The manufacturing industry is undergoing a transition from conventional manufacturing processes to process automation, with particular emphasis on factors contributing to its sustainability. Understanding the complexities of Industry 4.0 is fundamental for devising strategies that effectively address challenges and capitalize on the opportunities present in a competitive business environment. This study aims to identify and analyze the challenges influencing the implementation of Industry 4.0 in SMEs. A total of 10 key challenges were identified through extensive literature review and expert inputs. The challenges identified during implementation of Industry 4.0 in SMEs are: Device compatibility issues, Data security issues, Process-specific technology selection challenges, Data management challenges, Dynamic necessities in production, Unstable connectivity, Random practical issues, Data integration with product life cycle management, Scalability challenges, and Lack of interoperability with existing systems. The identified key challenges in Indian SMEs are analyzed to determine the significance of Industry 4.0 implementation and to explore the relationships among them. This knowledge will be instrumental in assisting practitioners in navigating the complexities of Industry 4.0 implementation in SMEs using the Total Interpretive Structural Modeling (TISM) and Matrice d’Impacts Croisées Multiplication Appliquée à un Classement (MICMAC) methods. The study results revealed that two challenges — namely, device compatibility and interoperability with existing systems — are highly impactful in Indian SMEs. This model was further validated through structural equation modeling, satisfying the criteria of goodness-of-fit indices. This study also offers insights into the significant benefits of Industry 4.0 in addressing contemporary challenges in SMEs and to provide a comprehensive analysis of the challenges that drive its successful implementation within the manufacturing sector, particularly with an emphasis on sustainability including technological, organizational, regulatory, economic, environmental, and social dimensions.
Bioinspired design, inspired by nature’s time-tested principles, has become a transformative approach in engineering and allows the development of innovative solutions by mimicking the efficient structures and mechanisms found in biological systems. Integrating with additive manufacturing (AM), bioinspired design facilitates the creation of complex, multi-material, and multifunctional structures that offer superior performance compared to traditional manufacturing methods. This review delves into the synergies between bioinspired design and AM, emphasizing how natural concepts, such as Bouligand-like structures, nacre-inspired composites, hierarchical architectures etc., are utilized to enhance material properties like toughness, fracture resistance, and energy dissipation which is crucial to the field of 3D printing. These advancements have led to breakthroughs in aerospace, biomedical engineering, and environmental technologies, including lightweight and impact-resistant components, tissue scaffolds for regenerative medicine, self-cleaning surfaces, and anti-fouling coatings. The paper also identifies key material considerations for successful bioinspired AM, focusing on polymers, composites, hybrid materials, and nanomaterials essential for realizing these innovations. Despite the immense potential, challenges remain in replicating the multifunctionality and scalability of natural systems, especially in terms of digital modeling, multi-material printing techniques, and cost-efficiency. Looking forward, the combination of bioinspired design with advancements in generative design, artificial intelligence, and novel materials will pave the way for future innovations, offering sustainable and high-performance solutions across diverse industrial applications. This review ultimately demonstrates how the fusion of nature-inspired design principles with AM technologies can unlock innovative solutions to modern engineering challenges.
A thin, broadband frequency-selective surface (FSS)-based multilayer microwave absorber is proposed for stealth applications. The design integrates two material layers composed of e-waste-derived copper composite and ball-milled cobalt, strategically incorporated above an FSS-patterned FR-4 substrate. The FSS geometries, featuring simple square loops and cross dipoles, are optimized to achieve enhanced absorption performance while maintaining structural simplicity. The material layers are developed using ball milling (5 h) for cobalt and a blending method for the e-waste/copper composite, and their experimentally measured complex permittivity and permeability are used as inputs for full-wave electromagnetic simulations. The simulated reflection loss results demonstrate a broadband operating frequency range of 7.46 to 18 GHz with reflectivity below −10 dB, achieved with a compact total thickness of 2.3 mm. This design establishes a cost-effective and high-performance solution for advanced microwave absorption applications.
Aiming at the problems of low harvesting efficiency and high cost for leafy vegetables, a fully electric vegetable harvester was designed, which takes an electric tractor as the power source and is mainly composed of a cutting device, a reel device, a conveying device, a traveling device, a suspension device, a frame and other components, enabling the harvesting of Brassica rapa var. chinensis (seedling stage, commercially known as “Jimaocai” in Chinese) and other similar crops. On the basis of elaborating the overall structure and working principle, SolidWorks was applied to simplify the models of Brassica rapa var. chinensis and the cutting blade. The Recurdyn software was used to conduct kinematic simulation analysis on the cutting device, clarifying the movement process of the cutting blade. Meanwhile, the EDEM software was adopted to establish a discrete element simulation model for the kinematic simulation and analysis of the process of cutting vegetable stems by the cutting blade. The simulation results showed that the optimal harvesting effect was achieved when the traveling speed of the tractor was 0.691 m/s and the reciprocating cutting frequency of the cutting blade was 25.54 Hz. Considering the field operation efficiency and operational convenience, a traveling speed of 0.7 m/s was adopted in the field verification test with the reciprocating cutting frequency of the cutting blade maintained at 25 Hz. Field verification tests demonstrated that the average harvesting loss rate and the average missing cutting rate of Brassica rapa var. chinensis were 6.7% and 4.3% respectively, and the test results were basically consistent with the simulation results, meeting the design requirements of the vegetable harvester. This equipment is equipped with an ordered harvesting function, which can reduce the subsequent sorting cost, and the research results can provide a theoretical basis for the subsequent test and optimization of fully electric vegetable harvesters.
Drying is widely used in the food and agriculture industries to preserve food products and crops. Conventional dryers largely use fossil fuels, and their use is discouraged due to environmental pollution, greenhouse gas emissions, and increasing fuel prices. Therefore, solar energy is considered as sustainable and economically viable alternative because it is free, clean, and renewable. This review focuses on the recent advancements of solar drying technology and various methodologies of solar drying processes. The study investigates many aspects, such as design considerations, installation and operation, integration of heat storage materials, nano materials performance evaluation, economic feasibility, and comparative analysis. Both experimental and simulation studies of solar dryers, with and without phase change materials (PCMs), have been reviewed to understand their impact on drying performance. The study further investigates the action of various air heating systems in enhancing drying rate. Hybrid solar dryers have the ability to provide stable and continuous drying conditions, thereby improving drying performance and food product quality. Additionally, the integration of heat storage devices and smart monitoring systems for real-time analysis have been discussed in this study. Overall, solar dryers are considered as a strong potential for sustainable applications in food processing, agro-based industries, and domestic sectors.
This paper focuses on exploring the use cases and practical applicability of deep learning in Industry 4.0 by studying on a water pump time series dataset with 5 models namely LSTM, CNN-LSTM, GAF-CNN, BiLSTM and Time-Series Transformer. The unplanned downtime due to sudden equipment failure costs the industry huge losses every year. The proposed methodology based on deep learning architectures uses sensor readings and leads to meaningful predictions for cost-cutting and time saving. The study evaluates and compares these models in terms of fine-grained architecture-level components and prediction accuracy. The results demonstrated that the transformer based time series hybrid model is more accurate in prediction with balanced performance and strong interpretability than other models.
Sustainability has become a vital objective in modern manufacturing, encompassing a careful balance between environmental conservation, economic efficiency, and the well-being of workers. While many studies in machining focus on reducing energy consumption or production cost reduction, few integrate the social dimension, particularly the reduction of harmful noise emissions that directly affect human health. A framework for optimization is presented in this paper for all three pillars of sustainability through the proper optimal selection of machining parameters. Experimental studies were conducted on milling, both surfacing and contouring, by minimizing energy consumption and noise levels by adjusting three key parameters: ‘depth of cut’, ‘cutting speed’, and ‘feed rate’. Experimental investigations were conducted for this purpose according to a structured Design of Experiments (DOE) supported by Analysis of Variance (ANOVA). Predictive models were developed, and a multi-objective optimization was performed using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the best compromise solution was identified using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). These results demonstrate that significant reductions in energy use and noise emissions can be achieved. For AU4G, the measured responses reached up to 90 dBA and 303 W, yet the optimization reduced them to 77.57 dBA and 218.03 W. For IRON-500, the maximum recorded values were 100 dBA and 322 W for both operations, whereas the optimized settings lowered these responses to 84.8 dBA with 250.88 W in contouring, and 84.51 dBA with 276.62 W in surfacing. This approach provides a practical pathway toward truly sustainable manufacturing, where environmental, economic and human-centered objectives are achieved in an integrated way.
Photovoltaic (PV) panels are vital renewable energy sources that convert solar radiation into electricity. However, dust and snow accumulation significantly reduce their efficiency, particularly in arid or cold regions. Existing cleaning systems—whether manual, semi-automated, or robotic—often consume excessive water or fail to adapt to changing environmental conditions. Moreover, current reinforcement learning (RL) approaches such as Q-learning and Deep Q-Network (DQN) lack the flexibility required for real-time optimization. This study proposes a Deep Deterministic Policy Gradient (DDPG)-based intelligent wiper system capable of dynamically adjusting air-blowing and brushing speeds according to debris levels, completely eliminating water use. Simulations were conducted under diverse environmental conditions, including variable dust accumulation rates and solar irradiance levels, to assess system performance. Results show that the DDPG-based model achieved a 35% improvement in panel efficiency, 97% dust cleaning effectiveness, and the lowest mean squared error (0.05) among all tested methods. Operating within a flexible wiper speed range of 1.0–2.5 m/s, it also yielded the greatest reduction in cleaning frequency. The proposed system demonstrates superior adaptability, sustainability, and efficiency, providing a practical, water-free intelligent cleaning solution for maximizing the long-term performance of photovoltaic panels.
Structural topology optimization provides an effective computational approach for determining optimal material distributions that achieve lightweight and high-performance structures. In this study, the Solid Isotropic Material with Penalization (SIMP) method is employed to investigate and compare single-material and multi-material topology optimization for interconnected automotive structural components. Three material configurations—steel, aluminum, and a steel–aluminum hybrid—are evaluated through numerical simulations. A simplified truck chassis model was used to demonstrate the proposed method. The results show that the proposed multi-material design significantly improves weight reduction and material cost efficiency. The SIMP formulation drives all designs toward a comparable level of material efficiency, with mass reductions exceeding 95% in each case of different materials of the truck chassis model. These findings demonstrate the effectiveness of multi-material topology optimization for developing lightweight automotive structures and provide design insights for next-generation vehicle body systems.
Boron carbide (B 4 C) is recognized for its low density, and excellent thermal stability, making it a strong candidate for ceramic-based microwave-absorbing materials. The present study focuses on the influence of Al-dispersion on electromagnetic attenuation behaviour of B 4 C. The Al/B 4 C composites were synthesized via high-energy mechanical ball milling with Al dispersion from 2 to 10 wt.%. Phase constitution and magnetic response were examined using X-ray diffraction and vibrating sample magnetometry, confirming phase stability of B 4 C and the non-magnetic nature of the composites. Microstructural analysis using Field-emission scanning electron microscopy coupled with energy-dispersive spectroscopy mapping demonstrated homogeneous Al dispersion throughout the B 4 C matrix promoting the formation of abundant heterogeneous interfaces. Dielectric properties and reflection loss were evaluated using a vector network analyzer over the 2–18 GHz range. Among the single-layer absorbers, sample BA1 (2 wt.% Al) exhibited the minimum RL of −42.75 dB at a matching thickness of 1.1 mm, with an effective absorption bandwidth of 2.19 GHz (RL < −10 dB). Sample BA4 (8 wt.% Al) showed the maximum effective absorption bandwidth (RL < −10 dB) of 2.63 GHz, indicating that increasing Al content improves bandwidth via enhanced conductive loss and interfacial polarization. To further broaden absorption performance, a double-layer absorber was designed, and layer sequences and thicknesses were optimized using a genetic algorithm (GA). The optimal configuration, BA3 as the front layer and BA1 as the backing layer, each with a thickness of 1.0 mm, achieved a substantially enhanced bandwidth of 5.7 GHz while maintaining >99% absorption efficiency across the operating band. The novelty of this work lies in demonstrating that controlled Al dispersion in B 4 C, combined with GA-assisted double-layer design, enables thin and broadband microwave absorption without magnetic fillers.
Engineer-to-order (ETO) and configure-to-order (CTO) companies reuse modules across product variants while continuously maturing them through concurrent engineering processes. In this setting, readiness for module release depends not only on the module’s own state but also on the readiness of every revision it requires. Because modules are shared across variants, a readiness contradiction in one dependency can block release across multiple products. Previous research has addressed variant governance, change propagation analysis, and effectivity-enabled product structures, but has paid little attention to engineer-facing, explainable checks that diagnose present-state readiness contradictions across the product structure. To address this gap, this paper proposes a Revision Coherence Checking method that (i) maps enterprise evidence — lifecycle state, change-task completion, approvals, and effectivity rules — into canonical readiness states via an auditable interpretation layer, and (ii) checks coherence constraints over a product-structure dependency graph to identify violations where a module revision is treated as more ready than a required dependency. The method is operationalized through an interactive graph-based prototype that localizes blockers, exposes propagation paths, and supports prioritization using reuse impact. A case study at a European custom laser manufacturer, covering 24 product lines and 183,265 dependency relationships, identified 532 direct readiness contradictions not surfaced by the company’s existing PLM system. The results demonstrate that the method can detect and characterize coherence violations, enabling earlier detection of release blockers, reduced rework from late discovery of readiness mismatches, and more targeted engineering coordination. Overall, the study frames readiness coherence as a computable, structure-level property of revision-aware product structures, rather than an attribute of individual revisions, thereby providing a basis for diagnostic readiness reasoning in reuse-intensive, PLM-enabled engineering environments.
The paper aims to analyze and optimize the FSWing process of plates in a butt configuration. The ultimate objective is to desensitize the process to both endogenous and exogenous noises, which are lacing the manufacturing process.. To this end, the Robust Design for Products and Processes using the stochastic frontier (RDPP-SF) and the Taguchi-Grey methods are employed, and settings of the process parameters, which yield the best mechanical performances, are sought. The process parameters are the pin rotation speed (x 1 ), feed rate (x 2 ), and plunged surface (x 3 ), whereas the process responses are the longitudinal stress (σ L ), transverse stress (σ T ), longitudinal strain (ε L ), and transverse strain (ε T ) that occur in the FSWed seams. Three replications of an FCCD design are conducted and then statistically analyzed. For the RDPP-SF method, the optimal mechanical properties are σ L = 22.197 MPa, σ T = 22.607 MPa, ε L = 18.33 %, and ε T = 15.83 % and they occur when setting the rotation speed (x 1 ) at 1700 rpm, the feed rate (x 2 ) at 29 mm/min, and the plunged surface (x 3 ) at 478 mm 2 . The optimal values with regard to the Taguchi-Grey method are achieved at, σ L = 23.733 MPa, σ T = 23.886 MPa, ε L = 17.76 %, and ε T = 16.91 % while setting the rotation speed (x 1 ) at 1280 rpm, the feed rate (x 2 ) at 29 mm/min, and the plunged surface (x 3 ) at 478 mm 2 . The discrepancy between the RDPP-SF and the Taguchi-Grey method is partly due to the variance decomposition models, which are employed.
The high level of computerisation of modern production systems has led to the generation of large volumes of machining data, offering significant potential for using data-driven decision making (DDDM) for examining the fault analysis and incorporating efficient preventive maintenance measures. This research proposes a DDDM framework to incorporate data driven preventive maintenance and fault analysis in modern production systems. This framework uses IoT sensors, cloud computing, and machine learning for real-time fault analysis and preventive maintenance. This approach incorporates the analysis of failures using past machining data and suggests maintenance strategies based on fault analysis. The framework exhibits a continuous model improvement nature based on the system data by including continuous monitoring and model refinement loops. The failure mode effect analysis does systematic fault identification, predictive maintenance planning, and continuous improvement through feedback loops. The applicability of the framework is demonstrated through an experimental study on CNC turning of D2 alloy steel performed on a FANUC CNC lathe. The systematic fault analysis, visualisation of tool wear with machining parameters, capacity utilisation analysis, tool life prediction by multivariate linear regression machine learning model, failure analysis and FMEA were performed by the deep analysis of the recorded experimental data. A random forest model demonstrated an accuracy of 84.37% in tool failure prediction, effectively correlating predicted and actual tool life. Thus, by proposing framework and demonstrating its applicability in an industrial setup, this research establish theoretical as well as practical foundation for applying data driven preventive maintenance and fault analysis in modern production systems to enhance production quality and efficiency.
The use of recycled aggregates (RA) derived from construction and demolition waste (CDW) offers a viable alternative to natural aggregates (NA), with potential to reduce reliance on virgin resources. However, the effective utilization of RA requires integrated process design to mitigate the adverse effects of adhered mortar and interfacial heterogeneity. This study proposes an integrated multi-stage treatment framework for RA, combining mild chemical pre-soaking, controlled mechanical abrasion, and pressurized carbonation, with emphasis on process sequencing and threshold-based optimization. Raw RA was soaked in a mild acetic acid solution for 24 h, mechanically processed using a Los Angeles abrasion test machine (LAATM) at rotation levels ranging from 0 to 600, and subsequently subjected to 2 h of pressurized carbonation. The resulting combinedly treated recycled aggregates (CTRAs) were incorporated into M40-grade concrete at replacement levels of 0%, 20%, 40%, 60%, 80%, and 100%. Mechanical performance was evaluated through compressive and splitting tensile strength tests. The results indicate the existence of a critical mechanical processing window at approximately 500 LAATM rotations, beyond which further processing provides diminishing benefits. Concrete incorporating 60% CTRA processed at 500 rotations exhibited a reduction of 17.34% in compressive strength and approximately 16% in splitting tensile strength relative to natural aggregate concrete, while maintaining stable performance trends within the investigated scope. These findings demonstrate that coordinated treatment sequencing and processing intensity, rather than isolated treatment steps, govern the mechanical response of recycled aggregate concrete, supporting a concurrent engineering approach to recycled aggregate system design.
Manufacturing organisations face pressure to develop effective engineering solutions while maintaining their competitiveness in rapidly evolving markets. This challenge necessitates product development approaches that balance rigorous knowledge-driven methodswith responsive adaptation to changing requirements. This research presents an empirical application of the Lean and Agile Model for Product Development (LAMPD) in a manufacturing environment, addressing critical gaps in integrated methodologies for hardware contexts. LAMPD was implemented through a 4-month case study at Atlas Copco Henrob, a UK manufacturer of self-piercing riveting systems, focusing on enhancing a rivet feeder system used in automotive manufacturing that had not previously undergone dedicated design optimisation. The research aimed to document LAMPD implementation processes, determine its impact on design efficiency, identify process benefits and challenges, and explore implications for manufacturing product development practices. The LAMPD framework systematically integrated Set-Based Concurrent Engineering principles with Agile methodologies including Scrum ceremonies and Kanban visualisation techniques across three development phases: Project Definition, Project Feasibility, and Project Realisation. The integrated approach facilitated evidence-based decision-making, which resulted in a 53% component reduction, eliminated two sensors from the original design, and improved system reliability by reducing false readings from 12% to near zero principally through the rigorous application of SBCE methodology combined with continuous integration cycles. Challenges identified included harmonising sprint cadences with traditional phase boundaries and balancing multiple methodological frameworks without compromising their individual strengths. Despite these implementation complexities, the research demonstrates that combining Lean knowledge management principles with Agile responsiveness creates substantial value in manufacturing product development. The LAMPD model provides organisations with practical mechanisms to enhance both design efficiency and process adaptability, contributing to theory and practice by showing how integrated approaches can significantly improve product development outcomes in hardware manufacturing contexts.
This paper explores the development of a computer framework for electric harness routing considering design requirements and ergonomic assembly aspects simultaneously. It expands the current knowledge in this field by introducing methods for multidisciplinary analysis, allowing for the weighting of different criteria to create various solutions evaluated against multiple factors such as harness length, number of clipping points and ergonomic factors etc., utilizing Bayesian optimization. Two case studies are presented to demonstrate and evaluate the framework. The first case focuses on the evaluation of the multi-objective optimization, showing that the framework successfully generates several alternative design solutions and a wide variation of Pareto optimal solutions. In the second case the framework is brought out to the industry where four engineers use the framework for a real case. The work process and design results are compared with a test group which solves the same design problem but uses standard design tools. The evaluation highlights the framework’s potential to enhance the harness design process, with an estimated potential to reduce engineering time by 60%. The full code is available at https://github.com/wiberganton/autopack2.1 .
Energy consumption in additive manufacturing processes is critical in improving environmental quality and attaining a sustainable equilibrium between energy use and environmental degradation. The study employs two widely accepted non-parametric machine learning (ML) methods, i.e., Artificial Neural Network (ANN) and Gaussian Process Regression (GPR), to predict the energy consumption in the Fused Deposition Modeling manufacturing process (AM-FDM). The ANN and GPR models are compared based on a numerical general factorial design accounting for six graded-level parameters, i.e., A: learning rate, B: momentum. C: number of hidden nodes, D: proportions of the training data, E: proportion of test data, and F: the activation function. The sensitivity of the ANN and GPR models for the training subsets is further examined using different percentages of the original dataset—25%, 50%, and 75%—that are sampled using a modified hypercube sampling technique. Under all circumstances, the GPR model has performed better than the ANN.
Bioinspired microrobots, a rapidly advancing field at the intersection of biology, microengineering, and medicine, has gained momentum due to recent progress in microfabrication techniques, smart materials, and precision control systems. Microrobots, miniaturized machines typically less than a millimeter in size, are engineered to emulate natural organisms such as bacteria, spermatozoa, and insects, enabling them to navigate complex, viscous biological environments. These devices offer transformative potential for targeted drug delivery, in vivo diagnostics, microsurgery, and tissue manipulation, especially in regions inaccessible to conventional medical tools. Despite their promise, microrobots face significant challenges related to propulsion efficiency, biocompatibility, actuation, and real-time imaging within the human body. This review contributes a comprehensive synthesis of current research in the field, organizing into four key sections: (1) bioinspired locomotion strategies, (2) advanced fabrication techniques and material innovations, (3) external actuation and control methodologies, and (4) current and emerging clinical applications. In addition to reviewing recent breakthroughs, this work critically discusses the limitations of current designs and outlines practical considerations for clinical translation, and scalability. This article emphasizes cross-disciplinary integration and application-specific design, positioning it as a valuable resource for guiding future innovations in minimally invasive microrobotic systems for medicine.
The existence of notch or crack leads to impact the material behavior of any material, owed to the formation of discontinuity on the surface. In this study, the creep performance of AlSiMgCuFe alloy has been examined using molecular dynamics package under various creep conditions along with the existence of notch (crack) and un-notch (without crack) conditions and found that the notch existence has invoked larger impact on the creep performance of aluminum-silicon alloys tailed by the temperature and pressure (applied), due to the incidence of severe diffusion performance amidst the atoms. Further, it is quantified that the surge in the displacement and shear strain performance as a outcome of creep distortion time under various creep circumstances shows a vigorous part on the creep behavior and the pair correlation function and mean square displacement behavior has confirmed the similar performance which leads to modify its creep life.
Chatter remains a critical limitation in milling operations, impairing surface integrity, reducing tool life, and limiting productivity. Accurate real-time prediction of chatter is essential for achieving process stability and enhancing machining efficiency. This paper provides a comparative evaluation of Support Vector Machines (SVM) and Artificial Neural Networks (ANN) combined with Variational Mode Decomposition (VMD) for the accurate identification of chatter in real-time vibration data. The milling vibration signals are decomposed into an Intrinsic Mode Functions (IMFs) with VMD to obtain chatter sensitive features. Axial depth of cut, table feed and Spindle speed data combined with these characteristics are used as input to both ANN and SVM models. The ANN model uses Tangent Sigmoid (TANSIG) activation function and six training algorithms, out of which Levenberg-Marquardt (LM) is found to produce the best results. Experimental verification proved that the prediction accuracy of 91.44% was outstripped (over 87.21%) in terms of the SVM model by the ANN model. The robustness of this framework in detecting stable, transitional, and unstable cutting zones has also been demonstrated through Stability Lobe Diagrams (SLDs) analysis. The proposed VMD-ANN approach offers an accurate and efficient solution to chatter prediction and parameter optimisation in real-time, which further contributes to higher material removal rates, better surface quality, and a longer tool life for smart manufacturing applications.
