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Showing papers in "The International Journal of Advanced Manufacturing Technology in 2019"


Journal ArticleDOI
TL;DR: The conducted review has identified the significant limitations and gaps in the existing literature and has highlighted the areas that need further research in the design, optimization, characteristics, and applications, and the AM of the cellular structures.
Abstract: Cellular structures are made up of an interconnected network of plates, struts, or small unit cells and acquire many unique benefits such as, high strength-to-weight ratio, excellent energy absorption, and minimizing material requirements. When compared with the complicated conventional processes, additive manufacturing (AM) technology is capable of fabricating geometries in almost all types of shapes, even with the small cellular structures inside, by adding material layer-by-layer directly from the digital data file. All major industries have been exploiting the benefits of cellular structures due to their prevalence over a wide range of research fields. To date, there are a few state-of-the-art reviews compiled focusing on a specific area of lattice structures, but many aspects still need to be reviewed. Therefore, this paper aims to provide a comprehensive review of the various lattice morphologies, design, and the AM of the cellular structures. Furthermore, the superior properties of the additively fabricated structure, as well as the applications and challenges, are presented. The conducted review has identified the significant limitations and gaps in the existing literature and has highlighted the areas that need further research in the design, optimization, characteristics, and applications, and the AM of the cellular structures. This review would provide a more precise understanding and the state-of-the-art of AM with the cellular structures for engineers and researchers in both academia and industrial applications.

246 citations


Journal ArticleDOI
TL;DR: This paper is intended to sum up the latest research results and achievements made in recent years in the interface bonding property, mechanical properties, and shape precision promotion of FDM 3D-printed PLA parts as well as the functional expansion of the PLA parts based on vast domestic and overseas literature.
Abstract: Different from other 3D printing techniques such as selective laser sintering (SLS), stereolithography (SLA), three-dimensional printing (3DP), and laminated object manufacturing (LOM), the fused deposition modeling (FDM) technology is widely used in aerospace, automobile making, bio-medicals, smart home, stationery and training aids, and creative gifts for its easy use, simple operation, and low cost. The polylactic acid (PLA) is a material most extensively applied in FDM technology for its low melting point, non-poison, non-irritation, and sound biocompatibility. The FDM 3D-printed PLA parts are a research hotspot in the 3D printing field. This paper is intended to sum up the latest research results and achievements made in recent years in the interface bonding property, mechanical properties, and shape precision promotion of FDM 3D-printed PLA parts as well as the functional expansion of the PLA parts based on vast domestic and overseas literature. The literature research collection focuses on the following two aspects: one is the macroscopic technical research on the optimal settings of key technological parameters; the other one is the PLA modification research on improvement of cross-linking state and crystallinity of PLA molecular chains, carbon reinforced phase modification of PLA, and PLA functional compound modification. The researches in the two aspects are of importance in improving whole properties, enhancing functional applications, and expanding and enriching the applications of FDM 3D-printed PLA parts. This paper is expected to give some helps and references to the researchers who are specializing in the 3D printing field.

231 citations


Journal ArticleDOI
TL;DR: An overview of the underlying principles of AJP are summarized, applications of the technology are reviewed, and where gains may be realised are hypothesised through this assistive manufacturing process.
Abstract: Aerosol Jet Printing (AJP) is an emerging contactless direct write approach aimed at the production of fine features on a wide range of substrates. Originally developed for the manufacture of electronic circuitry, the technology has been explored for a range of applications, including, active and passive electronic components, actuators, sensors, as well as a variety of selective chemical and biological responses. Freeform deposition, coupled with a relatively large stand-off distance, is enabling researchers to produce devices with increased geometric complexity compared to conventional manufacturing or more commonly used direct write approaches. Wide material compatibility, high resolution and independence of orientation have provided novelty in a number of applications when AJP is conducted as a digitally driven approach for integrated manufacture. This overview of the technology will summarise the underlying principles of AJP, review applications of the technology and discuss the hurdles to more widespread industry adoption. Finally, this paper will hypothesise where gains may be realised through this assistive manufacturing process.

219 citations


Journal ArticleDOI
TL;DR: In this article, the authors provide an up-to-date review of robotic machining research works, a critical analysis of publications that publish the research works and an understanding of the future directions in the field.
Abstract: For the past three decades, robotic machining has attracted a large amount of research interest owning to the benefit of cost efficiency, high flexibility and multi-functionality of industrial robot. Covering articles published on the subjects of robotic machining in the past 30 years or so; this paper aims to provide an up-to-date review of robotic machining research works, a critical analysis of publications that publish the research works, and an understanding of the future directions in the field. The research works are organised into two operation categories, low material removal rate (MRR) and high MRR, according their machining properties, and the research topics are reviewed and highlighted separately. Then, a set of statistical analysis is carried out in terms of published years and countries. Towards an applicable robotic machining, the future trends and key research points are identified at the end of this paper.

181 citations


Journal ArticleDOI
TL;DR: In this article, an attempt has been made to evaluate the effectiveness of two cooling and lubrication techniques namely cryogenic cooling and hybrid nanoadditive-based minimum quantity lubrication (MQL).
Abstract: Owing to superior physio-chemical characteristics, titanium alloys are widely adopted in numerous fields such as medical, aerospace, and military applications. However, titanium alloys have poor machinability due to its low thermal conductivity which results in high temperature during machining. Numerous lubrication and cooling techniques have already been employed to reduce the harmful environmental footprints and temperature elevation and to improve the machining of titanium alloys. In this current work, an attempt has been made to evaluate the effectiveness of two cooling and lubrication techniques namely cryogenic cooling and hybrid nanoadditive–based minimum quantity lubrication (MQL). The key objective of this experimental research is to compare the influence of cryogenic CO2 and hybrid nanofluid–based MQL techniques for turning Ti–6Al–4V. The used hybrid nanofluid is alumina (Al2O3) with multi-walled carbon nanotubes (MWCNTs) dispersed in vegetable oil. Taguchi-based L9 orthogonal-array was used for the design of the experiment. The design variables were cutting speed, feed rate, and cooling technique. Results showed that the hybrid nanoadditives reduced the average surface roughness by 8.72%, cutting force by 11.8%, and increased the tool life by 23% in comparison with the cryogenic cooling. Nevertheless, the cryogenic technique showed a reduction of 11.2% in cutting temperature compared to the MQL-hybrid nanofluids at low and high levels of cutting speed and feed rate. In this regard, a milestone has been achieved by implementing two different sustainable cooling/lubrication techniques.

170 citations


Journal ArticleDOI
TL;DR: In this article, three major process parameters such as layer height, raster angle, and infill density have been considered to study their effects on mechanical properties of acrylonitrile butadiene styrene (ABS) as this material is widely used industrial thermoplastic in FDM technology.
Abstract: Fused deposition modeling (FDM) technology works with specialized 3D printers and production-grade thermoplastics to build robust, durable, and dimensionally stable parts with the best accuracy and repeatability of any other available 3D printing technology. FDM is one of the highly used additive manufacturing technology due to its ability to manufacture very complex geometries. However, the critical problems with this technology have been to balance the ability to produce esthetically appealing products with functionality and properties at the lowest cost possible. In this study, three major process parameters such as layer height, raster angle, and infill density have been considered to study their effects on mechanical properties of acrylonitrile butadiene styrene (ABS) as this material is widely used industrial thermoplastic in FDM technology. The test results show a clear demonstration of the considered factors over the mechanical variables measured. Response surface methodology is used for the validation of the experimental data and the future prediction of the test results. It was found that the optimum parameters for 3D printing using ABS are 80% infill percentage, 0.5 mm layer thickness, and 65° raster angle. The achieved experimental ultimate tensile strength, elastic modulus, yield strength, fracture strain, and toughness (energy absorption) are 31.57 MPa, 774.50 MPa, 19.95 MPa, 0.094 mm/mm, and 2.28 Jm−3, respectively. Mathematical equation has been developed using surface response methodology which can be used to predict the ABS tensile properties numerically and also to predict the optimum parameter for ultimate properties.

170 citations


Journal ArticleDOI
TL;DR: This study covers the majority of relevant literature from 2008 to 2018 dealing with machine learning and optimization approaches for product quality or process improvement in the manufacturing industry and shows that there is hardly any correlation between the used data, the amount ofData, the machine learning algorithms, the used optimizers, and the respective problem from the production.
Abstract: Due to the advances in the digitalization process of the manufacturing industry and the resulting available data, there is tremendous progress and large interest in integrating machine learning and optimization methods on the shop floor in order to improve production processes. Additionally, a shortage of resources leads to increasing acceptance of new approaches, such as machine learning to save energy, time, and resources, and avoid waste. After describing possible occurring data types in the manufacturing world, this study covers the majority of relevant literature from 2008 to 2018 dealing with machine learning and optimization approaches for product quality or process improvement in the manufacturing industry. The review shows that there is hardly any correlation between the used data, the amount of data, the machine learning algorithms, the used optimizers, and the respective problem from the production. The detailed correlations between these criteria and the recent progress made in this area as well as the issues that are still unsolved are discussed in this paper.

151 citations


Journal ArticleDOI
TL;DR: This guideline shows the physical, tribological, and heat transfer mechanisms associated with employing such cooling/lubrication approaches and their effects on different machining quality characteristics such as tool wear, surface integrity, and cutting forces.
Abstract: The cutting fluid is significant in any metal cutting operation, for cooling the cutting tool and the surface of the workpiece, by lubricating the tool-workpiece interface and removing chips from the cutting zone. Recently, many researchers have been focusing on minimum quantity lubrication (MQL) among the numerous methods existing on the application of the coolant as it reduces the usage of coolant by spurting a mixture of compressed air and cutting fluid in an improved way instead of flood cooling. The MQL method has been demonstrated to be appropriate as it fulfills the necessities of ‘green’ machining. In the current study, firstly, various lubrication methods were introduced which are used in machining processes, and then, basic machining processes used in manufacturing industries such as grinding, milling, turning, and drilling have been discussed. The comprehensive review of various nanofluids (NFs) used as lubricants by different researchers for machining process is presented. Furthermore, some cases of utilizing NFs in machining operations have been reported briefly in a table. Based on the studies, it can be concluded that utilizing NFs as coolant and lubricant lead to lower tool temperature, tool wear, higher surface quality, and less environmental dangers. However, the high cost of nanoparticles, need for devices, clustering, and sediment are still challenges for the NF applications in metalworking operations. At last, the article identifies the opportunities for using NFs as lubricants in the future. It should be stated that this work offers a clear guideline for utilizing MQL and MQL-nanofluid approaches in machining processes. This guideline shows the physical, tribological, and heat transfer mechanisms associated with employing such cooling/lubrication approaches and their effects on different machining quality characteristics such as tool wear, surface integrity, and cutting forces.

143 citations


Journal ArticleDOI
TL;DR: In this article, the authors developed prediction models of minimum chip thickness and ductile-brittle transition chip thickness (hd-b) according to the grinding mechanism, which showed that both hmin and hd-b decreased with increasing friction coefficient.
Abstract: The removal of material in the ductile regime while improving machining efficiency is currently the technical bottleneck in grinding zirconia ceramics. Prediction models of minimum chip thickness (hmin) and ductile–brittle transition chip thickness (hd–b) were developed according to grinding mechanism. Results showed that both hmin and hd–b decreased with increasing friction coefficient. Grinding experiments were carried out using the maximum undeformed chip thickness as the input parameter. Experimental results showed that the hmin value in dry grinding is 0.24 μm. Meanwhile, the hmin values under minimum quantity lubrication (MQL) and nanoparticle jet MQL (0.4, 0.8, 1.2, 1.6, and 2 vol.%) are 0.27, 0.34, 0.49, 0.65, 0.76, and 0.91 μm, respectively. Furthermore, the hd–b value in dry grinding is 0.8 μm, and the hd–b values under lubrication condition that corresponds to hmin are 1.79, 1.98, 2.15, 2.27, 2.39, and 2.59 μm, respectively. The experimental results show the same trend as that of the prediction model. The theoretical calculation is basically consistent with the measured values, with model errors of 7.9% and 6.3%, thereby verifying the accuracy of the chip thickness models.

132 citations


Journal ArticleDOI
TL;DR: An autonomous SMSs model driven by dynamic demand and key performance indicators is proposed and the reference can be provided for the transformation of more manufacturing enterprises from the traditional to the intellectualized ones.
Abstract: With the development and application of advanced technologies such as Cyber Physical System, Internet of Things, Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., more manufacturing enterprises are transforming to intelligent enterprises. Smart manufacturing systems (SMSs) have become the focus of attention of some countries and manufacturing enterprises. At present, there are some applications of SMSs in different industrial fields. However, there is still a lack of a unified definition of SMSs and a unified analysis of requirements. In order to have a comprehensive understanding of SMSs, this paper summarized the evolution, definition, objectives, functional requirements, business requirements, technical requirements, and components of SMSs. At the same time, it points out the current development status and level. Based on above, an autonomous SMSs model driven by dynamic demand and key performance indicators is proposed. Through the review of this paper, the reference can be provided for the transformation of more manufacturing enterprises from the traditional to the intellectualized ones.

132 citations


Journal ArticleDOI
TL;DR: In this article, the effect of printing layer thickness on technological properties of 3D-printed specimens fabricated from wood flour/PLA filaments having a diameter of 1.75mm was investigated.
Abstract: Effect of printing layer thickness on technological properties of 3D-printed specimens fabricated from wood flour/PLA filaments having a diameter of 1.75 mm was investigated. For this aim, four different printing layers, 0.05 mm, 0.1 mm, 0.2 mm, and 0.3 mm, were used in the production of the 3D-printed specimens. The water absorption of the specimens (28 days immersion in water) increased with increasing printing layer thickness while the thickness swelling decreased. The tensile and bending properties of the specimens significantly improved with decreasing printing layer thickness. The increase in the layer thickness caused bigger gaps, which increased the porosity in the cross section of the specimen. Higher porosity resulted in lower mechanical properties.

Journal ArticleDOI
TL;DR: In this article, the impact of the interrelation between the adoption of Industry 4.0 technologies and the implementation of lean production practices on the improvement level of European manufacturers' operational performance is examined.
Abstract: This study aims at examining the impact of the interrelation between the adoption of Industry 4.0 technologies and the implementation of lean production (LP) practices on the improvement level of European manufacturers’ operational performance. To achieve that, we conducted a survey with 108 European manufacturers that have been implementing LP and initiated their Industry 4.0 adoption. The collected data was analyzed through multivariate techniques, allowing to identify the effect of this relationship according to different contextual factors deemed as influential by previous literature, such as company size, LP implementation experience, type of ownership, and business operating model. Results underpin the idea of a wide applicability of both approaches, indicating that higher adoption levels of Industry 4.0 may be easier to achieve when LP practices are extensively implemented in the company. In opposition, when processes are not robustly designed and continuous improvement practices are not established, companies’ readiness for adopting novel technologies may be lower. By comprehending that Industry 4.0 technologies are highly related to LP practices, disregarding the context, managers from EU manufacturers can address the implementation of both approaches in a more assertive way.

Journal ArticleDOI
TL;DR: In this article, a detailed review of the progress of drilling of carbon fiber reinforced polymers with special attention given to carbon fiber-reinforced polymers is presented, where the role of drilling parameters and composite properties on the drilling-induced damage in machined holes is discussed.
Abstract: Drilling is considered as one of the most challenging problems in aerospace structures where stringent tolerances are required for fasteners such as rivets and bolts to join the mating parts for final assembly. Fiber-reinforced polymers are widely used in aeronautical applications due to their superior properties. One of the major challenges in machining such polymers is the poor drilled-hole quality which reduces the strength of the composite and leads to part rejection at the assembly stage. In addition, rapid tool wear due to the abrasive nature of composites requires frequent tool change which results in high tooling and machining costs. This review intended to give in-depth details on the progress of drilling of fiber-reinforced polymers with special attention given to carbon fiber–reinforced polymers. The objective is to give a comprehensive understanding of the role of drilling parameters and composite properties on the drilling-induced damage in machined holes. Additionally, the review examines the drilling process parameters and its optimization techniques, and the effects of dust particles on human health during the machining process. This review will provide scientific and industrial communities with advantages and disadvantages through better drilled-hole quality inspection.

Journal ArticleDOI
TL;DR: A novel big data approach for tool wear classification based on signal imaging and deep learning is presented, able to work with the raw data directly, avoiding the use of statistical pre-processing or filter methods.
Abstract: Tool condition monitoring (TCM) has become essential to achieve high-quality machining as well as cost-effective production. Identification of the cutting tool state during machining before it reaches its failure stage is critical. This paper presents a novel big data approach for tool wear classification based on signal imaging and deep learning. By combining these two techniques, the approach is able to work with the raw data directly, avoiding the use of statistical pre-processing or filter methods. This aspect is fundamental when dealing with large amounts of data that hold complex evolving features. The imaging process serves as an encoding procedure of the sensor data, meaning that the original time series can be re-created from the image without loss of information. By using an off-the-shelf deep learning implementation, the manual selection of features is avoided, thus making this novel approach more general and suitable when dealing with large datasets. The experimental results have revealed that deep learning is able to identify intrinsic features of sensory raw data, achieving in some cases a classification accuracy above 90%.

Journal ArticleDOI
TL;DR: In this article, the authors studied the mechanical properties tensile strength, flexural strength, and impact energy of 3D printed parts manufactured with FDM technology and PLA-graphene raw material by varying the infill and layer thickness parameters using a statistical technique CCD.
Abstract: Fused deposition modelling is an additive manufacturing technology that is widely employed to produce good quality products with complex geometries at a low cost and with efficient manufacturing and delivery logistics. The mechanical properties can be enhanced by studying the numerous FDM parameters and by using new materials. In this work, was studied the mechanical properties tensile strength, flexural strength, and impact energy of 3D printed parts manufactured with FDM technology and PLA-graphene raw material by varying the infill and layer thickness parameters using a statistical technique CCD—central composite design. Due to the layered production process, 3D printed parts exhibit anisotropic behaviour. In the tests, the flat orientation and honeycomb infill pattern were maintained. The results showed that the mechanical properties improve as the linear layer thickness parameter increases. The behaviour was different in each test for the linear infill parameter. The mechanical properties, tensile strength and flexural strength, increased as the infill increased, while impact energy decreased as infill increased. The relationship between mechanical properties and printing time/weight was also evaluated.

Journal ArticleDOI
TL;DR: A road map is created to understand and explore the impact of typical I4.0 new technologies on assembly systems, and points out the cases where clear performance improvement is expected due to the integration of the new technologies.
Abstract: The 4th industrial revolution (Industry 4.0, I4.0) is based upon the penetration of many new technologies to the industrial world. These technologies are posed to fundamentally change assembly lines around the world. Assembly systems transformed by I4.0 technology integration are referred to here as Assembly 4.0 (A4.0). While most I4.0 new technologies are known, and their integration into shop floors is ongoing or imminent, there is a gap between this knowledge and understanding the form and the impact of their full implementation in assembly systems. The path from the new technological abilities to improved productivity and profitability has not been well understood and has some missing parts. This paper strives to close a significant part of this gap by creating a road map to understand and explore the impact of typical I4.0 new technologies on A4.0 systems. In particular, the paper explores three impact levels: strategic, tactical, and operational. On the strategic level, we explore aspects related to the design of the product, process, and the assembly system. Additionally, the paper elaborates on likely changes in assembly design aspects, due to the flexibility and capabilities that these new technologies will bring. Strategic design also deals with planning and realizing the potential of interactions between sub-assembly lines, kitting lines, and the main assembly lines. On the tactical level, we explore the impact of policies and methodologies in planning assembly lines. Finally, on the operational level, we explore how these new capabilities may affect part routing and scheduling including cases of disruptions and machine failures. We qualitatively assess the impact on performance in terms of overall flow time and ability to handle a wide variety of end products. We point out the cases where clear performance improvement is expected due to the integration of the new technologies. We conclude by identifying research opportunities and challenges for advanced assembly systems.

Journal ArticleDOI
TL;DR: The present overview gives a new approach toward developments and recent achievements in severe plastic deformation through a review of SPD research status in the world based on the total number of publications, citations, and the contribution of the top-ranked countries.
Abstract: The present overview gives a new approach toward developments and recent achievements in severe plastic deformation. The review focuses on several subjects. First, an outline of SPD research status in the world is presented by literature analysis based on the total number of publications, citations, and the contribution of the top-ranked countries. Second, the mechanisms of grain refinement and grain growth during SPD processing are discussed by means of the latest concepts. Third, all SPD methods invented so far are classified based on a new approach. Up to now, the growing tendency of researchers to introduce new SPD techniques results in a large number of SPD methods which can be considered as new or modified techniques or a combination of previous ones. Such a reference can help to prevent the future duplication to introduce the SPD processes, which are technically similar. At the end, the practical applications of ultrafine/nanostructured materials and industrial commercialization of SPD methods are summarized.

Journal ArticleDOI
TL;DR: In this article, a wire arc additive manufacturing (WAAM) method was used to fabricate a low carbon low-alloy steel wall using a gas metal arc welding (GMAW) torch translated by six-axis robotic arm.
Abstract: The emerging technology of wire arc additive manufacturing (WAAM) has been enthusiastically embraced in recent years mainly by the welding community to fabricate various grades of structural materials. In this study, ER70S-6 low-carbon low-alloy steel wall was manufactured by WAAM method, utilizing a gas metal arc welding (GMAW) torch translated by a six-axis robotic arm, and employing advanced surface tension transfer (STT) mode. The dominant microstructure of the fabricated part contained randomly oriented fine polygonal ferrite and a low-volume fraction of lamellar pearlite as the primary micro-constituents. Additionally, a small content of bainite and acicular ferrite were also detected along the melt-pool boundaries, where the material undergoes a faster cooling rate during solidification in comparison with the center of the melt pool. Mechanical properties of the part, studied at different orientations relative to the building direction, revealed a comparable tensile strength along the deposition (horizontal) direction and the building (vertical) direction of the fabricated part (~ 400 MPa and ~ 500 MPa for the yield and ultimate tensile strengths, respectively). However, the obtained plastic tensile strain at failure along the horizontal direction was nearly three times higher than that of the vertical direction, implying some extent of anisotropy in ductility. The reduced ductility of the part along the building direction was associated with the higher density of the interpass regions and the melt-pool boundaries in the vertical direction, containing heat-affected zones with coarser grain structure, brittle martensite–austenite constituent, and possibly a higher density of discontinuities.

Journal ArticleDOI
TL;DR: In this paper, the synergistic effect of multiangle 2D ultrasonic and minimum quantity lubrication (NMQL) was investigated in zirconia ceramic grinding. And the results reveal that the adhesion and material peeling phenomenon on the workpiece surface is reduced compared with dry grinding without ultrasonic vibration.
Abstract: Nanofluid minimum quantity lubrication (NMQL) technique has many technological and economic advantages in grinding operation. NMQL can improve grinding performance in terms of cooling and lubrication and is ecofriendly because it consumes a small amount of grinding fluid. Ultrasonic machining can improve grinding performance owing to its reciprocating vibration mechanism and furrow widening. Consequently, the simultaneous utilization of these techniques is anticipated to improve the surface quality, especially for hard brittle materials. In this research, multiangle two-dimensional (2D) ultrasonic vibration is utilized in zirconia ceramic grinding. Results reveal that the adhesion and material peeling phenomenon on the workpiece surface is obviously reduced compared with dry grinding without ultrasonic vibration. The synergistic effect of multiangle 2D ultrasonic and NMQL is also studied. With increased angle, the roughness value is found to initially increase (from 45° to 90°) and then decreases (from 90° to 135°). Moreover, the lubricating effect under 90° is the poorest, with the highest Ra and RSm values of 0.703 μm and 0.106 mm, respectively; conversely, the minimum Ra value (0.585 μm) is obtained under 45°, and the lowest RSm value (0.076 mm) is obtained under 135°.

Journal ArticleDOI
TL;DR: In this article, the influence of pure cooling-lubrication (C/L) agents to reduce friction at faying surfaces can ameliorate overall machinability.
Abstract: In machining of soft alloys, the sticky nature of localized material instigated by tool-work interaction exacerbates the tribological attitude and ultimately demeans it machinability. Moreover, the endured severe plastic deformation and originated thermal state alter the metallurgical structure of machined surface and chips. Also, the used tool edges are worn/damaged. Implementation of cooling-lubrication (C/L) agents to reduce friction at faying surfaces can ameliorate overall machinability. That is why, this paper deliberately discussed the influence of pure C/L methods, i.e., such as dry cutting (DC) and nitrogen cooling (N2), as well as hybrid C/L strategies, i.e., nitrogen minimum quantity lubrication (N2MQL) and Ranque–Hilsch vortex tube (RHVT) N2MQL conditions in turning of Al 7075-T6 alloy, respectively. With respect to the variation of cutting speed and feed rate, at different C/Ls, the surface roughness, tool wear, and chips are studied by using SEM and 3D topographic analysis. The mechanism of heat transfer by the cooling methods has been discussed too. Furthermore, the new chip management model (CMM) was developed under all C/L conditions by considering the waste management aspects. It was found that the R-N2MQL has the potential to reduce the surface roughness up to 77% and the tool wear up to 118%. This significant improvement promotes sustainability in machining industry by saving resources. Moreover, the CMM showed that R-N2MQL is more attractive for cleaner manufacturing system due to a higher recyclability, remanufacturing, and lower disposal of chips.

Journal ArticleDOI
TL;DR: The current trends and challenges that FRAM is bringing to AM ecosystem are reported, including the impact of fiber orientations and fraction on the performance of parts, improving the printing parameters, and other subjects.
Abstract: In the last few years, utilizing fiber reinforced additive manufacturing (FRAM)-based components in several industries has become quite popular. Compared to conventional AM technologies, FRAM offered complementary solutions to their needs. In general, fibers have been traditionally used in many manufacturing processes for various reasons. However, using conventional methods, there are obstacles in obtaining the desired complex geometries and low setup costs. AM offers possible avoidance of these limitations. Shape complexity, infill density, and manufacturing lead times are no longer barriers. Bridging AM with fiber reinforced materials offers a vast opportunity for lightweight and strong parts. Depending on the affinity, fibers with different structures can be mixed with different matrix materials and, thus, create stronger parts with improved mechanical properties. Process parameters like raster angle, infill speed, layer thickness, and nozzle temperature also strongly impact physical properties of FRAM products and are considered carefully. FRAM-based components are used in many industries such as aerospace, motorsports, and biomedicine, where the weight, strength, and complexity of parts are critical. Hence, numerous industrial companies and research facilities are investigating the implementation and adaptation of FRAM to their requirements. Studies are generally conducted on new materials, new FRAM technologies, the effect of fiber orientations and fraction on the performance of parts, improving the printing parameters, and other subjects. This study reports the current trends and challenges that FRAM is bringing to AM ecosystem.

Journal ArticleDOI
TL;DR: In this article, failure by elastic buckling and plastic collapse of wall structures during extrusion-based 3D printing processes is studied. And the authors validate the results obtained from the parametric 3D print model with the results of dedicated FEM simulations and 3D concrete printing experiments.
Abstract: This contribution studies failure by elastic buckling and plastic collapse of wall structures during extrusion-based 3D printing processes. Results obtained from the parametric 3D printing model recently developed by Suiker (Int J Mech Sci, 137: 145–170, 2018), among which closed-form expressions useful for engineering practice, are validated against results of dedicated FEM simulations and 3D concrete printing experiments. In the comparison with the FEM simulations, various types of wall structures are considered, which are subjected to linear and exponentially decaying curing processes at different curing rates. For almost all cases considered, the critical wall buckling length computed by the parametric model turns out to be in excellent agreement with the result from the FEM simulations. Some differences may occur for the particular case of a straight wall clamped along its vertical edges and subjected to a relatively high curing rate, which can be ascribed to the approximate form of the horizontal buckling shape used in the parametric model. The buckling responses computed by the two models for a wall structure with imperfections of different wavelengths under increasing deflection correctly approaches the corresponding bifurcation buckling length. Further, under a specific change of the material properties, the parametric model and the FEM model predict a similar transition in failure mechanism, from elastic buckling to plastic collapse. The experimental validation of the parametric model is directed towards walls manufactured by 3D concrete printing, whereby the effect of the material curing rate on the failure behaviour of the wall is explored by studying walls of various widths. At a relatively low curing rate, the experimental buckling load is well described when the parametric model uses a linear curing function. However, the experimental results suggest the extension of the linear curing function with a quadratic term if the curing process under a relatively long printing time is accelerated by thermal heating of the 3D printing facility. In conclusion, the present validation study confirms that the parametric model provides a useful research and design tool for the prediction of structural failure during extrusion-based 3D printing. The model can be applied to quickly and systematically explore the influence of the individual printing process parameters on the failure response of 3D-printed walls, which can be translated to directives regarding the optimisation of material usage and printing time.

Journal ArticleDOI
TL;DR: The results show that the proposed approach could detect anomaly working condition with 99% accuracy under a completely unsupervised learning environment and offer an alternative method to leverage and integrate features for anomaly detection without empirical knowledge.
Abstract: Anomaly in mechanical systems may cause equipment to break down with serious safety, environment, and economic impact. Since many mechanical equipment usually operates under tough working environments, which makes them vulnerable to types of faults, anomaly detection for mechanical equipment usually requires considerable domain knowledge. However, a common dilemma in many practical applications is that one may not be able to obtain the empirical knowledge about anomaly or the history data is completely unlabelled, which makes conventional fault identification methods not applicable. In order to fill the gap, this paper proposes a novel deep learning–based method for anomaly detection in mechanical equipment by combining two types of deep learning architectures, stacked autoencoders (SAE) and long short-term memory (LSTM) neural networks, to identify anomaly condition in a completely unsupervised manner. The proposed method focuses on the anomaly detection through multiple features sequence when the history data is unlabelled and the empirical knowledge about anomaly is absent. An experiment for anomaly detection in rotary machinery through wavelet packet decomposition (WPD) and data-driven models demonstrates the efficiency and stability of the proposed approach. The method can be divided into two stages: SAE-based multiple features sequence representation and LSTM-based anomaly identification. During the experiment, fivefold cross-validation has been applied to validate the performance and stability of the proposed approach. The results show that the proposed approach could detect anomaly working condition with 99% accuracy under a completely unsupervised learning environment and offer an alternative method to leverage and integrate features for anomaly detection without empirical knowledge.

Journal ArticleDOI
TL;DR: In this article, the state-of-the-art readiness for Industry 4.0 concerning assembly and manufacturing systems through a literature review of the relevant papers recently published is addressed.
Abstract: The advances in Industry 4.0 provide both challenges and opportunities for digital manufacturing and assembly systems. This paper first addresses the state-of-the-art readiness for Industry 4.0 concerning assembly and manufacturing systems through a literature review of the relevant papers recently published. Then it assesses the challenges faced nowadays by assembly and manufacturing systems. Third, it focuses on the most promising future developments and evolution of such production systems as well as their digitalisation. Finally, this manuscript illustrates the content of the papers selected for this special issue. Through the study presented in this special issue, valuable contributions to both theory and application in this area have been achieved, and a useful reference for future research is given.

Journal ArticleDOI
TL;DR: In this paper, the authors evaluated the performance of different types of nanofluids in milling titanium alloy Ti-6Al-4V. The workpiece surface morphology was the best for Al2O3 and SiO2.
Abstract: The objective of this research is to experimentally evaluate the lubrication performances of different nanofluids in milling titanium alloy Ti-6Al-4V. Six types of nanofluids, namely, Al2O3, SiO2, MoS2, CNTs, SiC, and graphite, were selected. Cottonseed oil was used as the base oil. The lubrication performance was investigated in terms of milling force, surface roughness, and morphology of workpiece surface. Experimental results demonstrated that the Al2O3 nanoparticle obtained the minimal milling force (Fx = 277.5 N, Fy = 88.3 N), followed by the SiO2 nanoparticle (Fx = 283.6 N, Fy = 86.5 N). The surface roughness obtained by the Al2O3 nanofluid was the minimum (Ra = 0.594 μm), whereas it was the maximum by using minimum quantity lubrication (Ra = 1.772 μm). The surface roughness of the six nanofluids was described by the following order: Al2O3 < SiO2 < MoS2 < CNTs < graphite < SiC. The workpiece surface morphology was the best for Al2O3 and SiO2. The viscosity of the nanofluids was also analyzed. Spherical Al2O3 and SiO2 nanoparticles improved the lubrication effect of base oil mostly and were more suitable as environment-friendly additives for the base oil compared with the others.

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TL;DR: In this paper, the authors used advanced cooling lubrication, i.e., nanofluid assistance, besides dry and flood cooling, during machining and used as the basis for sustainability assessment.
Abstract: The constant pressure on the manufacturers to innovate and implement sustainable processes has triggered researching on machining with low carbon footprint, minimum energy consumption by machine tools, and improved products at the lowest cost—this is exactly done in this paper. Herein, the advanced cooling lubrication, i.e., nanofluid assistance, besides dry and flood cooling, during machining has been experimented, and used as the basis for sustainability assessment. This assessment is carried out in respect of surface quality and power consumption as well as the impact on environment, cost of machining, management of waste, and finally the safety and health issues of operators. For a better sustainability, a systematic optimization has been performed. In addition, the solution for an improved machinability has been proposed along with the statistically verified mathematical models of machining responses. Results showed that the nanofluid minimum quantity lubrication showed the most sustainable performance with a total weighted sustainability index 0.7, and it caused the minimum surface roughness and power consumption. The highest desirable (desirability = 0.9050) optimum results are the cutting speed of 116 m/min, depth of cut 0.25 mm, and feed rate of 0.06 mm/rev. Furthermore, a lower feed rate is suggested for better surface quality while for reduced power consumption the lower control factors are better.

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TL;DR: The machinability of superalloy Inconel-800 has been investigated by performing different turning tests under MQL conditions, and MQL was found to be a better cooling technique when compared to the dry and the flood cooling.
Abstract: The manufacturing of parts from nickel-based superalloy, such as Inconel-800 alloy, represents a challenging task for industrial sites. Their performances can be enhanced by using a smart cutting fluid approach considered a sustainable alternative. Further, to innovate the cooling strategy, the researchers proposed an improved strategy based on the minimum quantity lubrication (MQL). It has an advantage over flood cooling because it allows better control of its parameters (i.e., compressed air, cutting fluid). In this study, the machinability of superalloy Inconel-800 has been investigated by performing different turning tests under MQL conditions, where no previous data are available. To reduce the numerous numbers of tests, a target objective was applied. This was used in combination with the response surface methodology (RSM) while assuming the cutting force input (Fc), potential of tool wear (VBmax), surface roughness (Ra), and the length of tool–chip contact (L) as responses. Thereafter, the analysis of variance (ANOVA) strategy was embedded to detect the significance of the proposed model and to understand the influence of each process parameter. To optimize other input parameters (i.e., cutting speed of machining, feed rate, and the side cutting edge angle (cutting tool angle)), two advanced optimization algorithms were introduced (i.e., particle swarm optimization (PSO) along with the teaching learning-based optimization (TLBO) approach). Both algorithms proved to be highly effective for predicting the machining responses, with the PSO being concluded as the best amongst the two. Also, a comparison amongst the cooling methods was made, and MQL was found to be a better cooling technique when compared to the dry and the flood cooling.

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TL;DR: A novel digital twin-based approach for reusing and evaluating process knowledge with dynamical changing machining status is proposed and can promote the development and application of the smart process planning.
Abstract: With the advances in new-generation information technologies, smart process planning is becoming the focus for smart process planning with less time and lower cost. Big data-based reusing and evaluating the multi-dimensional process knowledge is widely accepted as an effective strategy for improving competitiveness of enterprises. However, there was little research on how to reuse and evaluate process knowledge with dynamical changing machining status. In this paper, we propose a novel digital twin-based approach for reusing and evaluating process knowledge. First, the digital twin-based process knowledge model which contains the geometric information and real-time process equipment status is introduced to represent the purpose and requirement of machining planning. Second, the process big data is constructed based on the three-layer and its association rules for accumulating process knowledge. Moreover, the similarity calculation algorithm of the scene model is proposed to filter the unmatched process knowledge. For accurately reusing the process knowledge, the process reusability evaluation approach of the candidate knowledge set is presented based on the real-time machining status and the calculated confidence. Finally, the diesel engine parts are applied in the developed prototype module to verify the effectiveness of the proposed method. The proposed method can promote the development and application of the smart process planning.

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TL;DR: In this paper, the impact of FDM process parameters, such as layer thickness, shells, infill pattern, and infill percentage, on the compressive strength of the case study was evaluated.
Abstract: The control of process parameters to customize a part has been a value-added ability related to additive manufacturing (AM). In this paper, parametric optimization of fused deposition modeling (FDM) process is performed using Taguchi design of experiments (DOE). Two sets of experiments were conducted on an industrial case study from the aerospace industry to assess the impact of FDM process parameters: layer thickness, shells, infill pattern, and infill percentage, on the compressive strength of the case study. A generic methodology was also proposed. Analysis of variance (ANOVA) and signal-to-noise (S/N) ratio analysis were performed to evaluate the importance of experimental error, finding the optimal combination of process parameters for maximizing the compressive strength, and assessing the robustness of the design. The paper concluded with the display of results, discussion, and conclusions drawn.

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TL;DR: A gesture-based HRI framework in which a robot assists a human co-worker delivering tools and parts, and holding objects to/for an assembly operation, and a parameterization robotic task manager (PRTM), in which the co- worker selects/validates robot options using gestures are proposed.
Abstract: The paradigm for robot usage has changed in the last few years, from a scenario in which robots work isolated to a scenario where robots collaborate with human beings, exploiting and combining the best abilities of robots and humans. The development and acceptance of collaborative robots is highly dependent on reliable and intuitive human-robot interaction (HRI) in the factory floor. This paper proposes a gesture-based HRI framework in which a robot assists a human co-worker delivering tools and parts, and holding objects to/for an assembly operation. Wearable sensors, inertial measurement units (IMUs), are used to capture the human upper body gestures. Captured data are segmented in static and dynamic blocks recurring to an unsupervised sliding window approach. Static and dynamic data blocks feed an artificial neural network (ANN) for static, dynamic, and composed gesture classification. For the HRI interface, we propose a parameterization robotic task manager (PRTM), in which according to the system speech and visual feedback, the co-worker selects/validates robot options using gestures. Experiments in an assembly operation demonstrated the efficiency of the proposed solution.