Drilling is the most important machining operation applicable to polymeric composite materials since it is essential for mechanical coupling of parts. Drilling process provokes several issues, ...including localized thermal shock, caused by the presence of abrasive and extremely hard fibres and the low thermal conductivity of composite, which restricts the heat dissipation. Such dangerous issue can be evaluated by examining the process temperature, whose value depends on cutting parameters. Therefore, monitoring of process temperature is indispensable to obtain useful information for machining optimization. In this work the influence of cutting parameters was analysed, measuring the temperature during drilling on tool and in the laminate for both CFRP and GFRP. Temperature trends as a function of cutting speed and feed rate were obtained and dangerous values of cutting parameters were identified. Finally, a preliminary numerical model was developed to simulate the temperature rising in the material during drilling.
•The effect of cutting parameters on the non-cutting energy consumption is considered.•The trade-off between cutting and non-cutting energy consumptions is optimised.•The optimal cutting parameters ...for turning operations are obtained by simulated annealing.•The relation between the part length and the optimal spindle rotation speed is analysed.•The approach achieves a 19.28% reduction of machining energy consumption for a case.
Reducing the machining energy consumption (MEC) of machine tools for turning operations is significant to promote sustainable manufacturing. It has been approved that selecting optimal cutting (turning) parameters is an effective approach to reduce the cutting energy consumption (CEC) within the MEC. However, the potentiality for this approach to reduce the non-cutting energy consumption (NCEC) has not received sufficient attentions. Especially, the energy consumed for spindle rotation change (SRCE) was neglected. Thus, this article aims at developing an integrated MEC model with NCEC and SRCE considered. Then, Simulated Annealing (SA) is employed to find the optimal spindle rotation speed (SRS) and feed rate which result in the minimum MEC. A case study is conducted, where five parts with different cutting lengths are processed on a lathe. The experiment results show that SA can obtain the global optimum in a short computation time when the step sizes for SRS and feed rate are 0.1 and 0.001, respectively. The optimal solution achieves a 19.28% MEC reduction. Finally, the relation between the part length and the optimal SRS is analysed, and the consequence of MEC minimisation on machining time is discussed.
Abstract Precision cutting technology plays an important role in modern manufacturing, which providing key solutions for the production of high-precision, high-quality parts. Firstly, on the basis of ...reading a lot of relevant papers, the classification of precision cutting technology are summarized in this paper, and then the research status of cutting force control, tool wear, nano cutting, model simulation and optimization and green sustainable development are summarized and analyzed. On this basis, the problems existing in the current precision cutting technology are pointed out, and the future development trend is predicted.
Future sensing applications will include high-performance features, such as toxin detection, real-time monitoring of physiological events, advanced diagnostics, and connected feedback. However, such ...multi-functional sensors require advancements in sensitivity, specificity, and throughput with the simultaneous delivery of multiple detection in a short time. Recent advances in 3D printing and electronics have brought us closer to sensors with multiplex advantages, and additive manufacturing approaches offer a new scope for sensor fabrication. To this end, we review the recent advances in 3D-printed cutting-edge sensors. These achievements demonstrate the successful application of 3D-printing technology in sensor fabrication, and the selected studies deeply explore the potential for creating sensors with higher performance. Further development of multi-process 3D printing is expected to expand future sensor utility and availability.
Kirigami tessellations, regular planar patterns formed by partially cutting flat, thin sheets, allow compact shapes to morph into open structures with rich geometries and unusual material properties. ...However, geometric and topological constraints make the design of such structures challenging. Here we pose and solve the inverse problem of determining the number, size and orientation of cuts that enables the deployment of a closed, compact regular kirigami tessellation to conform approximately to any prescribed target shape in two or three dimensions. We first identify the constraints on the lengths and angles of generalized kirigami tessellations that guarantee that their reconfigured face geometries can be contracted from a non-trivial deployed shape to a compact, non-overlapping planar cut pattern. We then encode these conditions into a flexible constrained optimization framework to obtain generalized kirigami patterns derived from various periodic tesselations of the plane that can be deployed into a wide variety of prescribed shapes. A simple mechanical analysis of the resulting structure allows us to determine and control the stability of the deployed state and control the deployment path. Finally, we fabricate physical models that deploy in two and three dimensions to validate this inverse design approach. Altogether, our approach, combining geometry, topology and optimization, highlights the potential for generalized kirigami tessellations as building blocks for shape-morphing mechanical metamaterials.
Plane milling is one of the most prevalent methods in metalworking, recognized for its potential to uncover energy-saving opportunities by optimizing cutting parameters. However, existing research on ...cutting parameters optimization primarily adopts a phased independent optimization approach, with some studies focusing on rough milling parameters and others on finish milling parameters under predetermined allowances. These research methods lack integrated optimization for both rough and finish milling parameters, leading to suboptimal outcomes. To address this issue, this paper proposes an integrated multi-objective optimization method for both rough and finish cutting parameters in plane milling. The aim is to achieve sustainable machining by considering efficiency, energy consumption, and quality. Specifically, the research accomplishes three key objectives: (1) developing detailed models for the efficiency, energy, and quality of plane milling; (2) presenting an integrated multi-objective optimization model and approach for determining rough and finish cutting parameters in plane milling; (3) conducting a specific case study to validate the effectiveness and feasibility of the proposed model and approach. Experimental findings demonstrated that optimizing cutting parameters in the plane milling process through an integrated approach can reduce processing time by 19.37%, decrease energy consumption by 16.88%, and significantly improve the surface roughness of the workpiece by 27.07% compared to traditional empirical methods. Furthermore, compared to independent optimization schemes, processing time is reduced by 2.99%, energy consumption is lowered by 4.72%, and surface roughness is improved by 13.16%.
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•A novel integrated multi-objective optimization method is proposed for plane milling.•Both rough and finish cutting parameters are considered in the optimization model.•The multi-objective model considering efficiency, energy, and quality is established.•The method can better help engineers in the energy lean management of machine tools.
Numerous researchers have developed theoretical and experimental approaches to force prediction in surface grinding under dry conditions. Nevertheless, the combined effect of material removal and ...plastic stacking on grinding force model has not been investigated. In addition, predominant lubricating conditions, such as flood, minimum quantity lubrication, and nanofluid minimum quantity lubrication, have not been considered in existing force models. This work presents an improved theoretical force model that considers material-removal and plastic-stacking mechanisms. Grain states, including cutting and ploughing, are determined by cutting efficiency (β). The influence of lubricating conditions is also considered in the proposed force model. Simulation is performed to obtain the cutting depth (ag) of each “dynamic active grain.” Parameter β is introduced to represent the plastic-stacking rate and determine the force algorithms of each grain. The aggregate force is derived through the synthesis of each single-grain force. Finally, pilot experiments are conducted to test the theoretical model. Findings show that the model's predictions are consistent with the experimental results, with average errors of 4.19% and 4.31% for the normal and tangential force components, respectively.
•Grinding force model based on material removal and plastic stacking mechanism is built.•Dynamic active grains in grinding zone are classified into cutting and ploughing grains.•β presents a S-shaped trend with the increase of ag, ag = 1.18 μm was the critical value.•Tribological tests is conducted to obtain μ under different lubricating conditions.•Model shows 4.19% and 4.31% of the average error for the normal/tangential force.
The research and application of high speed metal cutting (HSMC) is aimed at achieving higher productivity and improved surface quality. This paper reviews the advancements in HSMC with a focus on the ...material removal mechanism and machined surface integrity without considering the effect of cutting dynamics on the machining process. In addition, the variation of cutting force and cutting temperature as well as the tool wear behavior during HSMC are summarized. Through comparing with conventional machining (or called as normal speed machining), the advantages of HSMC are elaborated from the aspects of high material removal rate, good finished surface quality (except surface residual stress), low cutting force, and low cutting temperature. Meanwhile, the shortcomings of HSMC are presented from the aspects of high tool wear rate and tensile residual stress on finished surface. The variation of material dynamic properties at high cutting speeds is the underlying mechanism responsible for the transition of chip morphology and material removal mechanism. Less surface defects and lower surface roughness can be obtained at a specific range of high cutting speeds, which depends on the workpiece material and cutting conditions. The thorough review on pros and cons of HSMC can help to effectively utilize its advantages and circumvent its shortcomings. Furthermore, the challenges for advancing and future research directions of HSMC are highlighted. Particularly, to reveal the relationships among inherent attributes of workpiece materials, processing parameters during HSMC, and evolution of machined surface properties will be a potential breakthrough direction. Although the influence of cutting speed on the material removal mechanism and surface integrity has been studied extensively, it still requires more detailed investigations in the future with continuous increase in cutting speed and emergence of new engineering materials in industries.
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•Material removal mechanism of HSM is summarized considering material dynamic properties.•Machined surface integrity is discussed with emphasis on metallurgical alterations.•Cutting force/temperature and tool wear behavior in HSM are summarized.•The pros and cons of HSM are elaborated to guide its industrial application.•Challenges for further development of HSM and future directions are highlighted.
Deep eutectic solvents (DESs), surmised as "the organic reaction medium of the century", have reverberated a new symphony throughout the present green millennium. A brief historical account of the ...DES systems and their physicochemical properties as task-specific and designer solvents for cross-coupling reactions are appraised including the hole theory that explains the underlying mechanistic pathway for this emerging neoteric medium. The insights into cross-coupling reactions and their applications are included, highlighting the significant achievements pertaining to the dual role of DESs as a solvent and catalyst. In addition, popular "name-reactions" for the carbon-carbon and carbon-heteroatom bond formations related to the nature of DESs and the core optimum conditions are included. The review also encompasses the novel approaches to privileged catalytic systems and identify the voids left in cross-coupling reactions where DES systems have not made inroads yet. Finally, the challenges of utilizing the neoteric derivatives of DES for these reactions are expounded.
Deep eutectic solvents and their physicochemical properties as task-specific and designer solvents for cross-coupling reactions, are appraised.
Monitoring surface quality during machining has considerable practical significance for the performance of high-value products, particularly for their assembly interfaces. Surface roughness is the ...most important metric of surface quality. Currently, the research on online surface roughness prediction has several limitations. The effect of tool wear variation on surface roughness is seldom considered in machining. In addition, the deterioration trend of surface roughness and tool wear differs under variable cutting parameters. The prediction models trained under one set of cutting parameters fail when cutting parameters change. Accordingly, to timely monitor the surface quality of assembly interfaces of high-value products, this paper proposes a surface roughness prediction method that considers the tool wear variation under variable cutting parameters. In this method, a stacked autoencoder and long short-term memory network (SAE-LSTM) is designed as the fundamental surface roughness prediction model using tool wear conditions and sensor signals as inputs. The transfer learning strategy is applied to the SAE-LSTM such that the surface roughness online prediction under variable cutting parameters can be realized. Machining experiments for the assembly interface (using Ti6Al4V as material) of an aircraft's vertical tail are conducted, and monitoring data are used to validate the proposed method. Ablation studies are implemented to evaluate the key modules of the proposed model. The experimental results show that the proposed method outperforms other models and is capable of tracking the true surface roughness with time. Specifically, the minimum values of the root mean square error and mean absolute percentage error of the prediction results after transfer learning are 0.027 μm and 1.56%, respectively.
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