Tribo-mechanical experiments were performed on Glass Fiber Reinforced Polymer (GRFP) composites against different engineering materials, and the tribological behavior of these materials under dry conditions was investigated. The novelty of this study consists of the investigation of the tribomechanical properties of a customized GFRP/epoxy composite, different from those identified in the literature. The investigated material in the work is composed of 270 g/m² fiberglass twill fabric/epoxy matrix. It was manufactured by the vacuum bag method and autoclave curing procedure. The goal was to define the tribo-mechanical characteristics of a 68.5% weight fraction ratio (wf) of GFRP composites in relation to the different categories of plastic materials, alloyed steel, and technical ceramics. The properties of the material, including ultimate tensile strength, Young’s modulus of elasticity, elastic strain, and impact strength of the GFPR, were determined through standard tests. The friction coefficients were obtained using a modified pin-on-disc tribometer using sliding speeds ranging from 0.1 to 0.36 m s⁻¹, load 20 N, and different counter face balls from Polytetrafluoroethylene (PTFE), Polyamide (Torlon), 52,100 Chrome Alloy Steel, 440 Stainless Steel, and Ceramic Al₂O₃, with 12.7 mm in diameter, in dry conditions. These are commonly used as ball and roller bearings in industry and for a variety of automotive applications. To evaluate the wear mechanisms, the worm surfaces were examined and investigated by a Nano Focus—Optical 3D Microscopy, which uses cutting-edge μsurf technology to provide highly accurate 3D measurements of surfaces. The obtained results constitute an important database for the tribo-mechanical behavior of this engineering GFRP composite material.

Human Factors and Ergonomics (HF/E) play an essential role in the development of sustainable manufacturing systems. By prioritizing worker wellbeing through the mitigation of occupational hazards and the enhancement of workplace health, HF/E contributes significantly to improved system performance. In accordance with the principles of Industry 5.0 and Society 5.0, which emphasize human-centered design and wellbeing, organizations that effectively integrate HF/E principles can achieve a competitive advantage on the market. Based on a globally recognized ranking system utilized by investors in making informed decisions, the study focuses on manufacturing companies ranked by their occupational health and safety (OHS) scores, a key criterion for assessing the social dimension of company performance. This research aims to identify and analyze top-ranked companies that explicitly highlight HF/E-related benefits within their public documents and sustainability reports. The paper investigates aspects related to the integration of AI and digital technologies to enhance safety and health in manufacturing systems, with a specific focus on human presence detection in hazardous zones, improvements in machines and equipment design, occupational risk assessments, and initiatives for enhancing worker wellbeing. The findings are expected to provide compelling evidence for companies to prioritize HF/E consideration during the design and redesign phases of sustainable manufacturing systems. The paper provides significant value to non-indexed companies by offering a dual approach for improving OHS performance, based on an empirical evaluation assessment method and practical strategies for effective OHS implementation in different manufacturing industries and countries.

This paper presents a solution aimed at enhancing the accuracy of abrasive waterjet cutting (AWJC) for the processing of carbon-fiber-reinforced polymers (CFRP). Processing CFRP with high accuracy and good surface quality in a short processing time is a difficult task. One crucial problem is the occurrence of shape errors, overcuts, at the cut-in and cut-out point during the cutting process. Shape errors have the potential to create mechanical stress concentrators, which can result in structural failures and compromise the integrity and reliability of components. The primary objective of this study was to gain a comprehensive understanding of the formation mechanism underlying the shape error. The observed shape error is closely associated with both the lead-in/lead-out strategies employed and the process parameters selected. The experimental investigation focused on two commonly used strategies for CFRP cutting: lead-in/lead-out in arc and lead-in/lead-out in line. In order to minimize shape errors, this study proposed a correction method that offers a set of recommendations for selecting the appropriate lead-in/out strategy and a suitable combination of process parameters. Additionally, a mathematical model has been developed to determine the depth of the shape error. The conclusions drawn from this study have been successfully validated through industrial applications.

Small and medium-sized enterprises (SMEs) are expected to contribute to sustainable manufacturing, yet they often lack the resources and capabilities needed to adopt advanced automation in a structured and scalable manner. While lean robotics have been widely studied, there is still limited empirical evidence on how their integration can be systematically designed to improve sustainability-oriented performance in SME contexts. This paper examines how a scalable lean robotics system can be conceived and implemented to enhance productivity and resource efficiency in an SME packaging process. We develop a lean robotics design approach that jointly considers lean principles, collaborative industrial robotics, and Industrial Internet of Things (IIoT) monitoring. The approach is applied in a real-world case study of a “Fold Station” robotic cell, where stone paper sheets are destacked, glued, and formed into cylindrical plant protectors. Key performance indicators related to cycle time, material utilization, process stability, and manual workload are measured before and after implementation. The results show a three- to four-fold reduction in preparation time per unit, more efficient use of stone paper and adhesive, and a decrease in repetitive manual handling, thereby contributing to both economic and environmental sustainability. TRIZ (Teoriya Resheniya Izobretatelskikh Zadach, Theory of Inventive Problem Solving) is used to structure the resolution of design contradictions that arise when embedding lean principles into the robotic system and to support its scalable adaptation to different production scenarios. This study advances the understanding of lean robotics for sustainable SME production and derives practical guidelines for designing scalable, resource-efficient robotic cells.

Manual assembly of multi-pin cable harnesses remains vulnerable to miswiring when conductors are visually indistinguishable. This paper presents an industrial case study of a quick-connect harness composed of two connectors (receptacle-type and pin-type) linked by 16 black conductors (2.5 mm²; 200 mm length), where the dominant failure mode is a two-wire swap that breaks correct pin-to-pin mapping and may cause downstream equipment damage. In the baseline state, end-of-line verification relied on visual inspection only (1 min/unit), resulting in an internal nonconformity rate of 4% (repairable). To achieve the operational goal of zero defects (zero escapes), we propose and integrate an electronic pin-to-pin continuity and mapping fixture as a deterministic End-of-Line (EOL) quality gate implementing poka-yoke logic (“no PASS—no shipment”) and enabling structured traceability records. Using a before–after workload model that includes mandatory retest after rework, the fixture reduces test time to 0.33 min/unit. For a monthly volume of 1500 units, total quality workload (test + rework + retest) decreases from 31 h/month to 13.58 h/month, releasing 17.42 h/month. Global quality productivity increases from 48.39 units/h to 110.46 units/h (+128%). The proposed architecture couples deterministic electrical verification with data logging aligned to digital thread and data-driven quality management concepts to sustain continuous improvement and prevent customer escapes.

Background: Engineer-to-order (ETO) manufacturing faces material shortages arising from changing requirements and uncertain supply. This study develops Artificial Intelligence–Supply Chain Decision Intelligence (AI-SCDI), linking shortage prediction to feasible interventions under cost and capacity constraints. Methods: Six specialists assessed framework content and parameter plausibility. A locked experiment used 12 synthetic cohorts and 6000 paired Monte Carlo replications per policy, supplemented by model-recovery diagnostics, matched policy ablations, calibration assessment, and joint sensitivity analysis. Results: Histogram gradient boosting achieved a mean precision–recall area under the curve of 0.7587, compared with 0.5399 for logistic regression and 0.7415 for random forest. Under baseline assumptions, AI-SCDI reduced disruption time by 30.0% and synthetic operating cost by 19.1% relative to no proactive intervention. At baseline capacity, estimated-net-benefit selection and abstention contributed more to cost reduction than impact weighting alone. Learner rankings varied across generating mechanisms, horizon-specific calibration showed risk overestimation, and mean net benefit was negative in 19 of 64 informed-parameter scenarios. Conclusions: AI-SCDI provides a reproducible, capacity-constrained decision heuristic. Its benefits remain conditional on synthetic assumptions; external validation and empirical cost calibration are required before industrial effectiveness can be claimed.