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Paper dusting has recently been recognized as a significant issue in the hygiene tissue industry. This review considers the factors that may contribute to dusting and linting, as well as the challenges of evaluating dusting propensity. Examining both the paper and textile industries, several methods of dusting and linting assessment exist. This review describes the equipment, methodologies, targeted paper grades or materials, advantages, and disadvantages. Standards (if applicable) are also covered for each of the major methods. These existing methods were found to have various drawbacks, suggesting a need for an improved, standardized method and device. In conclusion, and to motivate future research, mechanisms of dust release are reviewed along with existing and possible solutions to combat the tissue-dusting problem. This paper outlines the importance of creating a repeatable, standard, and accurate testing procedure and methodology for dust particle characterization in the hygiene tissue industry. [ABSTRACT FROM AUTHOR]

Manual repair tasks in the industry of maintenance, repair, and overhaul require experience and object-specific information. Today, many of these repair tasks are still performed and documented with inefficient paper documents. Cognitive assistance systems have the potential to reduce costs, errors, and mental workload by providing all required information digitally. In this case study, we present an assistance system for object-specific repair tasks for turbine blades. The assistance system provides digital work instructions and uses augmented reality to display spatial information. In a user study with ten experienced metalworkers performing a familiar repair task, we compare time to task completion, subjective workload, and system usability of the new assistance system to their established paper-based workflow. All participants stated that they preferred the assistance system over the paper documents. The results of the study show that the manual repair task can be completed 21% faster and with a 26% lower perceived workload using the assistance system. [ABSTRACT FROM AUTHOR]

Origami techniques derived from antique paper folding arts have been applied to solve engineering problems such as weight/space-saving structures and their rapid deployment for foldability. Recently, origami techniques' potential in mechanical reinforcement for structural designs has also been discussed. For example, the mechanical performance of origami tubular structures can be significantly improved compared with the circular and hexagonal tubes under lateral loading. In this paper, an origami-inspired tubular structure was applied as the infill pattern for additive manufacturing, and energy absorption performance under dynamic and static loadings was investigated. Fused filament fabrication was selected as the testbed for the origami-inspired infill pattern investigation. The samples with the origami infill pattern presented higher dynamic and quasi-static energy absorption performance than samples with grid or honeycomb infill patterns. The proposed structural reinforcement method can be expanded to crash-worthy structures, thin-wall structures, functional structures, and the design of lightweight, high-strength parts. [ABSTRACT FROM AUTHOR]

In high-speed milling, the machining system is affected by a chatter resulting from the dynamic interaction between the tool and the part, which can cause harmful effects on the tool and the machined surface of the part. Chatter occurs more frequently for the milling of thin-walled parts due to their low stiffness. In addition, the dynamic characteristics of thin-walled parts vary along the tool path. The dynamics of the part are therefore the dominant factor that should be considered in the modeling and the study of the milling process and must be performed in 3D, where the third dimension is the tool position. This paper studies the milling stability of Al 7075-T6 thin-walled parts during high-speed milling considering the variation of dynamic characteristics and develops three-dimensional stability lobe diagrams of the spindle speed, axial depth of cut, and tool position. The dynamic equations of motion are solved numerically using semi-discretization method. Modal parameters of the tool and the part were extracted experimentally by modal tests. Then, cutting tests were conducted to validate the established model by measuring the machined surface roughness which is used as a criterion for detecting instability. The experimentally obtained results correspond well with the predicted stability limits. Moreover, the influence of different cutting parameters on the machining stability along the tool path was investigated. It is found that the variable spindle speed improves significantly the cutting process, and the best selection of feed per tooth impacts positively on the machined surface quality. [ABSTRACT FROM AUTHOR]

The paper industry manufactures corrugated cardboard packaging, which is unassembled and stacked on pallets to be supplied to its customers. Human operators usually classify these pallets according to the physical features of the cardboard packaging. This process can be slow, causing congestion on the production line. To optimise the logistics of this process, we propose a visual recognition and tracking pipeline that monitors the palletised packaging while it is moving inside the factory on roller conveyors. Our pipeline has a two-stage architecture composed of Convolutional Neural Networks, one for oriented pallet detection and recognition, and another with which to track identified pallets. We carried out an extensive study using different methods for the pallet detection and tracking tasks and discovered that the oriented object detection approach was the most suitable. Our proposal recognises and tracks different configurations and visual appearance of palletised packaging, providing statistical data in real time with which to assist human operators in decision-making. We tested the precision-performance of the system at the Smurfit Kappa facilities. Our proposal attained an Average Precision (AP) of 0.93 at 14 Frames Per Second (FPS), losing only 1% of detections. Our system is, therefore, able to optimise and speed up the process of logistic distribution. [ABSTRACT FROM AUTHOR]

This paper investigated the cutting behaviors and chip formation in machining of carbon fiber reinforced polymer (CFRP) composites. The single cutting edge and unidirectional (UD) laminates of six different fiber orientations from 0° to 150° with an interval of 30° were employed in the orthogonal cutting test. The effects of fiber cutting angle, cutting speed and cutting depth on cutting mechanism and micro-damage evolution were estimated. Cutting force and thrust force obtained by dynamometer were used to evaluate the interaction between the tool and the workpiece. Subsurface damage was assessed by damage depth and failure mode of microstructure such as fiber and matrix. The machined surface roughness was used to characterize the machined surface quality. The results indicated that the average cutting force at θ > 90° was 7.58 times higher than that at θ < 90° and large deformed fibers below the machined surface existed elasticity recovery in the former case. The cutting speed and cutting depth had the most significant effects on the cutting force at θ = 90° where the fiber bending deformation and fiber kinking keep cutting force at a high level. The micromorphology of machined surface and subsurface revealed four typical cutting mechanisms, namely, interface-debonding, fracture-sliding, shearing-fracture, and bending-fracture in the range of θ = 0 ~ 180°. The variation trend of surface roughness with cutting parameters at θ > 90° is obvious and in consistent with that of cutting force. The most efficient factor on the roughness is found to be the fiber cutting angle accounting for 98.60%, followed by the cutting depth (0.55%) and cutting speed (0.25%). The interaction between the factors is not significant according to the results of ANOVA. [ABSTRACT FROM AUTHOR]

Smart structures of 4D printing are perhaps interdisciplinary research area with significant development and applicability future. The cornerstone of 4D printing is smart materials. When a stimulus is put in front of this structure, it changes into a different structure. This article initially provides a succinct insight of the aforementioned 4DP: SMs-SSs: 4D printing (4DP) technology imminent enlargements of smart material's (SMs) prospective to physique smart structure (SSs). Investigation into 4D printing has pulled in phenomenal enthusiasm. The research work of 4DP: SMs-SSs has stimulated promising future. The paper discusses smart materials and their potential towards mainstream 4D printing for smart constructions (4DP: SMs-SSs). Cursory research write-up on smart materials associated 4D printing approaches to process them based on adaptability to stimuli, fabrication, control mechanisms, multi physics modeling, and existing as well as emerging functionalities. Indeed, innovative structural initiatives could perhaps inspire new paradigms for stimulate positive structures. Novelty structures are being thoroughly researched, and new ideas will be incorporated. Programmability, reactivity toward and adaptability to their circumstances, and automation are all functionality of 4D-printed items. The article's conclusion is that 4DP: SMs that can create intelligent/smart structures (SSs) will even trigger a massive era of construction material. This article shows an insight into how quickly technology is changing, how some researchers and scholars are figuring out what it can do, and how engineers could use the ideas. The adoption of smart materials will assist in resolving the problem to a greater extent. 4D printing relies on shape memory alloys/polymers (SMAs/SMPs) as so forth organic materials. Imminent enlargements of smart materials prospective to physique smart structures are still being experimented with by scientists and engineers. [ABSTRACT FROM AUTHOR]

The process of complex product assembly-commissioning has the characteristics of high flexibility and firm dynamics. To overcome the drawbacks of manual assembly, deploying automated and intelligent techniques can greatly boost efficiency, improve flexibility, and enhance the quality control. The human–robot collaborative (HRC) technology combines the advantages of human capabilities and the efficiency and precision of robots. However, current HRC technology lacks of perception and cognitive ability, especially in dynamic environments. Therefore, this paper proposed a digital twin-driven HRC assembly-commissioning framework. In this framework, a virtual-physical mapping environment for HRC is constructed. In order to improve the cognitive ability of robot units to tasks, a motion intention recognition approach is proposed which integrates the feature of part into human joint sequences. To improve the adaptability of the robot unit to tasks, the assembly-commissioning task knowledge graph is developed to extract the action sequence of the robot unit in a timely manner. Moreover, the deep deterministic policy gradient (DDPG) is used to adaptively adjust the robot unit movement path in the process of assembly-commissioning. Finally, the effectiveness of the proposed method is verified by taking a particular type of automobile generator as a case study product. [ABSTRACT FROM AUTHOR]

Nanocutting is a method of direct machining in nanomanufacturing field, and it is an important ultraprecision machining method. Nanocutting technology adopts single-point diamond cutting and nanoprobe scratching methods to make the machining accuracy of parts reach the nanometric scale. It is the method with the highest machining accuracy at present, so it is widely used in the processing of precision components. At the same time, nanocutting technology can be used to process nanostructures and provide processing methods for basic research in condensed matter physics. This paper summarized the cutting mechanism and cutting force in the nanocutting process, including the mechanism of nanocutting and brittle–ductile transition. The influence of RTS, temperature, wear and subsurface phase transformation on cutting performance is also discussed. At the same time, the characteristics of cutting force and friction coefficient during the cutting process are reviewed. After that, the simulation and experiment research of the nanocutting process are analyzed. At the end of the paper, the challenges and prospects of nanocutting technology are proposed and summarized. [ABSTRACT FROM AUTHOR]

Tool wear is unavoidable during machining, which is one of the most common tool failure modes. It is significant to evaluate the tool state quickly and effectively for timely tool change strategy. The cutting vibration signals after tool wear show strong non-Gaussian characteristics. Higher order spectrum is a powerful tool for analyzing the non-Gaussian characteristics of signals, and can restrain noise and provide more information than classical power spectrum analysis. This paper presents a milling tool wear state monitoring method based on higher order spectrum entropy. Due to the large amount of calculation of bispectrum, bispectrum diagonal slice is investigated. And the diagonal slice spectral entropy is proposed as tool wear indicator to monitor tool state. To verify the proposed method, cutting vibration signal of CNC machining center were collected and analyzed. The experimental results showed that the proposed approach can effectively monitor and diagnose the tool state, and has good robustness. It is feasible and effective for online monitoring milling tool wear. [ABSTRACT FROM AUTHOR]

Milling force during machining process has strong time-varying and multi-frequency characteristic, which is easy to cause tool vibration, wear, and cutting chatter, and even lead the machining accuracy of parts to be out of tolerance. The paper proposed a milling force prediction method for a general helix cutter during peripheral milling. In this model, the geometric parameters of milling cutter are parametric defined, and then an analytical instantaneous uncut chip thickness (IUCT) model is presented with taking cutter runout (offset and inclination) and geometric entity shape of workpiece into account. A series of milling experiments using aluminum alloy were carried out with six kinds of milling cutters in a high-speed machining center. The analysis results show that the predicted value of milling force can better reflect the change trend of the actual measured one, and well prove the validity of the presented method. Furthermore, the influences of milling parameters, cutter runout, milling type, cutter tooth number, and unequal spacing angle on the time domain and frequency domain excitation characteristics of milling force are systematically analyzed, which can provide useful guidance for the tool design and machining parameter selection in the future. [ABSTRACT FROM AUTHOR]

Faced with the global crisis of COVID-19 and the strong increase in customer demands, competition is becoming more intense between companies, on the one hand, and supply chains on the other. This competition has led to the development of new strategies to manage demand and increase market share. Among these strategies are the growing interest in sustainable manufacturing and the need for customizable products that create an increasingly complex manufacturing environment. Sustainable manufacturing and the need for customizable products create an environment of increased competition and constant change. Indeed, companies are trying to establish more flexible and agile manufacturing systems through several systems of reconfiguration. Reconfiguration contributes to an extension of the manufacturing system's life cycle by modifying its physical, organizational and IT characteristics according to the changing market conditions. Due to the rapid development of new information technology (such as IoT, Big Data analytics, cyber-physical systems, cloud computing and artificial intelligence), digital twins have become intensively used in smart manufacturing. This paper proposes a digital twin design and simulation model for reconfigurable manufacturing systems (RMSs). [ABSTRACT FROM AUTHOR]

Based on the proposed model, a multi-objective optimization framework is presented to optimize the feed rate with the objective of increasing the machining efficiency and the loading stability of the spindle. The in-process machining data generated in the machining process is calculated and aligned for building the data-based model of spindle power, and an artificial neural network (ANN)-based modeling approach is proposed for the spindle power. To validate the feasibility and advantage of the proposed methods, a set of machining experiments are conducted, showing that our proposed ANN-based model has good accuracy in terms of predicting the spindle power, as well as that the feed rate optimization framework as solved based on the multi-objective evolutionary algorithm based on decomposition (MOEA/D) can effectively improve the machining efficiency and reduce the fluctuation of spindle power. [Extracted from the article]

In the paper-based packaging field, a sheet of paper material is introduced in a die cutter to create several creases and cuts. These creases allow to fold the sheet, and consequently, to build the final package. Thus, the quality of the crease is critical to get a valuable final product. In this work, a new system for automatic crease inspection is presented. The inspection is performed by processing crease visual information captured by a laser sensor. Automation is achieved by moving the laser with a cartesian robot along the sheet. A crease profile processing algorithm is proposed as well, which is able to extract geometric features from the different patterns that are associated with different paper types. Moreover, a crease classification methodology is proposed, where creases are grouped taking into account some physical properties. The experiments performed show the viability and usefulness of the proposed system. [ABSTRACT FROM AUTHOR]

The B. John Davies Prize for the best paper published in IJAMT 2020 has been awarded to Yu YANG, Shimin MAO, Bo BAI, and Yuhua KUANG. It is applied directly to the free-form machine tool, eliminating the need for the equivalent conversion of machine settings from the cradle-type machine tool to the free-form machine tool, and taking advantage of the flexibility and freedom offered by the free-form machine tool. [Extracted from the article]

The digital twin (DT) technology is currently considered a key technology for the digital representation of real-world systems. The application of DT technology in smart manufacturing can provide accurate model support for the analysis of mechatronic equipment applications based on model simulations. However, for such a mechanics-electric-hydraulic-control coupled complex system of mechatronics equipment, how to quickly and effectively construct its consistent multi-domain DT mechanism model has become the biggest obstacle to the wide application of DT technology in this field. Therefore, based on the synthesis of existing model construction methods for mechatronics equipment, this paper proposes multi-domain, multi-level, parametric, and consistent mechatronics equipment DT mechanism model construction guidelines. Based on the proposed model construction guidelines, a DT mechanism model construction process for mechatronics equipment is given. Finally, a consistent multi-domain DT mechanism model of computer numerical control machine tools (CNCMT) is constructed by Simscape, to verify the feasibility of the proposed method. The solutions in this paper provide a reliable, rapid, and consistent mechanism model along with construction guidelines and theoretical systems for engineers or researchers that use DT technology to solve specific application problems. Also, the implementation of the virtual commissioning application case in this paper provides application guidance for service analysis based on the DT model. [ABSTRACT FROM AUTHOR]

Sustainability in electronics has a growing importance due to, e.g. increasing electronic waste, and global and European sustainability goals. Printing technologies and use of paper as a substrate enable manufacturing of sustainable electronic devices for emerging applications, such as the multi-layer anti-counterfeit label presented in this paper. This device consisted of electrochromic display (ECD) element, NFC (near field communication) tag and circuitry, all fully roll-to-roll (R2R) printed and assembled on plastic-free paper substrate, thus leading to a sustainable and recyclable device. Our setup uses harvested energy from HF field of a smartphone or reader, to switch an electrochromic display after rectification to prove authenticity of a product. Our novelty is in upscaling the manufacturing process to be fully printable and R2R processable in high-throughput conditions simulating industrial environment, i.e. in pilot scale. The printing workflow consisted of 11 R2R printed layers, all done in sufficient quality and registration. The printed antennas showed sheet resistance values of 32.9±1.9 mΩ/sq. The final yield was almost 1500 fully printed devices, and in R2R assembly over 1400 labels were integrated with 96.5% yield. All the assembled tags were readable with mobile phone NFC reader. The optical contrast (ΔE*) measured for the ECDs was over 15 for all the printed displays, a progressive switching time with a colour change visible in less than 5 s. The smart tag is ITO-free, plastic-free, fully printed in R2R and has a good stability over 50 cycles and reversible colour change from light to dark blue. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions during belt grinding is of great significance for environmentally friendly production in the manufacturing industry. In this paper, in order to better grinding aero-engine titanium alloy blades with the abrasive belt, an improved NSGA-II multi-objective optimization algorithm was proposed, which reduced the carbon emissions during the grinding process while ensuring the same surface roughness and material removal rate. Firstly, through analysis and finite element simulation, the model of abrasive belt grinding force is established and the rationality of the model is verified by experiments; furthermore, the carbon emission model of abrasive belt grinding and the multi-objective optimization model based on the improved NSGA-II algorithm are established; finally, the results of the algorithm are verified and compared through numerical simulation and experiments. Compared with the NSGA-II algorithm and the multiple objective particle swarm optimization algorithm, the optimization results of the algorithm in this paper have better diversity and uniformity and can find better non-dominated optimal solutions; the process parameters selected by the algorithm in this paper can more effectively reduce the carbon emissions during grinding. The optimization method proposed in this paper has certain reference significance for engineering practice. [ABSTRACT FROM AUTHOR]

The hot-dip galvanizing process is widely used in the production of steel products as an important anti-corrosion method for steel products. Due to the complexity of the galvanizing process and the immaturity of some technologies, some factory productions of hot-dip galvanizing products are often with defects such as unsuitable spangle size in the head and tail, unsuitable spangle size in the edge, diagonal edge, and penetration streaks (zinc flow lines), which seriously affect the appearance of the zinc layer and subsequent using function, and are likely to cause undesirable consequences. In order to solve these problems, the object detection method in the field of deep learning is applied to the zinc spangles defects detection. Firstly, a dataset of zinc spangles defects on zinc-coated steel surfaces was established, and the dataset is preprocessed in this paper. Data enhancement is used to improve the generalization ability of the model, and Wiener filter is used to reduce the motion blur. This paper combines transformer structure and Bi-FPN structure with the YOLO-v5 model to propose the YOLOv5-Transformer-BiFPN (YOLOv5-TB) model. The YOLOv5-TB model is used for zinc-coated steel defect detection, which can accurately and quickly detect spangles defects on the surface of zinc-coated steel. The experimental results show that the overall performance of YOLOv5-TB network applied to the detection of zinc spangles defects on zinc-coated steel surfaces is better than most of the current mainstream Object detection algorithms. [ABSTRACT FROM AUTHOR]

In the milling process, the wear area of the tool is often segmented using the traditional image processing method to quantify the tool wear value. However, these methods have the disadvantages of having weak anti-noise capabilities and low segmentation accuracy. Although the semantic segmentation network can achieve excellent segmentation accuracy, obtaining enough end mill wear images to support the semantic segmentation network's training is challenging due to the high acquisition cost of wear images. As a result, this paper suggests a small sample end mill wear area segmentation method based on transfer learning and generative adversarial networks to address the issue of insufficient samples of end mill wear images. In this paper, WGAN is used to generate wear images to expand the dataset with a few samples, and the transfer learning method is used to improve the generalization ability of the segmentation network and finally achieve small sample training. This approach increases mPA by 4.46% and mIOU by 8.97% when compared to merely using the semantic segmentation network for small sample training. According to experimental findings, this method not only has high stability and segmentation accuracy but also solves the problem of insufficient end mill wear image samples. The method proposed in this paper can be effectively applied to the intelligent detection of the tool wear state, improving the accuracy and stability of the measurement of the tool wear value. [ABSTRACT FROM AUTHOR]

Augmented Reality (AR) is a pillar of the transition to Industry 4.0 and smart manufacturing. It can facilitate training, maintenance, assembly, quality control, remote collaboration and other tasks. AR has the potential to revolutionize the way information is accessed, used and exchanged, extending user's perception and improving their performance. This work proposes a Pervasive AR tool, created with partners from the industry sector, to support the training of logistics operators on industrial shop floors. A Human-Centered Design (HCD) methodology was used to identify operators difficulties, challenges, and define requirements. After initial meetings with stakeholders, two distinct methods were considered to configure and visualize AR content on the shop floor: Head-Mounted Display (HMD) and Handheld Device (HHD). A first (preliminary) user study with 26 participants was conducted to collect qualitative data regarding the use of AR in logistics, from individuals with different levels of expertise. The feedback obtained was used to improve the proposed AR application. A second user study was realized, in which 10 participants used different conditions to fulfill distinct logistics tasks: C1 — paper; C2 — HMD; C3 — HHD. Results emphasize the potential of Pervasive AR in the operators' workspace, in particular for training of operators not familiar with the tasks. Condition C2 was preferred by all participants and considered more useful and efficient in supporting the operators activities on the shop floor. [ABSTRACT FROM AUTHOR]

Inspired by cloud computing, cloud manufacturing (CMfg) is a service-oriented manufacturing paradigm on an on-demand and pay-as-you-go business model through the internet. More specifically, new challenges for production planning and decision-making process have emerged in that resource scheduling and have gained the most attention, and there is an urgent need to determine the current status and identify issues and matters to be addressed in the future. This review paper is aiming to discuss aspects of the cloud-based resource scheduling problem through investigating the literature to date to identify the existing gaps and recommending the potential paths moving forward for researchers in this field. So far, literature reviews focused on a broad scope of cloud-based scheduling, as a new approach taking a "narrow scope" by focusing on resource scheduling and various steps of it in the cloud environment are considered. Using the data gathered from the popular databases, a comprehensive statistical analysis on the existing literatures is provided, and the rational sequences of the systematic literature review (SLR) are elaborated. The mathematical models in resource scheduling are thoroughly elucidated. Then, a comprehensive analysis of the main aspects of resource scheduling including the objective functions, constraints, and optimization algorithms is presented. Discussion of the findings of the review paper illustrates that time and cost gain more attention (almost 80%) among all objective functions, and the metaheuristic algorithms are the most widely used in the recent research papers. Finally, suggestions for potential future research to further consolidate this field have been enumerated. [ABSTRACT FROM AUTHOR]

As an important part of the real economy, manufacturing industry plays a major role in the whole human society. Smart manufacturing has become a strategic issue for many countries. Smart manufacturing puts forward higher requirements for the intelligence of shop-floor production process, product operation and maintenance, logistics and supply chain, which are inseparable from the support of advanced communication technology. As a new generation of mobile communication technology, 5G plays an important role in many areas of smart manufacturing with the characteristics of high bandwidth, low latency, and massive connectivity. This paper first analyzed the communication requirements for machine-to-machine, manufacturing Internet of Things, cyber-physical system-based manufacturing, logistics and supply chain, industrial Internet platform and digital twin–driven manufacturing. Based on the requirements, the research and application progress of 5G in manufacturing are investigated from the above six aspects. In addition, this paper proposed relevant future research hotspots for the further integration of 5G and the above-mentioned six areas of smart manufacturing. [ABSTRACT FROM AUTHOR]

The increase in port scale and business complexity has led to an increased demand for comprehensive and lean control on ports. The current operation mode is facing the bottleneck of the increasingly significant production efficiency and performance. Digital twin (DT) technology realizes holographic visual management and control patterns using cyber-physical fusion and promotes the transformation of a port to an intelligent operation mode. In this paper, the framework of a digital twin application system is proposed based on the analysis of business characteristics of large-scale comprehensive ports. Construction methods and technologies such as digital twin modeling, global ubiquitous perception, data mapping, and model fusion are analyzed. With regard to the construction needs of Qingdao Port's digital twin system, this paper presents a case study and illustrates the overall design process and function of the digital twin system for typical terminals. The system realizes the intelligent operation of the port with the core functions of three-dimensional visual monitoring and optimal dispatching based on real-time perception data. This paper serves as a feasible reference for future intelligent development of large ports and the application of digital twin technology. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A hot stamping process of quenchable steel tubes using a mandrel reinforced with eco-friendly fibers was developed to produce ultra-high strength steel hollow parts having enhanced lightweighting and crashworthiness. High internal pressure was generated to improve the die quenchability and shape accuracy of the formed parts by the fiber reinforcement. Wood sawdust, shredded copy paper, and plant fiber made of recycled toilet paper were chosen as the fibers, and not only the strength was evaluated from a uniaxial compression test but also the melting behavior of the mandrel was examined. The influence of the fiber reinforcement on the shape accuracy and die quenchability of hot-stamped parts was investigated. The generated internal pressure with the fiber-reinforced ice mandrel was higher than that with the pure ice mandrel without the reinforcement, and thus, the shape accuracy and die quenchability of hot-stamped parts were significantly improved even for a comparatively small change in internal volume of tubes. [ABSTRACT FROM AUTHOR]

Ultrasonic-assisted machining is an advanced method which could improve the process of machining. Besides, simulation modeling process is a method to help the researchers analyze different aspects of the process with more details in a shorter time. Simulation of ultrasonic-assisted machining is also a field of research that is of interest to researchers working in the field of machining processes. In recent years, a variety of papers have been published where cutting forces, chip formation, tool wear and temperature, and microstructure changes were simulated. That being the case, a review paper is required to represent the advances implemented by researchers in the simulation of ultrasonic-assisted machining process. Moreover, the difficulties and necessities of this process are mentioned at the end of this review paper. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Surface roughness is widely used for product quality assessment due to its ability to accurately portray the fatigue strength, wear resistance, surface hardness, and other properties of a product. In this paper, a spatiotemporal adaptive ensemble prediction method (STAEP) was proposed through the combination of static cutting parameters and dynamic vibration signals. We firstly performed envelope analysis of the vibration signal for the amplitude modulation phenomenon of vibration measurement and extracted the surface roughness features of the signal envelope. Meanwhile, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was also performed to decompose the complex vibration signal into intrinsic mode functions of different frequency ranges. Then, the features contained in the processed vibration signal were extracted by statistics and one-dimensional convolutional neural network (1D-CNN), respectively. The extracted features were fused via a hybrid feature selection method based on Pearson correlation coefficient and random forest. Finally, the highly correlated features were fed into the spatiotemporal adaptive support vector regression and bi-directional gated recurrent unit for surface roughness ensemble prediction. In this paper, surface roughness prediction experiments were conducted on the open-source dataset S45C and GAMHE 5.0. In comparison with the results of the latest methods, the proposed model has the highest prediction accuracy on both datasets, and the mean absolute percentage error on the test set is reduced by 5.4395% on average compared to the best comparison method. Moreover, ablation experiments were conducted to verify the effectiveness of the proposed model and its components. [ABSTRACT FROM AUTHOR]

Consistent part geometry and minimal part dimensional variations are among the greatest challenges in deep drawing operations. In this regard, spring-back is a phenomenon created when the residual energy generated during this forming process causes some changes from desired part dimensions when released from the tooling system. There are many parameters affecting spring-back which can generally be classified as design parameters, material properties, and process variables. This paper reports on experimental results obtained from a test setup and their comparison with simulation studies of these processes. The results are part of a broader comprehensive analysis of deep drawing and study of possible real-time control of the process, to minimize final part dimensional variations. It reports on experimental and simulation results of the data and deals with identifying the relative importance of process parameters and their contribution to spring-back. In this regard, an experimental setup is designed and developed for utilization in this study. Design of experiment (DoE) is used as a tool to analyze the data, and those results are reported in this paper. [ABSTRACT FROM AUTHOR]

During the curved surface lapping processing by robot, the surface quality of the processed workpieces cannot be fully guaranteed because of the uncontrollability of lapping contact force. Therefore, the accurate control of the contact force between the tool and the workpiece is important to improve the processing quality. This paper proposes an impedance control strategy based on an optimized environmental model to address the serious constraints brought by the uncertainty of the environmental model in impedance control on the compensation accuracy of contact force and constructed a contact environmental model of elastic abrasives based on neural network algorithm, which improved the compensation accuracy of contact force through iterative iteration and prediction between the environmental model and the impedance model. Constant force control is achieved through accuracy compensation, which ultimately leads to an improvement in the surface quality of the workpiece. Through comparison experiments, it is found that compared with the unoptimized method, the force control tracking error is reduced by 60.9%, the contact force variance is reduced by 25.7%, the contact force error during curved surface force control lapping process is reduced by 58.3% and the contact force variance is reduced by 52.5%, and the workpiece roughness Ra value is reduced by 37.2%. Therefore, the robotic constant force control system based on optimized environmental model can achieve a better force control effect, which can realize accurate and stable control of contact force during curved surface lapping process by robot, and has certain potential for engineering application. [ABSTRACT FROM AUTHOR]

The machined surface in the industrial cutting environment is often covered by some interference factors, such as chips or coolant residuals, which make the existing methods have large detection errors. Therefore, the interference factors in the cutting environment seriously affect the sustainability detection and evaluation of the processed surface quality. This paper discusses the relationship between interference factors and roughness to study the sustainability detection of roughness. Firstly, a multidimensional feature parameter matrix with strong correlation with surface roughness is extracted and constructed based on the gray level co-occurrence matrix. On this basis, the adverse effects of interference factors (chips) on image feature parameters are quantitatively analyzed. According to the relationship between chip area and error change rate of feature parameters, an error correction model is constructed to optimize the feature parameters that change due to interference factors. The error correction model greatly reduces the negative influence of chip interference. This is the core of this paper. Further, the BP neural network model and support vector machine (SVM) model are used to predict the surface roughness with the optimized multi-dimensional feature parameters matrix as input, respectively. The above process realizes the sustainable detection of machined surface roughness. At the same time, in order to facilitate industrial applications, this paper uses LabVIEW software and MATLAB software to package the above research into a software system. Finally, the practicability and effectiveness of the sustainable detection research are verified by practical application in the industrial scene. This study promotes the sustainable development between product safety and environmental impact in the industrial manufacturing process. [ABSTRACT FROM AUTHOR]

Ultrasonic cutting technology is widely used in the processing of Nomex honeycomb composites, but the identification of wear state and life prediction of ultrasonic straight-edge cutter are one of the problems to be solved urgently. In this paper, based on the tool wear mechanism, the multi-dimensional parameters of cutting force and cutting sound signal are extracted through experiments, the tool wear characteristics are established, and the tool wear stages are divided based on BP neural network and K-nearest neighbor cluster analysis, we established a tool wear recognition model with an accuracy rate of 92.3%. Further, a remaining life prediction model is established based on the HMM model, which can accurately predict the service life of the tool. The research on the wear state and remaining life of ultrasonic tools in this paper provides theoretical significance for the diagnosis of acoustic system faults and has important engineering value for reducing enterprise economic expenses and improving parts processing quality. [ABSTRACT FROM AUTHOR]

In this paper, a node detection method is proposed to detect the damaged parts of armor automatically. The traditional detection of armor damage is judged by experience first, and then the parts are decomposed and confirmed, which makes the problem solving time long and efficiency low. In this paper, the in-mold electronic decoration technology is used on the surface of armor, and the node displacement changes after the surface film is damaged are used to achieve the purpose of damage detection. By using the wavelet analysis, the singularity of the wavelet function can be found and the damaged part can be determined. The Lipschitz index can be used to judge the singularity of the wavelet function to detect the local damage on the armor surface. Finally, the changes of Lipschitz index of the signal with different damage degrees on the helmet deck were simulated. In terms of influencing factors of optimization process, moldex3D was used to simulate and analyze the damaged parts of armor—different injection molding parameter schemes were set to optimize the armor film. In the first stage, the displacement change at the damaged location is detected by the probe node through simulation. In the second stage, the armor was simulated by setting appropriate process parameters such as melt temperature, filling time, filling pressure, and filling time. In the third stage, the wavelet analysis and a Lipschitz index were used to detect the location of damaged nodes. In the fourth stage, the change curve of wavelet analysis is verified by the analysis experiment. Through the experimental verification, it can be seen that the position displacement of the damaged armor changes and the singularity is generated on the wavelet function to achieve the purpose of damage detection. The influencing factors of the film injection molding are temperature and pressure. Through the simulation analysis, we optimize the injection molding parameters of the film, thus improving the damage resistance of the armor. Finally, the optimal parameters of vulnerable parts are obtained and the optimization scheme is determined. [ABSTRACT FROM AUTHOR]

Fiber-reinforced polymers are highly demanding composites in aerospace and automotive areas because of their excellent mechanical properties such as stiffness and strength-to-weight ratio. The drilling remains the major machining operation applied to composites to provide high-quality holes for joints between parts. Due to composites plied structure, the drilling is accompanied with unusual form metal cutting defects such as delamination and uncut fibers around the drilled hole. Considering that the tool life of some drills reaches over thousands of holes, the research of composites machinability becomes difficult due to demanded labor intensity in manual inspection of the hole quality. Therefore, this paper develops the research solution for automatic analysis of the hole quality in drilled fiber-reinforced materials. The paper proposes a complex of solutions aimed to speed up the analysis of the hole quality when composites drilling. The solution consists of the developed vacuum table, robot arm with high-speed camera, developed top and bottom lightning systems, and Image Processing algorithms for defect detection from captured images. The paper results show how the developed solution can be used for high routine research activities. The output data, including tested 72 cutting data parameters and full-size tool life test, allowed identifying the operational window for high-speed steel drills and the range of tool life, where drill ensures a certain hole quality. The paper shows the efficiency of the developed research solution can reach 5 s per hole including drilling and full cycle of measurements having measurement error of 1–3%. [ABSTRACT FROM AUTHOR]

This paper presents a conceptual product design method that considers the carbon emission factors of mechanical and electrical products. This method aims to consider low carbon issues prior to product design. Based on a comprehensive analysis of conventional product design procedures, the study extracts design information related to greenhouse gas (GHG) emissions, itemizes and quantifies the information, and ultimately uncovers the relationship among carbon emissions, function, and economic factors of newly designed products. This study explains the proposed concepts of carbon efficiency (CE) and carbon factor and combines them with the product design process to present a low-carbon design process of mechanical and electrical products. Furthermore, this paper presents a low-carbon design method of mechanical and electrical products, drawing on the product family design idea, with GHG efficiency and cost constraint. In particular, GHG efficiency pays attention to product function realization efficiency and economic factors. The potential value of the method is demonstrated in a case study of a tomato picking robot. [ABSTRACT FROM AUTHOR]

The research undertaken for this paper was inspired by the results of theoretical analyzes and strong experimental evidence confirming the possibility to induce the phenomenon of viscous flow (typical for liquids) in the process of low-temperature KOBO extrusion of metals. Thus, despite the constant solid state of the metal, the KOBO plastic deformation process requires much lower stress (low flow resistance) in comparison with conventional extrusion. On the other hand, the KOBO method is particularly attractive due to the generation of high strength properties in the product/extrudate favorable for low (room) temperature deformation as well as superplastic features at higher temperature ranges. This paper presents the results of the studies on the efficiency of low-temperature KOBO extrusion of hardly-deformable materials in massive form (Mg4Li, AK11, Cu6.5P) and fragmented fractions (2014, 7075, AZ91, titanium grade 2). Particular attention was paid to the structure and mechanical properties of the obtained extrudates, which were rated as exceedingly favorable. As such, the paper provides new technological arguments for the application of the KOBO method in plastic deformation of materials as it guarantees their functional properties. [ABSTRACT FROM AUTHOR]

Precision complex surfaces components are in high demand for optical imaging, high-power lasers, and medical implants. Magnetorheological finishing (MRF) is widely used in ultra-precision machining of complex surfaces components due to its advantages of low processing cost, high precision, stable removal function, no surface damage, and the ability to achieve nano-scale surface roughness and micro-scale surface shape accuracy. However, the removal efficiency of MRF is still low, the material removal mechanism is not completely clear, and the properties of magnetorheological fluid (MR fluid) need to be improved, so its application in industrial production is limited. In order to further promote the development of MRF and break through the difficulties of current MRF, it is necessary to review and summarize the MRF technology. Recent studies progress on MRF need to be more comprehensive. It is not comprehensive to introduce only several different removal theories and the appearance of compound MRF. Research progress like MR fluid, MRF tools or other also should be mentioned in a short sentence. This paper gives a detailed literature review on MRF for complex surfaces. Firstly, the principle of MRF is introduced. The finishing tools are classified based on shape and the workpieces suitable for each tool are analyzed. Some new compound MRF techniques with high machining efficiency are introduced. Then, the researches on MRF influence function and force were reviewed, and the researches on three factors affecting MRF machining performance, including process parameters, MR fluid, and magnetic pole arrangement, were reviewed. Finally, the key works of MRF technology in the future are prospected: material removal theory, preparation of high performance MR fluid, and development of novel composite MRF based on interdisciplinary and universal optimization of MRF machine tools. This paper has important reference value for researchers in MRF-related fields. [ABSTRACT FROM AUTHOR]

Nanofluids are efficient heat transfer media that have been developed over the past 27 years and have been widely used in the electronic microchannel, engine, spacecraft, nuclear, and solar energy fields. With the high demand for efficient lubricants in manufacturing, the application of nanofluids in machining has become a hot topic in academia and industry. However, in the context of the huge amount of literature in the past decade, existing review cannot be used as a technical manual for industrial applications. There are many technical difficulties in establishing a mature production system, which hinder the large-scale application of nanofluids in industrial production. The physicochemical mechanism underlying the application of nanofluids in machining remains unclear. This paper is a complete review of the process, device, and mechanism, especially the unique mechanism of nanofluid minimum quantity lubrication under different processing modes. In this paper, the preparation, fluid, thermal, and tribological properties of nanofluids are reviewed. The performance of nanofluids in machining is clarified. Typically, in friction and wear tests, the coefficient of friction of jatropha oil-based alumina nanofluids is reduced by 85% compared with dry conditions. The cutting fluid based on alumina nanoparticles improves the tool life by 177–230% in hard milling. The addition of carbon nanotube nanoparticles increases the convective heat transfer coefficient of normal saline by 145.06%. Furthermore, the innovative equipment used in the supply of nanofluids is reviewed, and the atomization mechanisms under different boundary conditions are analyzed. The technical problem of parameterized controllable supply system is solved. In addition, the performance of nanofluids in turning, milling, and grinding is discussed. The mapping relationship between the nanofluid parameters and the machining performance is clarified. The flow field distribution and lubricant wetting behavior under different tool-workpiece boundaries are investigated. Finally, the application prospects of nanofluids in machining are discussed. This review includes a report on recent progress in academia and industry as well as a roadmap for future development. [ABSTRACT FROM AUTHOR]

Metal additive manufacturing is an active field of innovation. However, for laser power bed fusion (LPBF), supports removal is a major constraint. In this technology, supports are strongly welded to the part to tightly maintain it, avoid distortion, and evacuate thermal load. Although supports are usually optimized for manual removal, machining is often necessary, which can affect post-processing productivity. This paper proposes a comprehensive methodological approach to optimize the selection of cutting parameters, cutting tools, and support structure for LPBF. The aim is to help additive manufacturers find supports that reduce machining costs in terms of time and cutting tool degradation, from among the numerous support designs available. This approach can also optimize the design of lattice structures used inside parts. Our results show that among the 11 designs tested, honeycomb and squared pattern grid supports are the most efficiently machined using the 8-teeth tangential milling of the 3 tools tested, with a good post-machined surface roughness and tools' health. The method considers low magnification optical analysis and an accelerometer sensor, which is easy to use even for small- and medium-sized enterprises. This paper also proposes and analyzes a new kind of porous support using this method. [ABSTRACT FROM AUTHOR]

Uniform equal thickness layering is widely used in 3D printing, which cannot take into account the printing quality and printing efficiency. In this paper, a new adaptive layering algorithm based on multi-objective optimization is proposed for this problem. The algorithm comprehensively considers the surface features of the model, the slope and curvature of the contour, and establishes a multi-objective optimization model with print quality, print time, and feature constraints. And the Pareto optimal solution set of multi-objective optimization is solved by the improved non-dominated sorting genetic algorithm-II (NSGA-II), and the Pareto optimal solution that meets different printing requirements is selected by the Fuzzy-based weighted membership ranking method. Through comparative experiments, the method proposed in this paper reduces the volume error rate by 40.9% and the printing time by 33.3% compared with uniform layering, which can effectively improve the printing quality and printing efficiency. In addition, compared with the existing adaptive layering algorithms, it is also an algorithm with good comprehensive performance. [ABSTRACT FROM AUTHOR]

In the process of selective assembly for precision and complex mechanical products, it is necessary to perform the selective material kitting one by one and conduct check repeatedly according to the optimal matching result to ensure quality. The traditional manual picking and checking method by Bill of Material is inefficient and laborious. Currently, there is still no consensus reached on how to better transmit information and implement the material kitting in the course of selective assembly. In this paper, we pioneer in testing the performance between mainstream methods (Pick-by-Voice, Pick-by-Light, Pick-by-AR) and traditional methods (Pick-by-Paper) in terms of task time, errors, workload, and information perception for the selective material kitting. We developed an AR (augmented reality) on the level of material individual information level and verified its applicability. Meanwhile, we enhanced the effect of material individual information expression from different visual perspectives. We found that Pick-by-AR can outperform others in terms of selective assembly, which means expanding the dimension of information expression and enhancing users' perception of information can help users quickly and accurately select parts with the same appearance but different quality characteristics fast and accurately, thus providing a viable option for material kitting in the selective assembly process. [ABSTRACT FROM AUTHOR]

With many of the world's governments committing to achieve net-zero greenhouse gas (GHG) emissions by mid-century, with well-defined milestones along the road, it is important to investigate how each sector can contribute towards achieving this global goal. The manufacturing sector, with its energy-intensive processes, large amounts of wastes, and hazardous and harmful emissions, is one of the main contributors to global GHG emissions, as well as other sustainability aspects, and, thus, it has great potential to contribute substantially to achieve net-zero objectives. This paper presents a techno-environmental-economic analysis of technologies that can play a key, enabling and leading role in the quest towards net-zero. Such technologies typically bring modest improvement in the environmental performance; however, the aim of this paper is to demonstrate how such small changes, when implemented in an industrial setting, can contribute significantly to the collective improvement in the environmental performance. In order to put the potential improvements into perspective, a real case study from the UK aerospace manufacturing sector is conducted. In the case study, metrics measuring potential improvements from the installation of a low-to-medium waste heat recovery system, and the upgrade of electric motors in the shopfloor to more energy efficient ones, are calculated through environmental and economic models. The models are then subject to a series of sensitivity analyses experiments to help understand the impact of different sources of uncertainty on the perceived GHG emissions, and economic and energy savings. The techno-environmental-economic analysis results revealed that these small changes, when implemented in an industrial setting, can indeed bring valuable improvements in the environmental performance of a manufacturing institute. Further, the sensitivity analysis experiments demonstrated how the environmental and economic performances are not adversely affected by different levels of fluctuations in key, likely to fluctuate, input parameters. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the application of machine learning (ML) techniques for predicting the spatiotemporal evolution of thermal fields during additive manufacturing (AM) processes. AM, also known as three-dimensional printing, has gained significant attention in various industries due to its potential for rapid prototyping and customized production. However, accurately predicting and controlling the thermal behavior during the AM process is crucial for ensuring the quality and reliability of the printed components. Traditional physics-based models (PBM) often face challenges in capturing AM's complex dynamics and inherent uncertainties. In recent years, ML algorithms, particularly neural networks (NNs), have shown promising results in predicting the thermal field evolution. This paper reviews the existing literature and highlights the critical methodologies and recent advancements in NN-based predictions. It explores novel perspectives by discussing the hybrid modeling approaches, including the combination of PBMs with NNs. This overview highlights the evolving landscape of predictive techniques in the context of AM and underscores the potential for enhancing accuracy and efficiency in thermal field prediction. The paper also discusses the challenges and outlines future directions for enhancing the accuracy and efficiency of thermal field prediction in AM. By synthesizing current research, this overview will guide researchers and practitioners toward leveraging NNs effectively for optimizing thermal management in AM processes. The insights presented underscore the transformative potential of NN predictions in advancing AM capabilities. [ABSTRACT FROM AUTHOR]

Ball screws are subjected to complex loads during machining processes, which affects the position accuracy of the feed shaft of machine tools. The lack of a reliable model for analyzing the deformation of the ball screw, coupled with the unclear relationship between the deformation of the ball screw and the position error of the feed shaft, makes it challenging to accurately predict and compensate for the position error. The objective of the present research is to investigate the correlation between the deformation of the ball screw and the position error of the feed shaft to improve the accuracy of the machine tool. First, this paper establishes a FEM model to explore the loading and deformation of the ball screw. Secondly, a method for predicting position errors based on the deformation of the ball screw is proposed. Finally, the ball screw-loading test device is used for the position error compensation experiment to verify the reliability of the prediction method. This paper serves as a reference for understanding the generation mechanism of position errors in machine tool feed systems. [ABSTRACT FROM AUTHOR]

The unequal pitch end milling cutter can change the feed per tooth and the cut-in and cut-out period of each cutter tooth by changing the distribution of the pitch of the end milling cutter so that the energy in the milling frequency domain is dispersed to achieve the purpose of reducing the milling vibration. Because the amplitude of vibration in the milling process is closely related to the milling force, this paper establishes the instantaneous static milling thickness model based on the real trajectory of the milling edge according to the milling process of the end milling cutter with unequal pitch and establishes the instantaneous dynamic milling thickness model based on the regeneration effect, on which the instantaneous milling force model is established. At the same time, the relationship model of milling force coefficient is established according to the average milling force method. From the relationship model of milling force coefficient, the linear relationship between average milling force and average feed per tooth is obtained. Then, the finite element simulation method is used to simulate the actual milling process of milling typical material Al7075-T6 with unequal pitch end mill, and the milling force coefficient is fitted by the simulation results. Finally, the accuracy of the milling force model and the finite element model has been validated through experiments, and the feasibility of calibrating the milling force coefficients through finite element simulation method has been demonstrated. The milling force model of unequal pitch end mill and the method of calibrating the milling force coefficient by finite element simulation proposed in this paper will provide a theoretical basis for the study of unequal pitch end mill in vibration reduction. [ABSTRACT FROM AUTHOR]

Because of the high manufacturing precision and efficiency, the segmented grinding and ball-end milling method is often used in the finish machining process of the variable screw rotor instead of form grinding. To study the process of the rotor ball-end milling and improve the precision of the machining process simulation, the accurate tool-workpiece surface contacting model and cutting force model must be established first. In this paper, the normal cutting depth iteration algorithm was proposed to study the time-varying mapping relationship between the cutting edge trajectory and the workpiece. And the normal cutting depth was used instead of the cut-in angle, cut-out angle, and undeformed chip thickness to establish the cutting force model. The influence of the transitional surface topography on the undeformed chip thickness in the cut-in and cut-out process was calculated by this method. Then, the cutting force of the screw rotor cut-in process could be simulated precisely in the finish cutting process. In this paper, the normal manufacturing error of the rotor surface and cutting force of the screw rotor were measured, and the results showed that the experimental data and the simulation data were within 15%, which proved that the method could accurately simulate the cutting force and surface topography in the ball-end milling process. [ABSTRACT FROM AUTHOR]

The 3D inspection process based on the use of optical sensors is more widely used for industrial applications, due to their great ability to scan the entire surface in a short time. Laser-plane sensors mounted on coordinate measuring machines (CMMs) are commonly used for high-precision inspection. However, measurement quality is affected by the sensor path, defined primarily by the sensor orientation and position. Executing the scan path on the machine is time-consuming, and potential collision problems can occur. In this context, the aim of this paper is to develop a 3D scan simulation tool to evaluate a given scan path in terms of visibility and quality. Scanning quality is first investigated and evaluated experimentally depending on the configurations of the laser-plane sensor relative to the part geometry. The developed simulator is therefore based on the qualification results. The principle of the simulator is formulated by functions expressing visibility and quality conditions relative to scanning distance and angle. The path simulator is set up by visualizing the two conditions on facets of the part surface, and the quality is then estimated through the corresponding function. A case study is presented to illustrate the proposed simulation tool for scanning a test part defined by its CAD model with a given sensor path. The results are compared to real scans obtained from the sensor. This study demonstrates the effectiveness of the proposed simulation tool for predicting and estimating measurement quality. The outcome of the presented research in this paper has to be used as a predictive tool to evaluate scan paths in order to scan industrial parts with the required quality. Its integration into CMMs enables accurate anticipation of scanning results, thereby enhancing operational efficiency. [ABSTRACT FROM AUTHOR]

Due to their high hardness at elevated temperatures and chemical stability, alumina-based ceramic cutting tools have been successfully applied in the machining of ferrous materials, especially quenched and tempered steels. Their low fracture toughness, however, makes it difficult to finish them by grinding, as cracks and defects can occur. In addition, its application in machining processes demands appropriate edge preparation to avoid tool failure. Within this context, the paper aims to demonstrate the applicability of an experimental Al2O3 + MgO ceramic cutting insert in hard turning. For this, grinding tests were performed to determine the most appropriate conditions to improve its surface and edge quality. The effects of cutting and feed speeds, grinding wheel bonding material, and abrasive grain size were investigated. Moreover, different cutting-edge geometries were prepared in this tool material and tested in turning a hardened AISI 4140 steel (50 HRC) to determine their performance regarding tool life and workpiece roughness. The results demonstrated that a finer grinding wheel (D15) combined with high cutting (60 m/s) and feed (10 mm/min) speeds leads to the lowest surface roughness and edge chipping. In addition, both symmetric (Κ = 1) and asymmetric (Κ = 2) edge roundings led to higher tool lives in hard turning in comparison to a single chamfered tool, as they contributed to better load distribution in the cutting region. The paper provides information for the manufacture and application of alumina-based ceramic cutting inserts in the metalworking industry, as it disseminates good manufacturing practices and recommendations for appropriate cutting-edge geometry. [ABSTRACT FROM AUTHOR]