搜索结果：The International journal, advanced manufacturing technology

The paper is dedicated to the evaluation of the accuracy of rotary parts produced with the use of advanced manufacturing technology. The authors investigated the impact of the layer thickness of the applied material and the orientation of the model when printing using the PolyJet method™ on the geometrical quality of manufactured products. To analyze the influence of the assumed factors on the geometrical quality of the holes, a novel evaluation method has been developed. The proposed method takes into account parameters such as roundness deviation, profile irregularity coefficient, dominant harmonic component of the roundness profile, cylindricity deviation, diameter error, and surface topography parameters. The study presented in this paper had two main objectives. The former was to analyze the impact of the layer thickness of the applied material and the orientation of the model when printing using the PolyJet method™ on the geometrical quality of rotary parts. The latter objective was to test a novel, multi-parametric method of evaluation of the accuracy of produced parts in practice. The results obtained by the authors prove that the new evaluation method can be useful in the assessment of the accuracy of manufactured products.

The aim of this paper is to study the mechanical behavior of corrugated board boxes, focusing attention on the strength that the boxes are able to offer in compression under stacking conditions. A preliminary design of the corrugated cardboard structures starting from the definition of each individual layer, namely the outer liners and the innermost flute, was carried out. For this purpose, three distinct types of corrugated board structures that include flutes with different characteristics, namely the high wave (C), the medium wave (B), and even the micro-wave (E), were comparatively evaluated. More specifically, the comparison is able to show the potential of the micro-wave which would eventually allow a significant saving of cellulose in the fabrication process of the boxes, thus reducing the manufacturing costs and causing a lower environmental footprint. First, experimental tests were carried out to determine the mechanical properties of the different layers of the corrugated board structures. Tensile tests were performed on samples extracted from the paper reels used as base material for the manufacturing of the liners and flutes. Instead, the edge crush test (ECT) and box compression test (BCT) were directly performed on the corrugated cardboard structures. Secondly, a parametric finite element (FE) model to allow, on a comparative basis, the study of the mechanical response of the three different types of corrugated cardboard structures was developed. Lastly, a comparison between the available experimental results and the outputs of the FE model was carried out, with the same model being also adapted to evaluate additional structures where the E micro-wave was usefully combined with the B or C wave in a double-wave configuration.

One of the challenges in the transfer of heat during the mechanical machining process is the coolant substance used in the internal cooling method which is generally liquid water or a water-based coolant. This limits the heat transfer capacity insofar as the thermal conductivity of liquid water is concerned. The other difficulty is the requirement for an external mechanical system to pump the coolant around the internal channel, providing efficient transfer of the accumulated thermal energy. This study proposes a novel method to address this issue by using liquid gallium which provides the means to transfer the excess heat generated during the cutting process by integrating the design into an aluminium oxide insert. Combining this with a magnetohydrodynamic drive, the coolant system operates without the need for mechanical input. Liquid gallium is nontoxic and has a much higher thermal conductivity over liquid water. Investigations of the novel cooling system is performance compared against liquid water through numerical modelling, followed by an experimental machining test to ascertain the difference in heat transfer effectiveness, tool wear rates and workpiece surface finish when compared to dry machining and external cooling conditions on stainless steel 316L. Without cooling, experimental machining tests employing a cutting speed of Vc = 250 m min-1 resulted in a corner wear VBc rate of 75 μm, and with the magnetohydrodynamic-based coolant on, produced a VBc rate of 48 μm, indicating a difference of 36% in relative tool wear under the same cutting conditions. Increasing the cutting speed Vc to 900 m min-1, produced a corner wear VBc rate of 357 μm without the active coolant and a VBc rate of 246 μm with the magnetohydrodynamic-based coolant on, representing a decrease of 31% in relative tool wear. Further tests comparing external liquid water cooling against the liquid gallium coolant showed at Vc = 250 m min-1, a difference of 29% in relative tool wear rate reduction was obtained with the internal liquid gallium coolant. Increasing the cutting speed to Vc = 900 m min-1, the data indicated a difference of 16% relative tool wear reduction with the internal liquid gallium. The results support the feasibility of using liquid gallium as an internal coolant in cutting inserts to effectively remove thermal energy.

The assessment of energy consumed in manufacturing operations and the enhancement of their sustainability plays a fundamental role in the present research contest. Electron beam melting (EBM) is an additive manufacturing technique that allows the fabrication of titanium parts with high productivity and a low buy-to-fly ratio; on the other hand, the roughness of the parts is not adequate for high-performance applications, so a finishing step is always required. Aiming to reduce the energy used to produce a part, all the required manufacturing steps should by carefully treated in an integrated framework. The aim of this paper is to study the energy required to produce a Ti6Al4V part printed through EBM and the machined to achieve the desired surface finishing. Cylindrical specimens have been printed through an Arcam machine by using the processing conditions suggested by the manufacturer; then, the specimens have been turned under different processing conditions. The energy required in all the phases has been recorded and then carefully analyzed to point out the processing conditions which allows a more efficient use of resources. The results showed that the printing phase is by far the most energy demanding so should be carefully treated to reduce the printing time even if a greater roughness is achieved; the analysis of the machining stage suggested that both depth of cut and spindle speed must be kept the higher as possible to reduce the energy consumption of this stage.

The UK is the twelfth-largest manufacturing nation globally, yet its adoption of digital manufacturing technologies (DMTs) lags behind other European countries. In an era where industrial automation and digital transformation are essential for maintaining competitiveness, understanding the human factors influencing the acceptance and implementation of these technologies is critical. This study examines the perceptions of 313 UK manufacturing employees regarding the usefulness, ease of use, and workplace impact of DMTs. Findings indicate that while employees recognise the potential benefits of DMTs such as increased productivity, improved product quality, and enhanced competitiveness, concerns remain regarding ease of use, workforce upskilling, and physical interaction with new technologies. Notably, employees with lower educational qualifications expressed greater scepticism about the applicability of DMTs. Furthermore, those working in companies that had already implemented digital technologies reported more positive perceptions compared to non-users, emphasising the role of experience in shaping attitudes. The study highlights the need for targeted training and change management strategies to facilitate smoother workforce adaptation to digital advancements. These findings provide insights for policymakers, industry leaders, and system designers aiming to integrate human-centric approaches in the transition to Industry 4.0 and beyond.

Conducting Life Cycle Assessments (LCAs) for machines and tools in manufacturing is often time-intensive and hampered by difficulties in accessing suitable datasets. It is, nevertheless, important to consider the life cycle impacts of the machines and tools in a manufacturing process to identify hot spots, evaluate the relative importance of their impacts and develop strategies for further environmental improvements. This core novelty of this study is in the investigation of whether heterogeneous life cycle assessment (LCA) inventory and impact data for manufacturing machines can be meaningfully clustered, and whether the resulting groupings can be interpreted and operationalised as generalised proxy life cycle inventory datasets. It further evaluates the practical usefulness of these cluster-derived proxy datasets for supporting screening-level LCA and early-stage sustainability decision-making in manufacturing contexts. Three proxy LCA categories were developed, capturing broad similarities between machines while retaining sufficient detail for high-level sustainability assessments. The resulting machine proxy data provides a practical tool for streamlining LCA decision-making, allowing practitioners to estimate life cycle impacts from production to end-of-life even in the absence of detailed datasets. Validation against individual ecoinvent datasets showed that these generalised categories produce reasonably accurate approximations within typical uncertainty ranges, supporting exploratory analyses and screening applications. However, they are not intended to replace full LCAs for specific machines where precise assessment is required. Future work could enhance the proxy datasets by incorporating real-time operational data and regional variations, potentially using machine learning to refine impact estimates dynamically. Industrial integration of these datasets, such as in digital twin models or automated LCA platforms, would enable rapid, scalable sustainability assessments, supporting more informed decision-making in machine selection, procurement, and operational planning.

The present paper provides an overview of the state-of-the-art research, outlining the applications of the Industry 4.0 (I4.0) technologies on the aircraft manufacturing sector and their maturity state based on the technology readiness level (TRL) scale. A literature review has been conducted for the identification, selection, and evaluation of the published research. A total of 57 papers extracted from the two most relevant scientific databases for the area (Web of Science and Scopus), from 2010 to March 2021, were analysed and summarized. The research, analysis, and evaluation of these papers has provided an outlook of how the aircraft manufacturing industry is inserted into the I4.0 context, based on a classification of the I4.0 technologies maturity for this industrial branch. Then, a survey was performed with 12 specialists from 5 different aircraft manufacturing companies aiming to report the practical point-of-view in this area. Thus, this paper highlights and discusses the gaps found in the literature related to the I4.0 technologies applied to aircraft manufacturing and their main useful implications not only from the academic point-of-view but also from competitive business aspects, providing recommendations for industrial managers, engineers, and stakeholders. Finally, this paper proposes new opportunities and challenges for future research.

Additive Manufacturing is an innovative technology that fabricates parts layer by layer. However, in Laser Powder Bed Fusion (LPBF), printed metal parts often exhibit residual stresses, deformations, and other defects due to non-uniform temperature distribution during the printing process. To mitigate these issues, an optimized scan sequence within each layer can improve thermal uniformity. Traditional optimization methods, which rely on domain knowledge and employ trial-and-error or heuristic approaches, often fail to achieve optimal solutions due to the complex nature of the problem. One major challenge in improving scan strategies lies in the vast search space required to optimize the scan sequence for individual scan tracks within each layer, making it difficult to identify the best solution. To overcome this challenge, this work proposes an innovative scan strategy, Reinforced Scan, that leverages reinforcement learning to intelligently determine the optimal scan sequence. The method introduces a novel reward function that accounts not only for temperature variance but also for the spatial uniformity of the temperature field. By structuring the optimization problem into multiple hierarchical levels, the approach significantly reduces computational demand and enhances the manageability of the optimization process. The effectiveness of the proposed Reinforced Scan is validated through Netfabb™ Local Simulation and real-world laser scanning experiments on a Ti-6Al-4V thin plate. Its performance is compared against conventional heuristic scan sequences. Both simulation and experimental results demonstrate that Reinforced Scan achieves superior outcomes, notably reducing residual stress compared to traditional methods.

With the rapid advent of new information technologies (Big Data analytics, cyber-physical systems, such as IoT, cloud computing and artificial intelligence), digital twins are being used more and more in smart manufacturing. Despite the fact that their use in industry has attracted the attention of many practitioners and researchers, there is still a need for an integrated and comprehensive digital twin framework for reconfigurable manufacturing systems. To close this research gap, we present evidence from a systematic literature review, including 76 papers from high-quality journals. This paper presents the current research trends on evaluation and the digital twin in reconfigurable manufacturing systems, highlighting application areas and key methodologies and tools. The originality of this paper lies in its proposal of interesting avenues for future research on the integration of the digital twin in the evaluation of RMS. The benefits of digital twins are multiple such as evaluation of current and future capabilities of an RMS during its life cycle, early discovery of system performance deficiencies and production optimization. The idea is to implement a digital twin that links the virtual and physical environments. Finally, important issues and emerging trends in the literature are highlighted to encourage researchers and practitioners to develop studies in this area that are strongly related to the Industry 4.0 environment.

This study investigates the use of machine learning to predict bead geometry in cold metal transfer (CMT)-based wire-arc additive manufacturing (WAAM) of SS410 martensitic stainless steel. A dataset comprising 50 experimentally deposited single beads was developed indigenously to model the relationship between key process parameters and the resulting bead aspect ratio. Multiple regression and advanced tree-based ensemble models, including Random Forest, XGBoost, Extra Trees Regressor and Cat-Boost Regressor (CBR), were implemented to capture the influence of wire feed rate, deposition rate (torch travel speed), current and voltage on bead morphology. Comparative evaluation of the models, supported by independent validation demonstrated that the CBR model provides the most accurate prediction of aspect ratio among other models investigated in this study. Feature importance analysis indicated that welding current is the dominant parameter governing bead geometry, followed by voltage. Microstructural characterisation of the thick walls produced using the optimal process parameters revealed a progressive increase in δ-ferrite content with build height, associated with heat accumulation during multilayer deposition. Correspondingly, the ultimate tensile strength and yield strength decrease by 14% and 12.7%, respectively. These findings highlight the potential of machine-learning-based frameworks for predicting and optimising process-geometry relationships in WAAM, while also indicating the need for future AI-assisted strategies to control phase evolution and mitigate the formation of detrimental microstructural constituents in martensitic stainless steels. The online version contains supplementary material available at 10.1007/s00170-026-18086-6.

The growing complexity of manufacturing processes and the increasing diversity of decision-making tools present challenges in selecting effective approaches for process optimisation. Many existing tools are either too narrowly focused or inconsistently applied across sectors, limiting their broader impact. Additionally, the lack of clear integration strategies often hinders their full implementation in industrial settings. This systematic review examines decision-making tools that enable comparative assessments applied at the unit process level in manufacturing, covering both the selection between competing manufacturing routes and the optimisation of specific processes. A total of 37 journal articles were selected through a structured database search and evaluation process. The review analyses commonly used tools such as Multi-Criteria Decision Analysis (MCDA), Life Cycle Assessment (LCA), and Direct Comparison, highlighting their applications, benefits and limitations. Findings show that MCDA offers robust, multi-dimensional evaluations but is often constrained by complexity and data demands. In contrast, simpler methods like Direct Comparison provide more accessible insights but with a limited scope. Advanced tools such as Deep Learning and Computational Simulations hold promise but face challenges in scaling beyond the process level. Notably, there is limited integration of sustainability metrics within process-level decision-making. To address this, the study proposes a structured framework to guide future research and implementation, focusing on data management, AI integration and tool scalability. The results highlight the need for hybrid approaches that combine different tools to balance trade-offs and support long-term sustainability and operational efficiency in manufacturing systems.

This study investigates the mechanical response of antibacterial PA12/TiO2 nanocomposite 3D printed specimens by varying the TiO2 loading in the filament, raster deposition angle, and nozzle temperature. The prediction of the antibacterial and mechanical performance of such nanocomposites is a challenging field, especially nowadays with the covid-19 pandemic dilemma. The experimental work in this study utilizes a fully factorial design approach to analyze the effect of three parameters on the mechanical response of 3D printed components. Therefore, all combinations of these three parameters were tested, resulting in twenty-seven independent experiments, in which each combination was repeated three times (a total of eighty-one experiments). The antibacterial performance of the fabricated PA12/TiO2 nanocomposite materials was confirmed, and regression and arithmetic artificial neural network (ANN) models were developed and validated for mechanical response prediction. The analysis of the results showed that an increase in the TiO2% loading decreased the mechanical responses but increased the antibacterial performance of the nanocomposites. In addition, higher nozzle temperatures and zero deposition angles optimize the mechanical performance of all TiO2% nanocomposites. Independent experiments evaluated the proposed models with mean absolute percentage errors (MAPE) similar to the ANN models. These findings and the interaction charts show a strong interaction between the studied parameters. Therefore, the authors propose the improvement of predictions by utilizing artificial neural network models and genetic algorithms as future work and the spreading of the experimental area with extra variable parameters and levels.

The design of machine parts of different sizes and shapes has become relevant in the manufacturing industry which requires five-axis machine tools of high dynamic performance; different machining test pieces have been used to test and reflect the machine tool's performance. The S-shaped is still under development and consideration of which a new test piece better than the S-shaped part has been recommended to be put forward making the NAS979 the only standardized test piece; however, it has some limitations. Hence, this study proposes a new test piece to objectively satisfy the demand for machine tools with higher dynamic performance, which shows much improvement over the standard NAS979 and is the best alternative to the S-shaped test piece, and it combines the geometric and kinematic features of both test pieces. Geometrically, it has non-uniform surface continuity, variable twist angle, and variable curvature; and the cutting tool moves in close and opened angles along the tool path; there is sudden rise and fall of axes' velocity, acceleration, and jerk with much impact during machining which makes the S-cone test piece be machined by only five-axis machine tools with high dynamic performance, and has a better dynamic performance identification effect than the S-shaped test piece based on the trajectory test. Detailed work on the validation of the machine tool's dynamic performance using the S-cone part will be captured next part of this study.

In heavy industries like oil and gas, and shipbuilding, maintaining process quality is challenging. These sectors face inconsistent manual procedures and a shortage of skilled operators regarding thermal cutting and bevelling for welding preparation tasks. Manual fitting and repetitive quality control modifications, especially during thermal cutting, significantly increase time consumption and hinder productivity. Traditional thermal cutting methods are prone to human error, resulting in inconsistent cut quality, and demand high expertise leading to variability in cut precision, increased rework, and material wastage. The objective of this work is to address these challenges by introducing real-time ultrasonic sensing into a robotic plasma cutting control system to automate the steel plate bevelling process. The ultrasonic sensor enables the system to dynamically adapt to variations in steel plate thickness before cutting, ensuring precise and consistent results. The solution begins by presenting an automated method for measuring thickness and computing bevel distance per sample. Secondly, it proposes adaptive adjustments to cutting parameters per sample, leveraging the ultrasonic sensor data to enhance accuracy and reduce the need for manual intervention. Finally, the approach introduces adaptive robotic path generation for cutting and utilizing real-time ultrasonic sensor data to optimize cutting paths. The outcome of this study is the successful development and validation of an adaptive robotic plasma cutting system for steel plate bevel applications, which leverages real-time ultrasonic sensor data to automate the parameter input process and robotic motion planning, demonstrating improved accuracy and efficiency compared to traditional approaches. The results demonstrate that ultrasonic-driven robotic cutting significantly reduces the average error cut percentage to 4.47% with deviations ranging from 0.13 to 0.23° for the bevel angle and 14.27% with deviations between 0.02 and 0.05 mm for root face deviation, compared to the standard cutting approach which has an average error of 18% with deviations ranging from 0.10 to 0.38 mm and 77.1% with deviation between 0.48 to 0.90°, respectively. This paper highlights the benefits of using advanced sensing technology, particularly ultrasonic sensors, to automate plasma bevel cutting for metal plates in the steel fabrication and welding sectors.

Tolerance allocation is an important design step for determining robot accuracy and directly affecting manufacturing cost. However, existing methods typically consider dimension synthesis first before tolerances are allocated, which neglects the manufacturability constraints arising from the dependency between part size and achievable tolerance grades. This often leads to costly iterations between design and manufacturing and increasing manufacturing cost. To address this issue, an integrated tolerance allocation and dimensional synthesis method of robot design is proposed for optimizing both positioning accuracy reliability and manufacturing cost. The method simultaneously optimizes joint dimensions and corresponding tolerances by formulating a cost function that captures the relationship between dimensional parameters, robot end-effector accuracy reliability, tolerance-grade rules, and manufacturing cost. Additionally, a matrix-based Monte Carlo simulation (MCS) method is introduced to accelerate evaluation workspace-wide reliability under tolerance uncertainty. NSGA-II multi-objective optimization algorithm is employed to find the Pareto front of the optimal solutions. A case study of a surgical robot is taken to demonstrate the effectiveness of the proposed approach. Results show that the proposed method can reduce 22% of manufacturing cost while achieving better positioning accuracy reliability compared to the traditional tolerance allocation method, and the speed of matrix-based MCS method is improved by 400 times compared to the point-based MCS method.

Electric-field assisted (EA) manufacturing is a promising hybrid manufacturing technique, offering significant advantages over conventional manufacturing methods. Extensive experimental and numerical studies have demonstrated that the application of electric current reduces flow stress in metals and alloys, thereby improving their manufacturability. This enhancement is attributed to the synergistic effects of electroplasticity and Joule heating, both induced by the applied current during processing. Several key manufacturing processes have garnered substantial interest from the research community for their potential enhancement through electric fields. Here, we present a comprehensive review of recent developments in EA manufacturing over the last decade. The findings of various researchers investigating different EA manufacturing processes are discussed, accompanied by detailed tables summarizing the materials and electric current parameters employed in each process.

Precise tooling is vital for defect-free production of micro injection moulded (μ-IM) or hot-embossed products. The demoulding stage of such moulding and forming processes poses a serious challenge to the integrity of thin miniature features because of friction, adhesion, and thermal stresses. Typically, micro moulds involve geometrically textured patterns or features such as linear ridges, pillars, channels, and holes, the characteristic dimensions of which range from 10 to 300 μm. Realistically complex mould designs, containing precision micro features (enhanced fillet radius and positive draft angle) and high surface quality, are presented in this work. Electropolishing based on forward pulse currents (PC) has been used to shape and polish Ni micro moulds that contain sets of micron-scaled linear ridges and star patterns in order to ease the separation of moulded polymeric parts from the metallic mould during ejection and demoulding. The use of forward pulsed currents improved the mould design by increasing the fillet radii and draft angle while keeping the surface roughness low and maintaining a good surface shine. An optimization study of forward PC using a green solution of nickel sulfamate varied EP times (0-70 min) and duty cycles (40, 50, 60, and 70%) at a process conditions of 2.8 V, 50 °C, and 250 rpm. The best topographical and morphological changes were observed for a typical microfluidic channel (w × h, 100 × 110 μm) with an EP time of 70 min and 50% duty cycle: fillet radius increased by 3.8 μm, draft angle by 3.3°, and the channel width reduced by 11.4% while surface roughness changed by 8.6% and surface shine improved by 48.9%. Experimental validation was performed using hot embossing wherein the electropolished Ni mould replicated the micro channels and star patterns in PMMA chips with notably fewer burrs, material pile up, and no feature distortion. Moreover, there was a reduction in the side wall roughness of micro channels in PDMS casting with electropolished Ni mould by 16%. Hence, this work presents a significant scientific contribution to improving the efficiency of micro mould tools and reduces the defects caused by friction and adhesion in replicated polymeric parts.

This paper presents a novel holistic modeling approach for investigating and analyzing the relationship of qualitative variables such as training and absenteeism with quantifiable shopfloor key performance indicators such as quality, inventory, and production rate. Soft variables, supervisor support and work environment, and their relationships with the hard variables, facility layout, and production strategies were investigated in this research. It was found in the literature that increasing absenteeism reduces the rate of production and causes a decrease in motivation, while training can increase the level of motivation if effective. A causal loop diagram was developed based on the evidence in the literature, and a system dynamics simulation model was created to depict these relations. It was confirmed that absenteeism affected the cycle time and motivation inversely, but it was not possible to always maintain a desired level of motivation. A discrete event simulation model was also built for the current and the future state maps of the production system. The model used output from the system dynamics model as its input to investigate the effects of the qualitative variables on the production system performance. This paper discusses in detail the stages of building the simulation models and the results recorded.

Coating sprays play a crucial role in extending the capabilities of optical measuring systems, especially when dealing with reflective surfaces, where excessive reflections, caused by incident light hitting the object surface, lead to increased noise and missing data points in the measurement results. This work focuses on metal additively manufactured parts, and explores how the application of a sublimating matting spray on the measured surfaces can improve measurement performance. The use of sublimating matting sprays is a recent development for achieving temporary coatings that are useful for measurement, but then disappear in the final product. A series of experiments was performed involving measurement by fringe projection on a selected test part pre- and post-application of a sublimating coating layer. A comparison of measurement performance across the experiments was run by computing a selected set of custom-developed point cloud quality indicators: rate of surface coverage, level of sampling density, local point dispersion, variation of selected linear dimensions computed from the point clouds. In addition, measurements were performed using an optical profilometer on the coated and uncoated surfaces to determine both thickness of the coating layer and changes of surface texture (matte effect) due to the presence of the coating layer.