean Green practices. Full article

Standard operating procedures (SOPs) serve as critical control mechanisms in manufacturing systems, yet systematic approaches for quantifying procedural inefficiencies remain theoretically underdeveloped. Unlike traditional qualitative SOP analysis methods that rely on expert intuition and subjective assessment, current procedural optimization approaches lack the systematic rigor applied to physical process improvement. While lean manufacturing principles have demonstrated effectiveness in physical process optimization, their systematic application to procedural analysis represents an unexplored theoretical domain with significant potential for manufacturing systems improvement. This research addresses this gap by developing the Procedural Waste Index (PWI) framework, which establishes the first systematic theoretical integration of lean waste identification principles with procedural analysis. The framework extends the seven wastes of lean manufacturing to procedural analysis through systematic mapping to procedural elements identified via the extended Procedure Representation Language (e-PRL), creating a quantitative approach that enables the objective measurement of procedural efficiency where only subjective assessment methods previously existed. The PWI framework provides the following three key advantages over existing approaches: (1) systematic waste identification using proven lean principles rather than ad hoc improvement methods, (2) quantitative measurement capability enabling objective assessment and statistical process control, and (3) multi-perspective analytical framework through three complementary calculation methodologies (weighted aggregation, maximum constraint identification, and root mean square analysis) providing comprehensive analytical perspectives on procedural waste across discrete manufacturing contexts. The theoretical framework demonstrates practical applicability through a systematic analysis of a respirator fit testing procedure, revealing inventory waste as the primary inefficiency (70.0% waste score). This represents the first quantitative procedural waste assessment in the manufacturing literature, contributing to the foundational theory for systematic procedural optimization while establishing a methodology for future empirical validation studies. Full article

Lean manufacturing emphasized reducing waste and improving efficiency, with the 5S methodology, Seiri (Sort), Seiton (Set in Order), Seiso (Shine), Seiketsu (Standardize), and Shitsuke (Sustain) as key tools. This study explored 5S implementation in a laboratory of a university, which initially suffered from disorganization, inefficiencies, and wasted resources. The intervention involved data collection, discussions with lab technicians and students, and layout mapping. After applying the first 4S steps, the lab realized marked improvements in organization, cleanliness, and workflow. Designated storage improved space use, while time-motion studies showed an average 78.6 s reduction in activity times, saving 632 s weekly. A 54% efficiency enhancement has also been achieved. The successful implementation created a safer and more efficient lab environment. The final step, Shitsuke, ensured sustained improvements through training, cleaning schedules, and time management tools. This paved the way towards a green university. Full article

In the industrial internet environment, the operation and control of production equipment have become increasingly complex, and their performance directly affects the efficiency, benefits and sustainable development of manufacturing enterprises. From the three-dimensional perspective of “asset-application-maintenance”, this paper constructs a performance analysis framework for the operation and control of production equipment, systematically identifies the combination of core factors affecting performance, and fills the research gap in the current lack of empirical analysis from the configuration perspective in this field. On the basis of data from 82 manufacturing enterprises, the fsQCA method was used to identify three performance improvement paths: the high-load output mode, the lean management and control mode, and the low-failure operation mode. These paths clarify the equivalent approaches to achieve high performance in the operation and control of production equipment under the interaction of multiple factors. On this basis, the study demonstrates the operability and effectiveness of the proposed strategies in actual industrial scenarios through empirical verification in a manufacturing workshop of aero-engine transmission units. In contrast to existing studies, this study introduces the fsQCA method in the field of industrial equipment management and control for the first time to reveal the influencing paths; its originality and methodology have significant innovative significance. The research results provide new ideas and methodological guidance for enterprise managers to improve the performance of production equipment operations and controls in the industrial internet environment, which helps to enhance the sustainable development capability of manufacturing enterprises. Full article

This study implemented a Lean Six Sigma (LSS) methodology to enhance the productivity of the mechanical and industrial engineering technology workshops of an international higher education institution. The efficiency and effectiveness of the engineering workshops were often compromised by poor housekeeping and operational practices, which resulted in incomplete tasks, long operational and activity times, disorganized tools, cluttered workspaces, and a lack of systematic processes for managing materials. These issues led to waste in the form of lost time, unnecessary movement, and safety risks. This eventually affected the overall productivity of the workshops. Following the combination of the Define, Measure, Analyze, Improve, and Control (DMAIC) methodology of Six Sigma with Lean manufacturing, the investigation was conducted in two parts. The first part of this research mainly consisted of measuring the existing state of the three workshops to map the process and frame issues and origins of variations. During the second part of this study, the focus shifted towards Lean thinking while applying the chosen Lean Six Sigma (LSS) tools. Implementation revealed several benefits in the workshops during each phase of DMAIC. A Plan–Do–Check–Act (PDCA) continuous improvement board was installed in the main workshop to promote continuous improvement and sustainability. The process capability increased for the main workshop and welding laboratory, which shows an increase in service and performance standards after LSS implementation. For the main workshop, the process capability ‘Cp’ increased from 0.33 to 1.24 and the process capability index (Cpk) increased from 0.26 to 0.99. The process capability index (Cpk) for the main workshop increased; however, it did not reach the value of 1.33 due to the computer workstation installation not being completed during the study. The welding laboratory showed an increased ‘Cp’ from 0.67 to 2.13, and the process capability index (Cpk) increased from 0.18 to 1.34. The layout of the workshop office was improved to support efficient workflow by providing easy access to frequently used resources while keeping movement paths clear, thereby minimizing interruptions and promoting productivity. As a result, machines and tools were used more productively and operation times decreased. The mechanical workshops can continue increasing their process capability by following the outcomes and findings of the current study, leading to sustainable quality, efficiency, and operational reliability improvements. Full article

Welding-based manufacturing and joining processes are extensively used in various areas of industrial production. While welding has been used as a primary method of joining in many applications, its capability to fabricate metal components such as the Wire Arc Additive Manufacturing (WAAM) method should not be undermined. WAAM is a promising method for producing large metal parts, but it is still prone to defects such as porosity that can reduce structural reliability. To ensure these defects are found and measured in a consistent way, inspection methods must be tied directly to code-based acceptance limits. In this work, a three-pass WAAM joint specimen was made in a welded-joint configuration using robotic GMAW-based deposition. This setup provided a stable surface for Phased Array Ultrasonic Testing (PAUT) while still preserving WAAM process conditions. The specimen, which was intentionally seeded with porosity, was divided into five zones and inspected using the 6 dB drop method for defect length and amplitude-based classification, with AWS D1.5 serving as the reference code. The results showed that porosity was not uniform across the bead. Zones 1 and 3 contained the longest clusters (15 mm and 16.5 mm in length) and exceeded AWS length thresholds, while amplitude-based classification suggested they were less critical than other regions. This difference shows the risk of relying on only one criterion. By embedding these results in a DMAIC (Define–Measure–Analyze–Improve–Control) workflow, the inspection outcomes were linked to likely causes such as unstable shielding and cooling effects. Overall, the study demonstrates a code-referenced, dual-criteria approach that can strengthen quality control for WAAM. Full article

Stainless steels have undergone more than a century of continuous development, during which various advanced grades—such as lean duplex, super austenitic, and high-nitrogen stainless steels—have been introduced. Despite remarkable progress, the manufacturing of stainless steel remains a complex process that spans multiple critical stages, including stainless steelmaking, solidification and casting, continuous casting, heat treatment, electroslag and vacuum arc remelting, as well as both hot and cold rolling operations. Ensuring excellent corrosion resistance and mechanical performance of the final products continues to be a central focus of research and production. The current Special Issue (SI) entitled ‘Advanced Stainless Steel—from Making, Shaping, Treating to Products’ has collected eight research papers focusing on various aspects of steel production, e.g., inclusions in steelmaking and continuous casting processes, continuous casting processes and the quality of stainless steel casting, heat treatment, corrosion of steels, and fatigue of steels. This summary aims to contribute to the state-of-the-art of the development of steel production. Full article

In the current economic context, organizations providing automotive repair services face significant challenges in ensuring service quality, operational efficiency, and long-term sustainability. This paper examines the importance of implementing process monitoring systems through the integration of European quality frameworks and Japanese operational principles such as Kaizen, Lean Manufacturing, and Poka-Yoke, to improve the quality of services and increase performance within automotive repair organizations. The research is grounded in Sustainable Development Goals (SDG 9—Industry, Innovation and Infrastructure, and SDG 12—Responsible Consumption and Production), demonstrating how structured quality practices contribute to reducing waste, optimizing processes, and delivering responsible services. The main objectives of the study are to identify the elements that influence the performance of service-specific processes, to improve the quality management practices related to these processes, to eliminate non-conformities, and to enhance profitability and competitive differentiation through service quality assurance. A mixed-methods research design was applied, including direct participatory observation, performance monitoring, and correlational statistical analysis over a six-month period in two Romanian automotive service centers. Key performance indicators (KPIs) such as technician efficiency, rework rate, and order throughput time were collected and analyzed before and after the implementation of selected tools. Findings demonstrate measurable improvements: rework rates decreased from 7.8% to 2.6%, technician efficiency improved from 89% to 105%, and average service completion time was reduced by 1.6 days. Correlation analysis confirmed strong relationships between visual management adoption and rework reduction (r = −0.75), as well as between Lean implementation and technician efficiency (r = +0.89). The study’s novelty lies in its integration of cross-cultural quality management practices into a replicable and sustainable operational model for post-sale service environments. The results validate that implementing monitoring systems, combined with Kaizen, Lean, and Poka-Yoke, supported by visual management and active employee engagement, can lead to superior service quality management, increased customer satisfaction, and long-term organizational success in the automotive repair industry. Full article

Logistics and warehouse operations experience an increasing pressure to adopt sustainable practices. The logistics industry generates substantial material waste, with cardboard being the primary packaging material. Adopting Circular Economy (CE) principles to control this waste is important for enhancing sustainability. However, there is a lack of studies on transforming warehouses into more sustainable operations. This paper studies the ability to transform the linear supply chain of a distribution warehouse into a circular supply chain by applying lean manufacturing principles to eliminate cardboard waste. A structured framework is presented to outline the project’s methodology and illustrate the steps taken to apply the concept of CE. The paper also tests the capability to simulate warehouse operations with engineering software using limited available data to generate various scenarios. This study contributes by showing how discrete-event simulation combined with VSM and 6R principles can provide operational insights under data-constrained conditions. Bridging the gap between theory and practice. Multiple operational scenarios were modelled and run, including peak and off-peak demand periods, as well as a sensitivity analysis for recycling durations. A comparative evaluation is shown to demonstrate the effectiveness of each alternative and determine the most feasible solution. Results indicate that introducing recycling activities created some bottlenecks in the system and reduced its efficiency. Furthermore, suggestions for future improvements are presented, ensuring that on-site actions are grounded in a simulation that reflects reality. Full article

This research investigates the optimization of operational efficiency and cost reduction through the enhancement of material flow management within production line stations. Departing from conventional static analyses, the study employs advanced simulation tools to pinpoint performance bottlenecks and inefficiencies via dynamic modelling techniques. The Ishikawa diagram serves as the primary tool for conducting root-cause analysis. Simultaneously, the 5S methodology is implemented to foster workplace organization, standardization, and hygiene practices. In contrast to traditional optimization frameworks, the proposed strategy integrates real-time performance tracking systems, complemented by adaptive feedback mechanisms. This integration permits ongoing assessment of the production process, facilitating iterative improvement cycles. Empirical data gathered from monitored cycle times, equipment utilization rates, and defect frequencies substantiate the validation of implemented changes. The resulting optimized system significantly minimizes downtime and waste, thereby advancing sustainable and scalable operations. Ultimately, this research demonstrates that the fusion of simulation-based insights with lean management principles leads to considerable improvements in manufacturing productivity and overall product quality. Full article

This study presents a data-driven framework that integrates lean management and digital business process modelling to enhance sustainability in textile manufacturing. Conducted in a company producing industrial safety textiles from Peru, this research applies lean tools within a digital BPM structure supported by real-time data tracking. The integrated approach led to increased production efficiency (from 79% to 86%), reduced setup times, and improved operational agility. The digital infrastructure empowered operators and supported informed decision-making. This work contributes to Industrial Engineering, Business Administration, and MIS by offering a holistic model that bridges lean principles with Industry 4.0 technologies. The findings, though context-specific, provide actionable insights for manufacturers aiming for smart and sustainable operations. Future research should validate the proposed framework across diverse industrial contexts and assess its longitudinal impact on lean performance outcomes. Full article

As a pivotal component of the single-gear reducer, the casing of the miniature car reducer not only safeguards the internal transmission system but also interfaces seamlessly with the external structure. Currently, the structural design of domestic single-stage reducers primarily leans on experience and standardized specifications. To guarantee the reliable and stable operation of the casing, a high safety factor is often incorporated, which inevitably results in increased weight and necessitates secure bolting connections. This study presents an innovative scheme to design the flange with the box and realize the lightweight nature of the box by finite element analysis to reduce the manufacturing cost. Based on the working state of maximum torque and maximum speed, this study obtains the stress distribution of each bearing seat under different working conditions and carries out static and dynamic analysis combined with other coupling constraints. The analysis results show that the structure has high stiffness and strength, which is suitable for lightweight design, and that the first ten spontaneous vibration frequencies are far away from the excitation frequency of the inner and outer boundary, avoiding the resonance phenomenon. Moreover, this study proposes a new structure design method, which effectively improves the stiffness of the structure. Through the calculation of volume ratio before and after three optimizations, the optimal volume ratio of 30% is selected, unnecessary materials around the bearing seat are removed, and the layout of ribs is determined. After structural optimization, the weight of the shell is reduced by 10.2%, and both the static and dynamic characteristics meet the design requirements. Full article