Showing papers in "The International Journal of Advanced Manufacturing Technology in 2012"
TL;DR: In this paper, a comparison between two different technologies for metal part fabrication, the traditional high-pressure die-casting and the direct metal laser sintering additive technique, is done with consideration of both the geometric possibilities of AM and the economic point of view.
Abstract: Additive manufacturing (AM) of metal parts combined with part redesign has a positive repercussion on cost saving. In fact, a remarkable cost reduction can be obtained if the component shape is modified to exploit AM potentialities. This paper deals with the evaluation of the production volume for which AM techniques result competitive with respect to conventional processes for the production of end-usable metal parts. For this purpose, a comparison between two different technologies for metal part fabrication, the traditional high-pressure die-casting and the direct metal laser sintering additive technique, is done with consideration of both the geometric possibilities of AM and the economic point of view. A design for additive manufacturing approach is adopted. Costs models of both processes are identified and then applied to an aeronautical component selected as case study. This research evidences that currently additive techniques can be economically convenient and competitive to traditional processes for small to medium batch production of metal parts.
542 citations
TL;DR: In this article, a detailed investigation into the balling behavior of selective laser melting of stainless steel and pure nickel powder was conducted, and it was found that the SLM balling phenomenon can be divided into two types generally: the ellipsoidal balls with dimension of about 500μm and the spherical balls with dimensions of about 10μm.
Abstract: Balling phenomenon, as a typical selective laser melting (SLM) defect, is detrimental to the forming quality. In this work, a detailed investigation into the balling behavior of selective laser melting of stainless steel and pure nickel powder was conducted. It was found that the SLM balling phenomenon can be divided into two types generally: the ellipsoidal balls with dimension of about 500 μm and the spherical balls with dimension of about 10 μm. The former is caused by worsened wetting ability and detrimental to SLM quality; the latter has no obvious detriment to SLM quality. The oxygen content plays an important role in determining the balling initiation, which can be considerably lessened by decreasing the oxygen content of atmosphere to 0.1%. A high laser line energy density, which can be obtained by applying high laser power and low scan speed, could enable a well-wetting characteristic. The effect of scan interval on balling initiation is not obvious as long as the scan track is continuous. The surface remelting procedure can also alleviate the balling effect in a certain extent, due to the melting and wetting of metal balls. Moreover, the balling phenomenon of pure nickel was also studied, and the results implied that the balling discipline had a universality.
503 citations
TL;DR: In this article, the effects of scan speed, layer thickness, and building direction on the following part features: dimensional error, surface roughness, and mechanical properties for DMLS with DS H20 powder and SLM with CL 20 powder (1.4404/AISI 316L).
Abstract: Rapid manufacturing is an advanced manufacturing technology based on layer-by-layer manufacturing to produce a part. This paper presents experimental work carried out to investigate the effects of scan speed, layer thickness, and building direction on the following part features: dimensional error, surface roughness, and mechanical properties for DMLS with DS H20 powder and SLM with CL 20 powder (1.4404/AISI 316L). Findings were evaluated using ANOVA analysis. According to the experimental results, build direction has a significant effect on part quality, in terms of dimensional error and surface roughness. For the SLM process, the build direction has no influence on mechanical properties. Results of this research support industry estimating part quality and mechanical properties before the production of parts with additive manufacturing, using iron-based powders.
232 citations
TL;DR: A new particle swarm-based optimization method is presented for multi-objective optimization of vehicle crashworthiness that is applied to multi- objective crashworthiness optimization of a full vehicle model and milling optimization problems to demonstrate its effectiveness and validity.
Abstract: Vehicle crashworthiness is an important issue to ensure passengers safety and reduce vehicle costs in the early design stage of vehicle design. The aim of the crashworthiness design is to provide an optimized structure that can absorb the crash energy by controlled vehicle deformations while maintaining enough space of the passenger compartment. Meta-modeling and optimization techniques have been used to reduce the vehicle design cycle. In this paper, a new particle swarm-based optimization method is presented for multi-objective optimization of vehicle crashworthiness. The proposed optimization method is first validated with a multi-objective disk brake problem taken from literature. Finally, it is applied to multi-objective crashworthiness optimization of a full vehicle model and milling optimization problems to demonstrate its effectiveness and validity.
214 citations
TL;DR: In this article, a two-input single-output hybrid control system including a master height controller and a slave temperature controller is used to control both height growth and melt pool temperature at each deposition layer.
Abstract: This paper presents a hybrid control system that is able to improve dimensional accuracy of geometrically complex parts manufactured by direct metal deposition process. The melt pool height is monitored by three high-speed charged couple device cameras in a triangulation setup. The melt pool temperature is monitored by a dual-color pyrometer. A two-input single-output hybrid control system including a master height controller and a slave temperature controller is used to control both height growth and melt pool temperature at each deposition layer. The height controller is a rule-based controller and the temperature controller uses a generalized predictive control algorithm with input constraints. When the melt pool height is above a prescribed layer thickness, the master height controller blocks control actions from the temperature controller and decreases laser power to avoid over-building. When the melt pool height is below the prescribed layer thickness, the temperature controller bypasses the height controller and dynamically adjusts laser power to control the melt pool temperature. This hybrid controller is able to achieve stable layer growth by avoiding both over-building and under-building through heat input control. A complex 3-D turbine blade with improved geometrical accuracy is demonstrated using the hybrid control system.
204 citations
TL;DR: In this article, the current status of the FSP technology in the field of composite fabrication with the main impetus on aluminum and magnesium alloys is described, and a review article is presented.
Abstract: Composite manufacturing is one of the most imperative advances in the history of materials. Nanoparticles have been attracting increasing attention in the composite community because of their capability of improving the mechanical and physical properties of traditional fiber-reinforced composites. Friction stir processing (FSP) has successfully evolved as an alternative technique of fabricating metal matrix composites. The FSP technology has recently shown a significant presence in generation of ex situ and in situ nanocomposites. This review article essentially describes the current status of the FSP technology in the field of composite fabrication with the main impetus on aluminum and magnesium alloys.
195 citations
TL;DR: In this paper, the as-deposited selective laser-melted Hastelloy® X specimen is compared with the heat-treated (hot forged) samples, and the yield strength is higher than that of the hot forged samples.
Abstract: Tensile mechanical properties of selective laser-melted Hastelloy® X alloy in as-deposited condition and after hot isostatic pressing (HIP) have been studied at ambient and elevated temperatures. Room temperature four-point bending and tension–tension fatigue properties have also been investigated in as-deposited condition and after HIP. The yield strength of the as-deposited selective laser-melted Hastelloy® X specimen is higher than the heat-treated (hot forged) samples. The ultimate strength is also higher than that of the hot forged samples while the elongation property is lower. This can be attributed to its ultrafine microstructure caused by rapid solidification, which is characteristic of the selective laser melting process. It is also found that the mechanical properties (tensile and fatigue) do not vary with samples built in different bed locations.
189 citations
TL;DR: In this article, a survey on assembly sequence planning and assembly line balancing problems using various soft computing approaches is presented, including genetic algorithm, ant colony optimization, particle swarm optimization and particle swarm optimisation.
Abstract: Assembly optimisation activities occur across development and production stages of manufacturing goods. Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) problems are among the assembly optimisation. Both of these activities are classified as NP-hard. Several soft computing approaches using different techniques have been developed to solve ASP and ALB. Although these approaches do not guarantee the optimum solution, they have been successfully applied in many ASP and ALB optimisation works. This paper reported the survey on research in ASP and ALB that use soft computing approaches for the past 10 years. To be more specific, only Simple Assembly Line Balancing Problem (SALBP) is considered for ALB. The survey shows that three soft computing algorithms that frequently used to solve ASP and ALB are Genetic Algorithm, Ant Colony Optimisation and Particle Swarm Optimisation. Meanwhile, the research in ASP and ALB is also progressing to the next level by integration of assembly optimisation activities across product development stages.
185 citations
TL;DR: In this paper, a framework for determining importance degree of criteria to overcome the shortcomings of this subject in material selection is presented, and the suggested framework covers the situation of interdependent relationship between the criteria which has not been surveyed in the material selection.
Abstract: Material selection is an onerous process of design activities which needs to be carefully carried out in order to increase the probability of success. A lot of multi-criteria decision-making methods have been proposed in material selection, many of which require quantitative weights for the attributes. Since weights play a very significant role in the ranking results of the materials, this paper presents a framework for determining importance degree of criteria to overcome the shortcomings of this subject in material selection. Furthermore, the suggested framework covers the situation of interdependent relationship between the criteria which has not been surveyed in material selection yet. An example was considered to illustrate how this framework is conducted. On the basis of the numerical results, it can be concluded that the proposed method can soundly deal with the material selection problems.
183 citations
TL;DR: In this paper, an effective artificial bee colony (ABC) algorithm is proposed for solving the flexible job shop scheduling problem with the criterion to minimize the maximum completion time (makespan), which stresses the balance between global exploration and local exploitation.
Abstract: An effective artificial bee colony (ABC) algorithm is proposed in this paper for solving the flexible job-shop scheduling problem with the criterion to minimize the maximum completion time (makespan). The ABC algorithm stresses the balance between global exploration and local exploitation. First, multiple strategies are utilized in a combination to generate the initial solutions with certain quality and diversity as the food sources. Second, crossover and mutation operators are well designed for machine assignment and operation sequence to generate the new neighbor food sources for the employed bees. Third, a local search strategy based on critical path is proposed and embedded in the searching framework to enhance the local intensification capability for the onlooker bees. Meanwhile, an updating mechanism of population by generating the scout bees with the initialing strategy is proposed to enrich the searching behavior and avoid the premature convergence of the algorithm. In addition, a well-designed left-shift decoding scheme is employed to transform a solution to an active schedule. Numerical simulation results based on well-known benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed ABC algorithm.
178 citations
TL;DR: In this article, the processing parameters of selective laser melting were investigated in order to fabricate denser Ti6Al4V parts without post-processes, and it was found that there exists a great temperature gradient from the surface of powder bed to the experimental platform, and the maximum depth of molten powder layer is about 45 μm, very close to the total thickness of powder body (50 μm) under the condition of laser power of 110 W and scan rate of 0.2 m/s.
Abstract: Considering that the densification level and the attendant quality of selective laser melted Ti6Al4V parts depend strongly on the operating temperature of the melting system, which is mainly controlled by the processing parameters. The processing parameters of selective laser melting were thus investigated in this study to fabricate denser Ti6Al4V parts without post-processes. Temperature distribution calculation was firstly carried out based on a three-dimensional model. It was found that there exists a great temperature gradient from the surface of powder bed to the experimental platform, and the maximum depth of molten powder layer is about 45 μm, very close to the total thickness of powder bed (50 μm) under the condition of laser power of 110 W and scan rate of 0.2 m/s. The Ti6Al4V parts with lower porosity and higher density were then well fabricated by experimental method under the condition of laser power of 110 W and scan rate of 0.2 m/s. The experimental results also indicate that the microstructures exhibit more and more pores and the layer structures are more and more obvious with the increase in the scan rate. Moreover, the microhardness measurement yields different values with increasing scan rate, owing to the increase of α phase and porosity.
TL;DR: In this article, a multi-objective particle swarm optimization method was used to find the optimal process parameters which minimize the surface roughness and burr formation in micro-end milling.
Abstract: Micro-end milling is one of the promising methods for rapid fabrication of features with 3D complex shapes. However, controlling the micro-end milling process to obtain the desired results is much harder compared to that of macro-end milling due to the size effect and uncontrollable factors. The problem is much pronounced when workpiece material is a difficult-to-process material such as titanium-based alloys which are widely used as material of choice for aircraft structures, turbine blades, and medical implants. In order to find the optimal process parameters which minimize the surface roughness and burr formation, experiments were conducted and models obtained with statistically based methods utilized in multi-objective particle swarm optimization to identify optimum process parameters. The results show that the average surface roughness can be minimized while burr formation is reduced concurrently.
TL;DR: The BBO algorithm is developed for flexible job shop scheduling problem (FJSP), which means that migration operators of BBO are developed for searching a solution area of FJSP and finding the optimum or near-optimum solution to this problem.
Abstract: Biogeography-based optimization (BBO) algorithm is a new kind of optimization technique based on biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. In this paper, the BBO algorithm is developed for flexible job shop scheduling problem (FJSP). It means that migration operators of BBO are developed for searching a solution area of FJSP and finding the optimum or near-optimum solution to this problem. In fact, the main aim of this paper was to provide a new way for BBO to solve scheduling problems. To assess the performance of BBO, it is also compared with a genetic algorithm that has the most similarity with the proposed BBO. This similarity causes the impact of different neighborhood structures being minimized and the differences among the algorithms being just due to their search quality. Finally, to evaluate the distinctions of the two algorithms much more elaborately, they are implemented on three different objective functions named makespan, critical machine work load, and total work load of machines. BBO is also compared with some famous algorithms in the literature.
TL;DR: In this article, the authors have studied the single-track, multi-track and multi-layer and had obtained four types of typical tracks, including regular and thick shape, regular and thin shape, irregular and pre-balling shape.
Abstract: Energy input is crucial in the manufacturing of high-density metal part with smooth surface. In this article, the authors had studied the single-track, multi-track, and multi-layer and had obtained four types of typical tracks, including regular and thick shape, regular and thin shape, regular but occasionally broken shape, irregular and pre-balling shape. The analysis of single- and multi-track experiments showed that the regular and thin shape was the most suitable for selective laser melting (SLM) fabrication. Multi-track experiments proved that dense and smooth surface can be obtained when the overlapping rate was around 30% based on the regular- and thin-shaped track. As a result of the heat accumulation effect during multi-track and multi-layer fabrication, it was possible to obtain ideal track type with less energy input. In multi-layer experiment, the gradually thicken layer was the reason for the surface quality deterioration. The inter-layer stagger scanning strategy, which can improve the quality of the end-use part, was used in this experiment. By testing the 316 L stainless steel samples fabricated by the SLM process, the microstructure can be identified as composed of fine equiaxed and columnar grains, and the samples had higher tensile strength and hardness than castings of the same material, but with lower elongation. The experiments had proved that SLM process can directly produce high dense 316 L stainless steel part with smooth surface.
TL;DR: In this paper, the authors investigated a relief chain design problem where not only demands but also supplies and the cost of procurement and transportation are considered as the uncertain parameters, and the model considered uncertainty for the locations where those demands can arise and the possibility that a number of the facility could be partially destroyed by the disaster.
Abstract: Relief logistics is one of the most important elements of a relief operation. This paper investigates a relief chain design problem where not only demands but also supplies and the cost of procurement and transportation are considered as the uncertain parameters. Furthermore, the model considers uncertainty for the locations where those demands can arise and the possibility that a number of the facility could be partially destroyed by the disaster. The proposed model for this study is formulated as a mixed-integer nonlinear programming to minimize the sum of the expected total cost (which includes costs of location, procurement, transportation, holding, and shortage) and the variance of the total cost. The model simultaneously determines the location of relief distribution centers and the allocation of affected area to relief distribution centers. Furthermore, an efficient solution approach based on particle swarm optimization is developed in order to solve the proposed mathematical model. At last, computational results for several instances of the problem are presented to demonstrate the feasibility and effectiveness of the proposed model and algorithm.
TL;DR: In this article, surface textures were made using laser on the rake or flank face of the cemented carbide (WC/Co) inserts to form self-lubricating textured tools.
Abstract: Surface textures were made using laser on the rake or flank face of the cemented carbide (WC/Co) inserts. Molybdenum disulfide solid lubricants were filled into the textured grooves to form self-lubricating textured tools. Dry cutting tests on Ti-6Al-4V were carried out with these self-lubricating textured tools and conventional tool. The machining performance was assessed in terms of the cutting forces, cutting temperature, chip thickness ratio, friction coefficient at the tool–chip interface, and tool wear. Results show that the cutting forces and cutting temperature of the self-lubricating textured tools were reduced compared with that of the conventional tool. The application of the self-lubricating textured tool with elliptical grooves on its rake face can reduce the tool–chip friction coefficient and the chip thickness ratio. The tool life of the textured tools is improved compared with that of the conventional tool. The effectiveness of the self-lubricating textured tools in improving cutting performance is related to the cutting parameter.
TL;DR: A new comprehensive model for OACR is proposed in the CMfg system and new heuristics are designed flexibly based on the characteristics of the problem and pheromone is added for adaptive searching.
Abstract: As a new advanced service-oriented networked manufacturing model, cloud manufacturing (CMfg) has been proposed recently. The optimal allocation of computing resources (OACR) is a core part for implementing CMfg. High heterogeneity, high dynamism, and virtualization make the OACR problem more complex than the traditional scheduling problems in grid system or cloud computing system. In this paper, a new comprehensive model for OACR is proposed in the CMfg system. In this model, all main computation, communication, and reliability constraints in the special circumstances are considered. To solve the OACR problem, a new improved niche immune algorithm was presented. Associated with the niche strategy, new heuristics are designed flexibly based on the characteristics of the problem and pheromone is added for adaptive searching. Experiments demonstrate the effectiveness of the designed heuristic information and show NIA’s high performances for addressing the OACR problem compared with other intelligent algorithms.
TL;DR: In this paper, the authors presented an optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool to achieve minimum cutting forces and better surface roughness.
Abstract: High-speed machining (HSM) has emerged as a key technology in rapid tooling and manufacturing applications. Compared with traditional machining, the cutting speed, feed rate has been great progress, and the cutting mechanism is not the same. HSM with coated carbide cutting tools used in high-speed, high temperature situations and cutting more efficient and provided a lower surface roughness. However, the demand for high quality focuses extensive attention to the analysis and prediction of surface roughness and cutting force as the level of surface roughness and the cutting force partially determine the quality of the cutting process. This paper presents an optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool to achieve minimum cutting forces and better surface roughness. Taguchi optimization method is the most effective method to optimize the machining parameters, in which a response variable can be identified. The standard orthogonal array of L9 (34) was employed in this research work and the results were analyzed for the optimization process using signal to noise (S/N) ratio response analysis and Pareto analysis of variance (ANOVA) to identify the most significant parameters affecting the cutting forces and surface roughness. For such application, several machining parameters are considered to be significantly affecting cutting forces and surface roughness. These parameters include the lubrication modes, feed rate, cutting speed, and depth of cut. Finally, conformation tests were carried out to investigate the improvement of the optimization. The result showed a reduction of 25.5% in the cutting forces and 41.3% improvement on the surface roughness performance.
TL;DR: A comprehensive review of the literature regarding actuator systems which are based on the principle that combines the friction and inertia effect, named friction-inertia principle in this paper; such actuators is called frictioninertial actuator (FIA).
Abstract: This paper provides a comprehensive review of the literature regarding actuator systems which are based on the principle that combines the friction and inertia effect, named friction–inertia principle in this paper; such actuators is called friction–inertial actuator (FIA). The contribution of this paper lies in the generalization of various published (including patented) FIAs into a general principle with three specific principles. They are further taken as a framework upon which various published FIAs are classified and compared in terms of their principle, structure, and performance. In addition, this paper shows how this framework would allow for further innovation on FIAs. In the process of this generalization and classification, some confusion presented in the literature is also clarified. This paper also discusses further effort that may be taken to advance the friction–inertia actuation technology.
TL;DR: In this article, the influence of sheet thickness, nozzle diameter, standoff distance, and traverse speed during abrasive water jet machining (AWJM) of transformation-induced plasticity (TRIP) sheet steels on surface quality characteristics (kerf geometry and surface roughness) was investigated.
Abstract: In the present paper, the influence of sheet thickness, nozzle diameter, standoff distance, and traverse speed during abrasive water jet machining (AWJM) of transformation-induced plasticity (TRIP) sheet steels on surface quality characteristics (kerf geometry and surface roughness) was investigated. The experiments were designed using Taguchi methodology and carried out by AWJ Machining TRIP 700 CR-FH and TRIP 800 HR-FH steel sheets. As response variables, mean kerf width and average surface roughness were selected. The experimental results were analyzed using analysis of means and analysis of variance methods in order to correlate the AWJM process parameters the response variables. In addition, regression models were obtained using the experimental results and validated with six independent experiments. The reported results indicate that the proposed methodology can satisfactorily analyze the surface roughness and the mean kerf in AWJM; moreover, it can be considered as valuable tools for process planning in workshop.
TL;DR: In this article, the rotational magnetorheological abrasive flow finishing (R-MRAFF) has been proposed to enhance the finishing performance of MRAFF process, where a rotation cum reciprocating motion is provided to the polishing medium by a rotating magnetic field and hydraulic unit by intelligently controlling these two motions, a uniform smooth mirror-like finished surface with improved material removal rate and finishing rate is achieved for both stainless steel and brass workpieces.
Abstract: A new finishing process named as “rotational–magnetorheological abrasive flow finishing (R-MRAFF)” has been proposed to enhance the finishing performance of MRAFF process. In this process, a rotation cum reciprocating motion is provided to the polishing medium by a rotating magnetic field and hydraulic unit. By intelligently controlling these two motions, a uniform smooth mirror-like finished surface with improved material removal rate and finishing rate (nanometer per cycle) is achieved for both stainless steel and brass workpieces. From the preliminary experiments, it is found that R-MRAFF process produces better results than MRAFF. Experiments have been planned using design of experiments technique. Analysis of variance is conducted to find out the contribution of each model term affecting percent improvement in surface finish. The optimum finishing conditions are identified from optimization study. The present study shows that the combinations of rotational speed of the magnet and its square term together have the highest contribution to the percentage improvement in surface roughness. Other significant parameters in the order of decreasing percent contribution to the change in surface roughness value are finishing cycles, extrusion pressure, and fluid composition. The best surface finish obtained on stainless steel and brass workpieces with R-MRAFF process are 110 and 50 nm, respectively. From the scanning electron micrographs and atomic force micrographs, it has been observed that the abrasive cutting marks generate cross-hatch pattern on the surface finished by R-MRAFF process.
TL;DR: In this paper, a survey of different mathematical models, including quadratic assignment, Quadratic set covering, mixed integer programming, and graph theoretic models, the various solution methods especially intelligent approaches along with their advantages and disadvantages are surveyed.
Abstract: Facility layout problem is associated with the arrangement of facilities in a plant. It is a critical issue in the early stages of designing a manufacturing system because it affects the total manufacturing cost significantly. Dynamic and robust layouts are flexible enough to cope with fluctuations and uncertainties in product demands in volatile environment of flexible manufacturing systems. Since the facility layout is a hard combinatorial optimization problem, intelligent approaches are the most appropriate methods for solving the large size of this problem in reasonable computational time. In this paper, first of all, dynamic and robust layouts are surveyed. After a quick look of different mathematical models, including quadratic assignment, quadratic set covering, mixed integer programming, and graph theoretic models, the various solution methods especially intelligent approaches along with their advantages and disadvantages are surveyed. Finally, after review of hybrid algorithms, the conclusion of this paper is reported.
TL;DR: In this paper, the 30 criteria based leanness assessment methodology using fuzzy logic has been used to overcome the disadvantages with scoring method such as impreciseness and vagueness.
Abstract: Nowadays manufacturing organizations are moving towards lean practices to keep pace with the competition. Lean practices mainly focuses on eliminating seven deadly wastes, thereby reducing the cost. Leanness is the measure of lean manufacturing practices. This paper presents the 30 criteria based leanness assessment methodology using fuzzy logic. Fuzzy logic has been used to overcome the disadvantages with scoring method such as impreciseness and vagueness. During this research, a conceptual model for leanness assessment has been designed. Then the fuzzy leanness index which indicates the leanness level of the organization and fuzzy performance importance index which helps in identifying the obstacles for leanness has been computed. The results indicate that the model is capable of effectively assessing leanness and has practical relevance.
TL;DR: In this paper, a laser-based machine vision system is developed and implemented to monitor and control welding processes, which consists of three main modules: a laserbased vision sensor module, an image processing module, and a multi-axis motion control module.
Abstract: In this study, a laser-based machine vision system is developed and implemented to monitor and control welding processes. The system consists of three main modules: a laser-based vision sensor module, an image processing module, and a multi-axis motion control module. The laser-based vision sensor is designed and fabricated based on the principle of laser triangulation. By developing and implementing a new image processing algorithm on the platform of LabVIEW, the image processing module is capable of processing the images captured by the vision sensor, identifying the different types of weld joints, and detecting the feature points. Based on the detected feature points, the position information and geometrical features of the weld joint such as its depth, width, plates mismatch, and cross-sectional area can be obtained and monitored in real time. Meanwhile, by feeding these data into the multi-axis motion control module, a non-contact seam tracking is achieved by adaptively adjusting the position of the welding torch with respect to the depth and width variations of the weld joint. A 3D profile of the weld joint is also obtained in real time for the purposes of in-process weld joint monitoring and post-weld quality inspection. The results indicate that the developed laser-based machine vision system can be well suited for the measurement of weld joint geometrical features, seam tracking, and 3D profiling.
TL;DR: In this article, SiO2 nanoparticles are mixed with ordinary mineral oil having 0.2% weight concentration and a proper sonification method is used to mix and suspend the particles thoroughly and efficiently.
Abstract: Manufacturing industries have been pressured to use less power and reduce pollution by the development of power-efficient and pollution-preventing policies from the government. However, quality and cost are of main concern in this agenda. In machining, the key solution for this issue is by increasing the effectiveness of existing lubrication systems as this reduces the power required to overcome the friction component in machining processes for less fuel consumption and pollution. In machining processes, in particular, improved lubrication systems will increase batch production rates with better product quality. Introducing nanolubrication reduces power consumption as the rolling action of a billion units of nanoparticles in the tool chip interface decreases the cutting forces significantly. Additionally, using nanolubrication in machining minimizes the consumption of the lubrication oil, which decreases pollution. Detailed analysis and implementation of nanolubrication in machining process with the proper parameter setup are mandatory to ensure the efficiency of implementing nanolubrication. In this research, SiO2 nanoparticles are mixed with ordinary mineral oil having 0.2% weight concentration. A proper sonification method is used to mix and suspend the particles thoroughly and efficiently. The results show a reduction in the coefficient of friction in the tool/chip interface. Hence, the cutting force and working power are reduced considerably compared with conventional lubrication systems. Consequently, considerable power savings, less oil consumption, and less pollution are achieved.
TL;DR: In this article, surface quality generated under high speed finishing turning conditions on age-hardened Inconel 718 with focus on surface roughness, metallographic analysis of surface layer and surface damages produced by machining.
Abstract: Inconel 718 is known to be among the most difficult-to-machine materials due to its special properties which cause the short tool life and severe surface damages. The properties, which are responsible for poor machinability, include rapid work hardening during machining; tendency to weld with the tool material at high temperature generated during machining; the tendency to form a built-up edge during machining; and the presence of hard carbides, such as titanium carbide and niobium carbide, in their microstructure. Conventional method of machining Inconel 718 with cemented carbide tool restricts the cutting speed to a maximum 30 m/min due to the lower hot hardness of carbide tool, high temperature strength and low thermal conductivity of Inconel 718. The introduction of new coated carbide tools has increased cutting speed to 100 m/min; nevertheless, the time required to machine this alloy is still considerably high. High speed machining using advanced tool material, such as CBN, is one possible alternative for improving the productivity of this material due to its higher hot hardness in comparison with carbide tool. This paper specifically deals with surface quality generated under high speed finishing turning conditions on age-hardened Inconel 718 with focus on surface roughness, metallographic analysis of surface layer and surface damages produced by machining. Both coated and uncoated CBN tools were used in the tests, and a comparison between surfaces generated by both tools was also discussed.
TL;DR: In this article, the geometrical description of roughness profile is reported, by means of an analytic formulation, it is possible to calculate custom set of coarse-grained roughness parameters.
Abstract: Fused deposition modelling is an established additive manufacturing technique for creating functional prototypes from three-dimensional computer-aided design models. Despite of the potential advantages of this technology, surface roughness is a substantial problem and some attempts have been made to predict the average roughness Ra. As well known, the surface quality characterization of a part is not limited to Ra but involves many other roughness parameters. The knowledge in advance of these parameters is a critical point especially in the product design stage both for rapid prototyping purpose and finished part manufacturing. In this work, a novel approach aimed to the geometrical description of roughness profile is reported. By means of an analytic formulation it is possible to calculate custom set of roughness parameters. An experimental analysis, based on design of experiments technique, is carried out to investigate the effects of several factors on shape profile. The achieved results permit to define the domain in which the presented model depends only on two parameters. A profilometric analysis has been performed on specimens properly designed to validate the method. Statistical tests show a good accordance with predicted values for the made assumptions and the calculated roughness parameters.
TL;DR: In this article, a tool wear monitoring strategy based on a large number of signal features in the rough turning of Inconel 625 was presented, which were extracted from time domain signals as well as from frequency domain transforms and their wavelet coefficients (time-frequency domain).
Abstract: This paper presents a tool wear monitoring strategy based on a large number of signal features in the rough turning of Inconel 625. Signal features (SFs) were extracted from time domain signals as well as from frequency domain transforms and their wavelet coefficients (time–frequency domain). All of them were automatically evaluated regarding their relevancy for tool wear monitoring based on a determination coefficient between the feature and its low-pass-filtered course as well as the repeatability. The selected SFs were used for tool wear estimation. The accuracy of this estimation was then used to evaluate the sensor and signal usability.
TL;DR: Results show that both SGNN and PGNN perform better than the traditional grey model and ANN in terms of prediction accuracy and robustness, so the new models are more suitable for complex working conditions in industrial applications.
Abstract: This paper proposes a novel modeling methodology for machine tool thermal error. This method combines the advantages of both grey model and artificial neural network (ANN) in terms of data processing. To enhance the robustness and the prediction accuracy, two kinds of grey neural network, namely serial grey neural network (SGNN) and parallel grey neural network (PGNN), are proposed to predict the thermal error. Experiments on the axial directional spindle deformation on a five-axis machining center are conducted to build and validate the proposed models. The results show that both SGNN and PGNN perform better than the traditional grey model and ANN in terms of prediction accuracy and robustness. So the new models are more suitable for complex working conditions in industrial applications.
TL;DR: In this article, a hybrid method including response surface methodology (RSM) and back-propagation neural network (BPNN) integrated simulated annealing algorithm (SAA) were proposed to determine an optimal parameter setting.
Abstract: This study analyzes variations in metal removal rate (MRR) and quality performance of roughness average (Ra) and corner deviation (CD) depending on parameters of wire electrical discharge machining (WEDM) process in relation to the cutting of pure tungsten profiles. A hybrid method including response surface methodology (RSM) and back-propagation neural network (BPNN) integrated simulated annealing algorithm (SAA) were proposed to determine an optimal parameter setting. The results of 18 experimental runs via a Taguchi orthogonal table were utilized to train the BPNN to predict the MRR, Ra, and CD properties. Simultaneously, RSM and SAA approaches were individually applied to search for an optimal setting. In addition, analysis of variance was implemented to identify significant factors for the processing parameters. Furthermore, the field-emission scanning electron microscope images show that a lot of built-edge layers were presented on the finishing surface after the WEDM process. Finally, the optimized result of BPNN with integrated SAA was compared with that obtained by an RSM approach. Comparisons of the results of the algorithms and confirmation experiments show that both RSM and BPNN/SAA methods are effective tools for the optimization of parameters in WEDM process.