Showing papers in "The International Journal of Advanced Manufacturing Technology in 2018"
TL;DR: In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed, and three cases are given to illustrate the future applications of digital twin in three phases of a product respectively.
Abstract: Nowadays, along with the application of new-generation information technologies in industry and manufacturing, the big data-driven manufacturing era is coming. However, although various big data in the entire product lifecycle, including product design, manufacturing, and service, can be obtained, it can be found that the current research on product lifecycle data mainly focuses on physical products rather than virtual models. Besides, due to the lack of convergence between product physical and virtual space, the data in product lifecycle is isolated, fragmented, and stagnant, which is useless for manufacturing enterprises. These problems lead to low level of efficiency, intelligence, sustainability in product design, manufacturing, and service phases. However, physical product data, virtual product data, and connected data that tie physical and virtual product are needed to support product design, manufacturing, and service. Therefore, how to generate and use converged cyber-physical data to better serve product lifecycle, so as to drive product design, manufacturing, and service to be more efficient, smart, and sustainable, is emphasized and investigated based on our previous study on big data in product lifecycle management. In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed. The detailed application methods and frameworks of digital twin-driven product design, manufacturing, and service are investigated. Furthermore, three cases are given to illustrate the future applications of digital twin in the three phases of a product respectively.
1,571 citations
TL;DR: A framework of digital twin-based smart production management and control approach for complex product assembly shop-floors for cyber-physical systems (CPS) and how to apply the proposed approach to reality is proposed.
Abstract: Digital twin technology is considered as a key technology to realize cyber-physical systems (CPS) However, due to the complexity of building a digital equivalent in virtual space to its physical counterpart, very little progress has been achieved in digital twin application, especially in the complex product assembly shop-floor In this paper, we propose a framework of digital twin-based smart production management and control approach for complex product assembly shop-floors Four core techniques embodied in the framework are illustrated in detail as follows: (1) real-time acquisition, organization, and management of the physical assembly shop-floor data, (2) construction of the assembly shop-floor digital twin, (3) digital twin and big data-driven prediction of the assembly shop-floor, and (4) digital twin-based assembly shop-floor production management and control service To elaborate how to apply the proposed approach to reality, we present detailed implementation process of the proposed digital twin-based smart production management and control approach in a satellite assembly shop-floor scenario Meanwhile, the future work to completely fulfill digital twin-based smart production management and control concept for complex product assembly shop-floors are discussed
389 citations
TL;DR: The elaborately designed deep convolutional neural networks proposed by this paper can automatically extract powerful features with less prior knowledge about the images for defect detection, while at the same time is robust to noise.
Abstract: The fast and robust automated quality visual inspection has received increasing attention in the product quality control for production efficiency. To effectively detect defects in products, many methods focus on the hand-crafted optical features. However, these methods tend to only work well under specified conditions and have many requirements for the input. So the work in this paper targets on building a deep model to solve this problem. The elaborately designed deep convolutional neural networks (CNN) proposed by us can automatically extract powerful features with less prior knowledge about the images for defect detection, while at the same time is robust to noise. We experimentally evaluate this CNN model on a benchmark dataset and achieve a fast detection result with a high accuracy, surpassing the state-of-the-art methods.
319 citations
TL;DR: A review of the state-of-the-art methods employed for conducting TCM in milling processes includes three key components: sensors, feature extraction, and monitoring models for the categorization of cutting tool states in the decision-making process.
Abstract: Accurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. However, while considerable research has been conducted in industrial and academic settings, the complexity of milling processes continues to complicate the implementation of TCM. This paper presents a review of the state-of-the-art methods employed for conducting TCM in milling processes. The review includes three key components: (1) sensors, (2) feature extraction, and (3) monitoring models for the categorization of cutting tool states in the decision-making process. In addition, the primary strengths and weaknesses of current practices are presented for these three components. Finally, this paper concludes with a list of recommendations for future research.
214 citations
TL;DR: In this article, a review of the literature on facility layout problem is made by referring to numerous papers about FLP, mainly motivated by the current and prospective trends of research on such points as layout evolution, workshop characteristics, problem formulation, and solution methodologies.
Abstract: Facility layout problem (FLP) is defined as the placement of facilities in a plant area, with the aim of determining the most effective arrangement in accordance with some criteria or objectives under certain constraints, such as shape, size, orientation, and pick-up/drop-off point of the facilities. It has been over six decades since Koopmans and Beckmann published their seminal paper on modeling the FLP. Since then, there have been improvements to these researchers’ original quadratic assignment problem. However, research on many aspects of the FLP is still in its initial stage; hence, the issue is an interesting field to work on. Here, a review of literature is made by referring to numerous papers about FLPs. The study is mainly motivated by the current and prospective trends of research on such points as layout evolution, workshop characteristics, problem formulation, and solution methodologies. It points to gaps in the literature and suggests promising directions for future research on FLP.
170 citations
TL;DR: In this article, the authors identify, analyzes, and classifies the common defects and their contributing parameters reported in the literature, and define the relationship between the two and classify them under an umbrella of manufacturing features for monitoring and control purposes.
Abstract: The powder bed fusion additive manufacturing process enables fabrication of metal parts with complex geometry and elaborate internal features, the simplification of the assembly process, and the reduction of development time; however, its tremendous potential for widespread application in industry is hampered by the lack of consistent quality. This limits its ability as a viable manufacturing process particularly in the aerospace and medical industries where high quality and repeatability are critical. A variety of defects, which may be initiated during powder bed fusion additive manufacturing, compromise the repeatability, precision, and resulting mechanical properties of the final part. One approach that has been more recently proposed to try to control the process by detecting, avoiding, and/or eliminating defects is online monitoring. In order to support the design and implementation of effective monitoring and control strategies, this paper identifies, analyzes, and classifies the common defects and their contributing parameters reported in the literature, and defines the relationship between the two. Next, both defects and contributing parameters are categorized under an umbrella of manufacturing features for monitoring and control purposes. The quintuple set of manufacturing features presented here is meant to be employed for online monitoring and control in order to ultimately achieve a defect-free part. This categorization is established based on three criteria: (1) covering all the defects generated during the process, (2) including the essential contributing parameters for the majority of defects, and (3) the defects need to be detectable by existing monitoring approaches as well as controllable through standard process parameters. Finally, the monitoring of signatures instead of actual defects is presented as an alternative approach to controlling the process “indirectly.”
160 citations
TL;DR: In this article, a Gaussian process-based surrogate model of the laser powder-bed-fusion (L-PBF) process is used to predict melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination.
Abstract: Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood Successful processing for one material, might not necessarily apply to a different material This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination The predictions are then mapped onto a power versus scan speed diagram delimiting the conduction from the keyhole melting controlled regimes This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder
158 citations
TL;DR: In this article, the effects of two types of nano-cutting fluids on tool performance and chip morphology during turning of Inconel 718 were investigated, and it was found that MWCNT nano-fluid has shown better performance than Al2O3 nanofluid.
Abstract: Flood cooling is a typical cooling strategy used in industry to dissipate the high temperature generated during machining of Inconel 718. The use of flood coolant has risen environmental and health concerns which call for different alternatives. Minimum quaintly lubricant (MQL) has been successfully introduced as an acceptable coolant strategy; however, its potential to dissipate heat is much lower than the one achieved using flood coolant. MQL-nano-cutting fluid is one of the suggested techniques to further improve the performance of MQL particularly when machining difficult-to-cut materials. The main objective of this study is to investigate the effects of two types of nano-cutting fluids on tool performance and chip morphology during turning of Inconel 718. Multi-walled carbon nanotubes (MWCNTs) and aluminum oxide (Al2O3) gamma nanoparticles have been utilized as nano-additives. The novelty here lies on enhancing the MQL heat capacity using different nano-additives-based fluids in order to improve Inconel 718 machinability. In this investigation, both nano-fluids showed better results compared to the tests performed without any nano-additives. Significant changes in modes of tool wear and improvement in the intensity of wear progression have been observed when using nano-fluids. Also, the collected chips have been analyzed to understand the effects of adding nano-additives on the chip morphology. Finally, it has been found that MWCNT nano-fluid has shown better performance than Al2O3 nano-fluid.
158 citations
TL;DR: It is found that SAR is more effective for difficult tasks than for simple ones and that the main advantage of SAR is related more to the reduction of error rates than to completion times.
Abstract: Augmented reality (AR) is a key technology for the development of smart manufacturing. One of the main advantages of AR is that it can help workers to accomplish several tasks, making it possible the shift from mass production to mass customization. However, it is still not clear how these promises can be fulfilled in an industrial scenario. In particular, the question about which display solutions fit better the industrial constraints remains open. Based on the literature overview, laboratory experiments, and feedbacks from industrial companies, we supported the use of spatial augmented reality (SAR), designing a prototype intended to be used for manual working stations of the future smart factories. This work presents the evaluation of the effectiveness of conveying technical instructions with this SAR prototype as compared to paper manual. We run a within-subjects experiment with 16 participants to measure user task performance (completion times and error rates) and to collect subjective evaluation. We projected technical information on a motorbike engine during a seven-task maintenance procedure. Our results proved that SAR technology improves the operators’ performance with respect to a paper manual and that users well accept it. We found that SAR is more effective for difficult tasks than for simple ones and that the main advantage of SAR is related more to the reduction of error rates than to completion times. These results confirm the goodness of our design choices; then our prototype can be a valid candidate solution for a smart manufacturing application.
158 citations
TL;DR: In this paper, the authors present relevant studies being classified according to the technology implemented (vision, camera, acoustic emissions, ultrasonic testing (UT), eddy current technique (ECT)) for the quality inspection.
Abstract: Quality assessment methods and techniques for laser welding have been developed both in- and post-process. This paper summarizes and presents relevant studies being classified according to the technology implemented (vision, camera, acoustic emissions, ultrasonic testing (UT), eddy current technique (ECT)) for the quality inspection. Furthermore, the current review aims to map the existing modeling approaches used to correlating measured weld characteristics and defects with the process parameters. Research gaps and implications of the quality assessment in laser welding are also described, and a future outlook of the research in the particular field is provided.
150 citations
TL;DR: A novel method for the defect detection within the SLM parts is proposed by a simplified classification structure without signal preprocessing and feature extraction and showed that the utilization of acoustic signals was workable for quality monitoring, and the DBN approach could reach high defect detection rate among five melted states without signalPreprocessing.
Abstract: Selective laser melting (SLM) is one of the most important and successfully additive manufacturing processes in 3D metal printing technologies. Critical quality issues such as porosity, surface roughness, crack, and delamination continue to present challenges within SLM-manufactured parts. Monitoring and in-process defect diagnosis are the key to improving the final part quality. Currently, it greatly hinders the adaptability and the development within the defect detection system since the setup restricts the vision and photo diode applications in the SLM process monitoring. Additionally, defect detection with traditional classification approaches makes the system rather complex due to introducing a series of steps. To meet these needs, this study proposes a novel method for the defect detection within the SLM parts. The setup was flexibly conducted using a microphone, and the defect detection was obtained by the framework of deep belief network (DBN). It is implemented by a simplified classification structure without signal preprocessing and feature extraction. The experimental results showed that the utilization of acoustic signals was workable for quality monitoring, and the DBN approach could reach high defect detection rate among five melted states without signal preprocessing.
TL;DR: In this study, a distance-constrained mobile hierarchical facility location problem is used in order to find the optimal number and locations of launch and recharge stations with the objective of minimizing the total costs of the system.
Abstract: In the last decade, aerial delivery system has been considered as a promising response to increasing traffic jams and incremental demand for transportation. In this study, a distance-constrained mobile hierarchical facility location problem is used in order to find the optimal number and locations of launch and recharge stations with the objective of minimizing the total costs of the system. System costs include establishment cost for launching and recharge stations, drone procurement, and drone usage costs. It is supposed that the demand occurs according to Poisson distribution, distributed uniformly along the network edges and is satisfied by the closest open facility. Since the flying duration of a drone is limited to its endurance, it may visit one or more recharge stations to reach to the demand point. This route is calculated by the shortest path algorithm, and the Euclidean distance is considered between nodes and facilities. It is proved that facility location problems are NP-hard on a general graph. Accordingly, heuristic algorithms are proposed as solution method. To illustrate the applicability of the algorithms, a case study is presented and the results are discussed.
TL;DR: In this paper, the development of technology of the main individual physical condition monitoring and fault diagnosis of rolling bearings is introduced, then the fault diagnosis technology of multi-sensors information fusion is introduced.
Abstract: A rolling bearing is an essential component of a rotating mechanical transmission system Its performance and quality directly affects the life and reliability of machinery Bearings’ performance and reliability need high requirements because of a more complex and poor working conditions of bearings A bearing with high reliability reduces equipment operation accidents and equipment maintenance costs and achieves condition-based maintenance First in this paper, the development of technology of the main individual physical condition monitoring and fault diagnosis of rolling bearings are introduced, then the fault diagnosis technology of multi-sensors information fusion is introduced, and finally, the advantages, disadvantages, and trends developed in the future of the detection main individual physics technology and multi-sensors information fusion technology are summarized This paper is expected to provide the necessary basis for the follow-up study of the fault diagnosis of rolling bearings and a foundational knowledge for researchers about rolling bearings
TL;DR: In this paper, the authors employed convolutional neural network (CNN) as a well-established and powerful deep learning algorithm for tool wear estimation and proposed a hybrid feature extraction method using wavelet time-frequency transformation and spectral subtraction algorithms.
Abstract: Process monitoring is necessary in machining operation to increase productivity, improve surface quality, and reduce unscheduled downtime. Tool wear and breakage are important and common source of machining problems due to high temperatures and forces of the machining process. Therefore, it is highly beneficial to develop an online tool condition monitoring (TCM) system. This paper investigates a robust tool wear monitoring system for milling operation. Recent developments in machine learning, in particular deep learning methods, result in significant improvement in automation of different industries. Therefore, in this research, we employed convolutional neural network (CNN) as a well-established and powerful deep learning algorithm for tool wear estimation. Wavelet packet-based features are extracted for tool wear monitoring as a powerful time-frequency fault indicator. Moreover, a hybrid feature extraction method is proposed using wavelet time-frequency transformation and spectral subtraction algorithms to intensify the effect of tool wear in the signal and reduce the effect of other cutting parameters. CNN-based monitoring systems are compared with three other machine learning methods (support vector machine, Bayesian rigid network, and K nearest neighbor method) as the baseline. The research is validated using different datasets. The algorithms are implemented and compared using experimental force and vibration signals from LIPPS lab of ETS university as well as using current signals as the fault indicator from Nasa_Ames dataset.
TL;DR: In this paper, the authors investigated the influence of dispersed multi-walled carbon nanotubes (MWCNTs) into vegetable oil by implementing the minimum quantity lubrication (MQL) technique during turning of Ti-6Al-4V.
Abstract: Owing to their superior mechanical, physical, and chemical characteristics, titanium and its alloys are broadly used in different industrial applications such as military, aerospace, power generation, and automotive. However, titanium alloys are inherently difficult to cut materials due to the high generated temperature during machining. In addition to flood cooling, several other techniques were employed to reduce the harmful effect and the generated temperature and generally improve titanium alloys machinability. In this paper, an attempt is made to utilize nano-additives to improve the cooling efficiency of minimum quantity lubrication (MQL) during machining titanium alloys. The main objective of the current research is to investigate the influence of dispersed multi-walled carbon nanotubes (MWCNTs) into vegetable oil by implementing the MQL technique during turning of Ti–6Al–4V. The novelty here lies on enhancing the MQL heat capacity using different concentrations of nano-fluid in order to improve Ti–6Al–4V machinability. Different cutting tests were performed and relevant data were collected. The studied design variables were cutting speed, feed rate, and percentage of added nano-additives (wt%). It was found that 2 wt% MWCNT nano-fluid reduced the power consumption by 11.5% in comparison with tests performed without any nano-additives, while the same concentration reduced the flank wear by 45%.
TL;DR: In this article, the authors focused on friction stir welding (FSW) of dissimilar aluminum alloys and steels, an area that is getting great concern recently and identified the opportunities and challenges for the future.
Abstract: The present paper is focused on friction stir welding (FSW) of dissimilar aluminum alloys and steels, an area that is getting great concern recently The promise of FSW joints lies in low welding heat input and its ability to minimize the extent of the formation of intermetallic compound (IMC) in dissimilar metals The present paper assessed the status of FSW process of dissimilar aluminum alloys and steels, and to identify the opportunities and challenges for the future The essential reason for the formation of the dissimilar Al/steel FSW joints with high quality is explained by super diffusion behavior This paper will provide basis to designers and engineers to consider FSW for a wider range of dissimilar aluminum alloys and steels
TL;DR: This paper attempts to present an overview of current digital situation of factories, and proposes a systematical framework of cyber-physical integration in factories, with consideration of the concept of digital twin and the theory of manufacturing service.
Abstract: The current study on digital factory (DF) meets some problems, such as disconnected manufacturing sites, independent digital models, isolated data, and non-self-controlled applications. In order to move current situation of DFs forward towards smart manufacturing, this paper attempts to present an overview of current digital situation of factories, and propose a systematical framework of cyber-physical integration in factories, with consideration of the concept of digital twin and the theory of manufacturing service. Particularly, the proposed framework includes four key issues, i.e., (a) fully interconnected physical elements integration, (b) faithful-mirrored virtual models integration, (c) all of elements/flows/businesses-covered data fusion, and (d) data-driven and application-oriented services integration. The corresponding implementable solutions of these four key issues are discussed in turn. As a reference, this paper is promising to bridge the gap in factories from current digital situation to smart manufacturing, so as to effectively facilitate their smart production.
TL;DR: In this article, a series of milling experiments on Inconel 718 alloy was conducted under dry, conventional flood, and MQL cooling modes, and the particle swarm optimization (PSO) and bacteria foraging optimization (BFO) were employed to optimize the cutting speed, feed rate, and depth-of-cut to minimize the flank wear parameter of a cutting tool.
Abstract: The Inconel 718 alloy, a difficult-to-cut superalloy with an extensive demand on aircraft and nuclear industries, being a low thermally conductive material exhibits a poor machinability. Consequently, the cutting tool is severely affected, and the tool cost is increased. In this context, an intelligent solution is presented in this paper—investigation of minimum quantity lubrication (MQL) and the selection of best machining conditions using evolutionary optimization techniques. A series of milling experiments on Inconel 718 alloy was conducted under dry, conventional flood, and MQL cooling modes. Afterward, the particle swarm optimization (PSO) and bacteria foraging optimization (BFO) were employed to optimize the cutting speed, feed rate, and depth-of-cut to minimize the flank wear (VBmax) parameter of a cutting tool. Though both the PSO and BFO models performed well, the validated results showed the superiority of PSO. Furthermore, it was found that the MQL performed better than the dry and flood cooling condition with respect to the reduction of the tool flank wear.
TL;DR: In this paper, the experimental and theoretical studies on EDM that aimed to improve the process performance, including material removal rate, surface quality, and tool wear rate, among others.
Abstract: Electric discharge machining (EDM) is one of the leading edge machining processes successfully used to machine hard-to-cut materials in wide range of industrial applications. It is a non-conventional material removal process that can machine a complex shapes and geometries with high accuracy. The principle of the EDM technique is to use thermoelectric energy to erode conductive components by rapidly recurring sparks between the non-contacted electrode and workpiece. To improve EDM performance, the machine’s operating parameters need to be optimized. Studies related to the EDM have shown that the appropriate selection of the process, material, and operating parameters had considerably improved the process performance. This paper made a comprehensive review about the research studies on the EDM of different grades of titanium and its alloys. This review presents the experimental and theoretical studies on EDM that aimed to improve the process performance, including material removal rate, surface quality, and tool wear rate, among others. This paper also examines evaluation models and techniques used to determine the EDM process conditions. Moreover, the paper discusses the recent developments in EDM and outlines the progression for future research.
TL;DR: A new method for 5-axis flank computer numerically controlled (CNC) machining is proposed, reducing significantly the execution times while preserving or even reducing the milling error when compared to the state-of-the-art industrial software.
Abstract: A new method for 5-axis flank computer numerically controlled (CNC) machining is proposed. A set of tappered ball-end-mill tools (aka conical milling tools) is given as the input and the flank milling paths within user-defined tolerance are returned. Thespace of lines that admit tangential motion of an associated truncated cone along a general, doubly curved, free-form surface is explored. These lines serve as discrete positions of conical axes in 3D space. Spline surface fitting is used to generate a ruled surface that represents a single continuous sweep of a rigid conical milling tool. An optimization-based approach is then applied to globally minimize the error between the design surface and the conical envelope. The milling simulations are validated with physical experiments on two benchmark industrial datasets, reducing significantly the execution times while preserving or even reducing the milling error when compared to the state-of-the-art industrial software.
TL;DR: In this article, the Johnson-Cook model constants of ultra-fine-grained titanium (UFG Ti) were determined using a chip formation model and an iterative gradient search method using Kalman filter algorithm.
Abstract: This paper presents an original method to inversely identify the Johnson–Cook model constants (J-C constants) of ultra-fine-grained titanium (UFG Ti) based on a chip formation model and an iterative gradient search method using Kalman filter algorithm. UFG Ti is increasingly finding usefulness in lightweight engineering applications and medical implant filed because of its sufficient mechanical strength, high manufacturability, and high biocompatibility. Johnson–Cook model is one of the constitutive models widely used in analytical modeling of machining force, temperature, and residual stress because it is effective, simple, and easy to use. Currently, the J-C constants of UFG Ti are unavailable and yet an effective identification methodology based upon machining data is not readily available. In this work, multiple cutting tests were conducted under different cutting conditions, in which machining forces were experimentally measured using a piezoelectric dynamometer. The machining forces were also predicted using the chip formation model with inputs of cutting conditions, workpiece material properties, and a set of given model constants. An iterative gradient search method was enforced to find the J-C constants when the difference between predicted forces and experimental forces reached an acceptable low value. To validate the identified J-C constants, machining forces were predicted using the identified J-C constants under different cutting conditions and then compared to the corresponding experimental forces. Close agreements were observed between predicted forces and experimental forces. Considering the simple orthogonal cutting tests, reliable and easily measurable machining forces, and efficient iterative gradient search method, the proposed method has less experimental complexity and high computational efficiency.
TL;DR: A critical literature review of DM-assisted hybrid additive manufacturing (DM-HAM) has also been carried out as mentioned in this paper, which seems to be very promising for next generation multi-operational manufacturing as it is time saving and economical.
Abstract: From Germany’s Industry 4.0 mission to Made in China 2025 and Make in India mission to British Factory of the Future in 2050, digital manufacturing (DM) is promoting in the world’s major industrial countries as a technology foundation of the future manufacturing. At the same time, in the different segments of the DM realm, different forms of information technologies (IT) are flourishing such as the following: computer-aided manufacturing, robotics control in manufacturing, and process simulation. This paper is aimed to review the latest initiatives of DM in the leading universities and major industrial countries. Along with, a critical literature review of various initiatives in the area of DM-assisted hybrid additive manufacturing (DM-HAM) has also been carried out. DM-HAM seems to be very promising for next generation multi-operational manufacturing as it is time saving and economical. The highlights of this review will provide a guide for the upcoming research activities in the area of DM-HAM.
TL;DR: In this paper, a modularity-based multi-objective approach that uses an adapted version of the "Archived Multi-Objective Simulated Annealing" (AMOSA) method was proposed to solve the optimization problem by selecting from a set of candidate machines the most suitable ones.
Abstract: Enhancing productivity, reducing inaccuracy and avoiding time waste at changeover are considered major drivers in manufacturing system design. One of the emerging paradigms concerned with these characteristics is reconfigurable manufacturing systems (RMSs). The high responsiveness and performance efficiencies of RMS make it a convenient manufacturing paradigm for and flexible enabler of mass customization. The RMS offers customized flexibility and a variety of alternatives as features thanks to its reconfigurable machine tool (RMT). These machines represent a major component of RMS and are based on an adjustable, modular and reconfigurable structure. Hence, the system modularity is of great importance. This paper outlines a multi-objective approach to optimize the RMS design. Three objectives are considered: the maximization of the system modularity, the minimization of the system completion time and the minimization of the system cost. We developed a modularity-based multi-objective approach that uses an adapted version of the “Archived Multi-Objective Simulated Annealing” (AMOSA) method to solve the optimization problem by selecting from a set of candidate machines the most suitable ones. Implemented, the decision maker can use a multi-objective decision making tool based on the well-known “Technique for Order of Preference by Similarity to Ideal Solution” (TOPSIS) to choose the best solution in the Pareto front according to his preferences. We demonstrated the applicability of the proposed approach through an illustrative example and an analysis of the obtained numerical results.
TL;DR: A new evolutionary algorithm called modified adaptive differential evolution (MADE) is introduced for multi-objective optimization of a cam mechanism with offset translating roller follower for minimum congestion, maximum efficiency, and maximum strength resistance of the cam.
Abstract: The optimum design of a cam mechanism is a very interesting problem in the contact mechanics today, due to the alternative industrial applications as a mechanism of precision. In this paper, a new evolutionary algorithm called modified adaptive differential evolution (MADE) is introduced for multi-objective optimization of a cam mechanism with offset translating roller follower. The optimization procedure is investigated for three objectives among them minimum congestion, maximum efficiency, and maximum strength resistance of the cam. To enhance the design quality of the mechanism in the optimization process, more geometric parameters and more design constraints are included in the problem formulation. In order to validate the developed algorithm, three engineering design problems are solved. The simulation results for the tested problems indicate the effectiveness and the robustness of the proposed algorithm compared to the various existed optimization methods. Finally, the optimal results obtained for the case study example provide very useful decisions for a cam mechanism synthesis.
TL;DR: In this article, a review on laser welding of Al/Mg, Al/Ti, and Mg/Ti alloys is presented, with focus on the techniques used to suppress the formation of brittle intermetallic compounds (IMCs) and improve joining mechanism.
Abstract: With the growing demand for vehicle weight reduction and the increased application of multimaterial design, it is imperative to address the challenges of welding dissimilar light alloys. This paper presents a review on laser welding of Al/Mg, Al/Ti, and Mg/Ti alloys, with focus on the techniques used to suppress the formation of brittle intermetallic compounds (IMCs) and improve joining mechanism. For Al/Mg joints, studies have shown that the use of structural adhesives, the use of interlayers such as Ce, Ni, Ti, and Fe foils and Zn-Al filler metals, and hybrid adhesive-interlayers could suppress the formation of brittle IMCs and improve joint strength. The formation of brittle IMCs during laser welding of Al/Ti alloys could be minimized by offsetting the laser beam at an appropriate distance towards either the Al or Ti alloy, the use of split-beam laser with appropriate joint design, the use of high welding speed, improving the laser energy distribution, and the use of V-groove with Al-Si filler metals. For Mg/Ti alloys, offsetting the laser beam at an appropriate distance towards the Mg alloy, the use of AZ series Mg alloy-based filler wires, and coating the Ti alloy with Ni were found to facilitate the joint formation and improve the joint strength.
TL;DR: In this paper, the laser beam welding of similar and dissimilar titanium alloys is reviewed, focusing on the influence of the processing parameters, microstructure-property relationship, metallurgical defects, and possible remedies.
Abstract: In recent years, there is an increased in used of titanium alloys for some parts of mass-produced automobiles and aerospace. However, titanium alloys are characterized by difficult machinability, high melting temperature, high strength, low thermal conductivity, and high reactivity to oxygen, which overshadowed conventional manufacturing processes. To this end, there is a pressing need for more efficient technologies for the manufacture of low-cost titanium structures. Over the years, several joining techniques have been considered for fabrication of titanium alloys. Nevertheless, laser beam welding presents a viable option for welding of titanium due its versatility, high specific heat input, and flexibility. To date, under optimum processing conditions, the strength of the laser-welded titanium alloys can be close to the original material; however, there are still some processing problems such as lower elongation and corrosion resistance coupled with inferior fatigue properties. In this document, the laser beam welding of similar and dissimilar titanium alloys is reviewed, focusing on the influence of the processing parameters, microstructure-property relationship, metallurgical defects, and possible remedies.
TL;DR: In this article, the authors describe an experimental investigation into minimizing the aforementioned defects for hybrid fiber reinforced polymer composites and reveal that the kerf ratio was mainly influenced by the stand-off distance and traverse rate.
Abstract: Kerf taper and delamination are undesirable geometrical defects inherent to abrasive water-jet machining (AWJM) of layered fibre reinforced polymer composites. This is mainly attributed to the characteristics of water-jet energy as well as the anisotropic nature of the material. The present research describes an experimental investigation into minimizing the aforementioned defects for hybrid fibre reinforced polymer composites. Experimental results reveal that the kerf ratio was mainly influenced by the stand-off distance and traverse rate. Both sides of delamination were influenced by abrasive flow rate, traverse rate, and hydraulic pressure. Minimum kerf ratio and delamination damage can be achieved by increasing the kinetic energy of abrasive water-jet stream when impinging under a lower cutting speed. Response surface methodology (RSM) was employed for establishing empirical relationships between experimental outputs and controlled parameters. Confirmation tests have a variance of within 5% for both outputs via comparison between experimental values and the regression models.
TL;DR: In this article, a comprehensive review on the mechanism of pore formation, mechanical properties, and applications of metallic porous materials is presented, which highlights some important factors for advanced wear-resistant tool and biomedical implant applications of porous metallic materials.
Abstract: This paper presents a comprehensive review on the mechanism of pore formation, mechanical properties, and applications of metallic porous materials. The different manufacturing techniques of metallic porous materials using various pore-forming agents (e.g., sodium chloride, polymethyl methacrylate, magnesium, and cenosphere) are highlighted in the first part of this review. Subsequently, the pore formation mechanism and pore morphology in final products as well as corresponding pore-forming agent removal techniques (e.g., sintering-dissolution process, thermally stimulated decomposition, thermally melted elimination, and embedding cenosphere technique) are specifically discussed. Then, some major influential factors on the mechanism of pore formation, including pore size, shape, distribution, and porosity, are analyzed in detail. Meanwhile, the primary mechanical properties such as compressive strength, elastic modulus, fatigue properties, and flexural strength of metallic porous materials depending on pore morphology and porosity are explored in detail. Furthermore, their applications in structural and functional aspects according to their pore morphology and mechanical properties are emphatically summarized. Finally, this review article highlights some important factors for advanced wear-resistant tool and biomedical implant applications of porous metallic materials.
TL;DR: Two nonplanar slicing approaches are presented: a decomposition-based curved surface slicing strategy and a transformation-based cylinder surface slicing method that is capable of slicing mesh models.
Abstract: Additive manufacturing (AM, generally called 3D printing) has attracted great research interests due to its ability to build complex shapes. It transforms design files to functional products through slicing and material accumulation. Typically, the planar slicing strategy is used in AM to convert CAD model into accumulating layers. However, when building overhang structures and curved parts, it often needs support structures and generates a large number of planar layers, which lead to the fact that it spends more time in manufacturing. To reduce the need for support structures and decrease the number of layers, this paper presents two nonplanar slicing approaches: a decomposition-based curved surface slicing strategy and a transformation-based cylinder surface slicing method. The former is implemented based on STEP models and the latter is capable of slicing mesh models. The feasibility of the proposed methods are validated by printing two parts with a robotic fused deposition modelling system.
TL;DR: A service transaction model of 3D printing services in CMfg is built and a 3DPSS method is proposed to generate optimal service scheduling solutions to reduce the delivery time of tasks from service suppliers to service demanders.
Abstract: The problem of service matching and scheduling in cloud manufacturing (CMfg) is complex for different types of manufacturing services. 3D printing, as a rapidly developing manufacturing technology, has become an important service form in the CMfg platform due to its characteristics of personalized manufacturing. How to solve the task scheduling problem for distributed 3D printing services in CMfg needs further research. In this paper, a service transaction model of 3D printing services in CMfg is built. Based on the service transaction model, we propose 3D printing service matching strategies and matching rules of different service attributes, including model size, printing material, printing preciseness, task cost, task time, and logistics. To reduce the delivery time of tasks from service suppliers to service demanders, a 3D printing service scheduling (3DPSS) method is proposed to generate optimal service scheduling solutions. In 3DPSS, optimization objective, constraints, and optimization algorithm are presented in detail. Experimental results show that the average task delivery time of 3DPSS is shorter than that of typical scheduling methods, such as particle swarm optimization, pattern search, and sequential quadratic programming, when the amounts of tasks change.