Journal of Manufacturing and Materials Processing — Open Access Journal

This article elucidates the characteristics of machining forces (an important phenomenon by which machining is studied) using three sets of bimetallic specimens made of aluminum–titanium, aluminum–cast iron, and stainless steel–mild steel. The cutting, feed, and thrust forces were recorded for different cutting conditions (i.e., different cutting speeds, feeds, and cutting directions). Possibility distributions were used to quantify the uncertainty associated with machining forces, which were helpful in identifying the optimal machining direction. In synopsis, it was found that while machining the steel-based bimetallic specimens, keeping a low feed and high cutting speed is the better option, and the machining operation can be performed in both the hard-to-soft and soft-to-hard material directions, but machining in the soft-to-hard material direction is the better option. On the other hand, very soft materials should not be used in fabricating a bimetallic part because it creates machining problems. Cutting power was estimated using the cutting and feed force signals. Manufacturers who support sustainable product development (including design, manufacturing, and assembly) can benefit from the outcomes of this study because parts/products made of dissimilar materials (or multi-material objects) are better than their mono-material counterparts in terms of sustainability (cost, weight, and CO₂ footprint). Full article

Machining grooves into the surface of pine and fir (Abies spp.) deckboards reduces undesirable checking that develops when “profiled” boards are exposed to the weather. We aim to develop improved profiles for Douglas fir, western hemlock and white spruce decking to reduce their susceptibility to checking, and understand how profile geometry influences the stresses that cause checking. We varied the width and depth of grooves in profiled deckboards, exposed deckboards to the weather, and measured checking and cupping of boards. A numerical model examined the effect of groove depth on the moisture-induced stresses in profiled spruce boards. Profiling significantly reduced checking, but increased cupping of deckboards made from all three species. Western hemlock checked more than the other two species. Profiles with narrow grooves (rib profiles) were better at restricting checking than profiles with wider grooves. A rib profile with deeper grooves developed smaller stresses than a rib profile with shallower grooves, and boards with the former profile checked less than boards with shallower grooves. We conclude that checking of profiled Douglas fir, western hemlock and white spruce decking is significantly reduced by changing profile geometry, and our results suggest the best profiles to reduce checking of all three species. Full article

The manufacturing industry aims to produce many high quality products efficiently at low cost, thereby motivating companies to use advanced manufacturing technologies. The use of high-speed machining is increasingly widespread; however, it lacks a deep-rooted knowledge base needed to facilitate implementation. In this paper, response surface methodology (RSM) has been applied to determine the optimum cutting conditions leading to minimum flank wear in high-speed dry turning on AISI 1045 steel. The mathematical models in terms of machining parameters were developed for flank wear prediction using RSM on the basis of experimental results. The high speed turning experiments were carried out with two coated carbide and a cermet inserts using AISI 1045 steel as work material at different cutting speeds and machining times. The models selected for optimization were validated through the Pareto principle. Results showed the GC4215 insert to be the most optimal option, because it did not reach the cutting tool life limit and could be used for the whole range of cutting parameters selected. To quantitatively evaluate the usefulness of the cutting tools, it was proposed the coefficient of use of the tools from the results of the contour graphs. The GC4215 insert showed 100% effectiveness, followed by the GC4225 with 98.4%, and finally, the CT5015 insert with 83%. Full article

The sustainability of a manufacturing process can be measured by three main factors which impact both ecological and financial constraints. These factors are the energy required to achieve a specific job, the material utilized for the job, and the time taken to complete that job. These factors have to be quantified and analysed so that a proper manufacturing system can be designed to optimize process sustainability. For this purpose, a computer package, which utilizes life cycle inventory models has been presented for CNC (Computer Numerical Control) milling and turning processes. Based on utilization of resources and production stages, the job completion time for the turning and milling processes can be divided into process (i.e., machining), idle and basic times. As parameters are different for evaluating the process times, i.e., depth and width of cut in case of milling, initial and final diameters for turning, two different case studies are presented, one for each process. The effect of material selection on the sustainability factors has been studied for different processes. Our simulations show that highly dense and hard materials take more time in finishing the job due to low cutting speed and feed rates as compared to soft materials. In addition, face milling takes longer and consumes more power as compared to peripheral milling due to more retraction time caused by over travel distance and lower vertical transverse speeds than the horizontal transverse speed used in a peripheral retraction process. Full article

Additive manufacturing (AM) has developed rapidly since its inception in the 1980s. AM is perceived as an environmentally friendly and sustainable technology and has already gained a lot of attention globally. The potential freedom of design offered by AM is, however, often limited when printing complex geometries due to an inability to support the stresses inherent within the manufacturing process. Additional support structures are often needed, which leads to material, time and energy waste. Research in support structures is, therefore, of great importance for the future and further improvement of additive manufacturing. This paper aims to review the varied research that has been performed in the area of support structures. Fifty-seven publications regarding support structure optimization are selected and categorized into six groups for discussion. A framework is established in which future research into support structures can be pursued and standardized. By providing a comprehensive review and discussion on support structures, AM can be further improved and developed in terms of support waste in the future, thus, making AM a more sustainable technology. Full article

A physics-based analytical model is proposed in order to predict the temperature profile during metal additive manufacturing (AM) processes, by considering the effects of temperature history in each layer, temperature-sensitivity of material properties and latent heat. The moving heat source analysis is used in order to predict the temperature distribution inside a semi-infinite solid material. The laser thermal energy deposited into a control volume is absorbed by the material thermodynamic latent heat and conducted through the contacting solid boundaries. The analytical model takes in to account the typical multi-layer aspect of additive manufacturing processes for the first time. The modeling of the problem involving multiple layers is of great importance because the thermal interactions of successive layers affect the temperature gradients, which govern the heat transfer and thermal stress development mechanisms. The temperature profile is calculated for isotropic and homogeneous material. The proposed model can be used to predict the temperature in laser-based metal additive manufacturing configurations of either direct metal deposition or selective laser melting. A numerical analysis is also conducted to simulate the temperature profile in metal AM. These two models are compared with experimental results. The proposed model also well captured the melt pool geometry as it is compared to experimental values. In order to emphasize the importance of solving the problem considering multiple layers, the peak temperature considering the layer addition and peak temperature not considering the layer addition are compared. The results show that considering the layer addition aspect of metal additive manufacturing can help to better predict the surface temperature and melt pool geometry. An analysis is conducted to show the importance of considering the temperature sensitivity of material properties in predicting temperature. A comparison of the computational time is also provided for analytical and numerical modeling. Based on the obtained results, it appears that the proposed analytical method provides an effective and accurate method to predict the temperature in metal AM. Full article

Industry 4.0 has been advertised for a decade as the next disruptive evolution for production. It relies on automation growth and particularly on data exchange using numerous sensors in order to develop faster production with tight monitoring. The huge amount of data generated by clouds of sensors during production is often used to feed machine learning systems in order to detect faults, monitor and find possible ways for improvement. However, the artificial intelligence within machine learning requires finding and selecting key features, such as average and root mean square. While current machine learning has already proven its use in diverse applications, its efficiency could be further improved by generating better characteristics such as fractal parameters. In this paper, fractal analysis concept is presented and its current and future applications in machining are discussed. This sensitive and robust technique is already extracting high performance key features that could fill in monitoring and prediction systems. On top of improving features selection and, thus, improving the overall performance of monitoring and predictive systems in machining, this could lead to a more rapid artificial intelligence implementation into manufacturing. Full article

The ability to produce consistent material properties across a single or series of platforms, particularly over time, is the major objective in metal additive manufacturing (MAM) research. If this can be achieved, it will result in widespread adoption of the technology for industry and place it into mainstream manufacturing. However, before this can happen, it is critical to develop an understanding of how processing parameters influence the thermal conditions which dictate the mechanical properties of MAM builds. Research work reported in the literature of MAM is generally based on a set of parameters and/or the review of a few parameter changes, and observing the effects that these changes (i.e., microstructure, mechanical properties) have. While these articles provide results with some insight, there lacks a standard approach that can be used to allow meaningful comparisons and conclusions to be made concerning the optimization of the processing variables. This study provides a template which can be used for making comparisons across DED platforms. The tests are performed with a design of experiments (DOE) philosophy directed to evaluate the effect of selected parameters on the measured properties of the DED builds. Specifically, a laser engineering net shaping system (LENS) is used to build multilayered 316L coupons and analyze how build parameters such as laser power, travel speed, and powder feed rate influence the thermal conditions that will define both microstructure and microhardness. A fundamental conclusion of this research is that it is possible to repeatedly obtain a consistent microstructure that contains a fine cellular substructure with a low level of porosity (less than 1.1%) and with microhardness that is equal to or better than wrought 316L. This is mainly achieved by maintaining an associated powder flow to travel speed ratio at the power level, ensuring an appropriate net heat input for the build process. Full article

Volumetric errors (VE) are related to the machine tool accuracy state. Extracting features from the complex VE data provides with a means to characterize this data. VE feature classification can reveal the machine tool accuracy states. This paper presents a study on how to use principal component analysis (PCA) to extract the features of VE and how to use the K-means method for machine tool accuracy state classification. The proposed data processing methods have been tested with the VE data acquired from a five-axis machine tool with different states of malfunction. The results indicate that the PCA and K-means are capable of extracting the VE feature information and classifying the fault states including the C axis encoder fault, uncalibrated C axis encoder fault, and pallet location fault from the machine tool normal states. This research provides a new way for VE features extraction and classification. Full article

The geometrical accuracy of multi-pass sheet metal spinning products is crucial in many applications. Aerospace, petroleum, and chemical industries motivated the development of modern spun components of complicated shapes with special functionality, but a substantial research lag exists behind this progress. Due to the localized plastic deformation involved, careful control of dimensions and form is required in spinning procedures. In this study, two sets of experiments were implemented for cup manufacturing using a retrofitted computer numerically controlled (CNC) spinning machine to identify the critical factors affecting product geometry and reveal their influence on the shape accuracy of the spun cups. The first set is a screening experiment to determine the most significant parameters and the second provides the optimum processing conditions affecting cup quality. The feed ratio, number of spin-forming passes, spinning ratio, and lubrication were found to have the most important effect on the geometry of the spun cups. Optimum quality with a higher processing speed (productivity) was achieved under a lubricated condition using a larger number of spin-forming passes at a high feed ratio, diminishing the commonly adopted rule of slow spinning for accurate products and reflecting a significance for state-of-the-art spinning practice. The findings of this paper introduce a basis for a spinning quality database. Full article

Transient liquid phase (TLP) bonding of 6063 aluminum alloy (Al-6063) and duplex alloy 2304 stainless steel (UNS S32304) was performed using copper foil as an interlayer between the base metals. A compression load was applied normal to the specimens. Metallurgical examination of the produced joints showed three distinct regions including a reaction zone, diffusion affected zone, and the base metals. The diffusion of copper into aluminum resulted in an Al–Cu eutectic structure. However, the oxide layer on the aluminum surface controlled the dissolution behavior of copper and the extent of its wettability with the base metals. Although voids and intermetallic compounds were detected at the interfaces of the processed joints, a defect free joint was produced at 570 °C. In addition, the results from corrosion tests showed that the use of copper as an interlayer decreased the corrosion resistance of the joints. However, increase in thickness of the joining reaction zone with increasing bonding temperature was observed to increase corrosion resistance. Full article

Poly-crystalline diamond (PCD) is an extremely tough, synthetically produced cutting tool material, which offers outstanding capabilities concerning wear behavior in abrasive cutting environments. Currently, the primary application of PCD cutting tools is the machining of non-ferrous materials, as the diamond’s carbon high affinity towards iron causes diffusion effects while cutting steel with rising temperature. This effect significantly reduces tool life. To lower the occurring temperature of the cutting process, and therefore avoid the reaction of carbon and iron, a thermal functionalization of the cutting inserts has been investigated. The results give insight into making PCD cutting tools economically usable for the machining of iron-carbon materials. Full article

Laser powder bed fusion (LPBF) is one of the most potent additive manufacturing (AM) processes. Metallic LPBF is gaining popularity, but one of the obstacles facing its larger industrial use is the limited knowledge of its dimensional and geometrical performances. This paper presents a metrological investigation of the geometrical and dimensional deviations of a selected LPBF-manufactured component, according to the ASME Y14.5-2009 standard. This approach allows for an estimation of both the process capability, as per ISO 22514-4 standard, and the correlations between the part location in the manufacturing chamber and the profile deviations. Forty-nine parts, which are representative of a typical aerospace tooling component (30 mm in diameter and 27.2 mm in height) were manufactured from AlSi10Mg powder using an EOSINT M280 printer and subjected to a stress relief annealing at 300 °C for two hours. This manufacturing procedure was repeated three times. A complete statistical analysis was carried out and the results of the investigation show that LPBF performances for all geometrical variations of 147 identical parts fall within a range of 230 µm at a 99.73% level. Full article

Laser Cladding is one of the leading processes within Additive Manufacturing technologies, which has concentrated a considerable amount of effort on its development. In regard to the latter, the current study aims to summarize the influence of the most relevant process parameters in the laser cladding processing of single and compound volumes (solid forms) made from AISI 316L stainless steel powders and using a coaxial nozzle for their deposition. Process speed, applied laser power and powder flow are considered to be the main variables affecting the laser cladding in single clads, whereas overlap percentage and overlapping strategy also become relevant when dealing with multiple clads. By setting appropriate values for each process parameter, the main goal of this paper is to develop a processing window in which a good metallurgical bond between the delivered powder and the substrate is obtained, trying simultaneously to maintain processing times at their lowest value possible. Conventional metallography techniques were performed on the cross sections of the laser tracks to measure the effective dimensions of clads, height and width, as well as the resulting dilution value. Besides the influence of the overlap between contiguous clads and layers, physical defects such as porosity and cracks were also evaluated. Optimum process parameters to maximize productivity were defined as 13 mm/s, 2500 W, 30% of overlap and a 25 g/min powder feed rate. Full article

Laser chemical machining (LCM) is a non-conventional processing method that enables a smooth and precise micro structuring of metallic surfaces. However, a high-quality removal is limited to a laser power window of some 100 mW. This is due to the high sensibility to removal disturbances, such as the deposition of metallic salts and oxides. In this work, the dynamic process behavior around the transition from a disturbance-free to a disturbed removal is investigated for the laser chemical machining of titanium (3.7024) and stainless steel (AISI 304) in different phosphoric acid solutions. Therefore, the removal cavities are recorded using confocal scanning microscopy and characterized regarding width, depth and quality in dependence of the laser power, feed velocity and electrolyte concentration. While the removal characteristics within the disturbance-free regime are found to be material-independent, the disturbed regime is strongly dependent on the tendency of the material to gas bubble adherence. Additional CCD records of the interaction zone reveal that the transition to the disturbed regime is accompanied by significant light reflections and thereby indicate the influence of adhering gas bubbles on disturbing the removal process. Moreover, typical removal disturbances are presented and discussed with regard to the responsible mechanisms for their occurrence. Full article

This study proposes using the iterative learning control method to adjust the volumetric error-compensated tool path, where the working volume motion accuracy of three-axis computerized numerical control (CNC) milling machine tools is increased by segmented modification of the part program. As the three-axis CNC milling machine tools generally have volumetric error of working volume, this study refers to the measured and established table of volumetric errors and uses the method of the modifying part program for volumetric error compensation of machine tools. This study proposes using part-program single-block positioning segmented for volumetric error compensation, as the generated compensated part program with multiple compensated blocks can effectively compensate the volumetric error of working volume in the tool moving process. In terms of the compensated tool path computing method, this study uses the iterative learning control (ILC) method and refers to compensated tool path and volumetric errors along the compensated tool path for iterative computation. Finally, a part program with multiple blocks is modified by the converged optimal compensated tool path, in order that the modified part program has higher-precision volumetric error compensation effect. The simulation result shows that the rate of improvement of error of the volumetric error compensation method proposed in this study is 70%. The result of cutting tests shows that the average rate of improvement of the straightness error of the test workpiece is 60%, while the average rate of improvement of height error is 80%. Therefore, the results of simulation and cutting tests can prove the feasibility of using the ILC method for segmented modification of the volumetric error-compensated part programs proposed in this study. Full article

The concept of Skin Model Shape has been introduced as a method for a close representation of manufactured parts using a discrete geometry representation scheme. However, discretized surfaces make irregular polyhedra, which are computationally demanding to model and process using the traditional implicit surface and boundary representation techniques. Moreover, there are still some research challenges related to the geometrical variation modelling of manufactured products; specifically, methods for geometrical data processing, the mapping of manufacturing variation sources to a geometric model, and the improvement of variation visualization techniques. To provide steps towards addressing these challenges this work uses Octree, a 3D space partitioning technique, as an aid for geometrical data processing, variation visualization, variation modelling and propagation, and tolerance analysis. Further, Skin Model Shapes are generated either by manufacturing a simulation using a non-ideal toolpath on solid models of Skin Model Shapes that are assembled to non-ideal fixtures or from measurement data. Octrees are then used in a variation envelope extraction from the simulated or measurement data, which becomes a basis for further simulation and tolerance analysis. To illustrate the method, an industrial two-stage truck component manufacturing line was studied. Simulation results show that the predicted Skin Model Shapes closely match to the measurement data from the manufacturing line, which could also be used to map to manufacturing error sources. This approach contributes towards the application of Octrees in many Skin Model Shape related operations and processes. Full article

Liquid Assisted Laser Beam Micromachining (LA-LBMM) process is an advanced machining process that can overcome the limitations of traditional laser beam machining processes. This research involves the use of a Molecular Dynamics (MD) simulation technique to investigate the complex and dynamic mechanisms involved in the LA-LBMM process both in static and dynamic mode. The results of the MD simulation are compared with those of Laser Beam Micromachining (LBMM) performed in air. The study revealed that machining during LA-LBMM process showed higher removal compared with LBMM process. The LA-LBMM process in dynamic mode showed lesser material removal compared with the static mode as the flowing water carrying the heat away from the machining zone. Investigation of the material removal mechanism revealed the presence of a thermal blanket and a bubble formation in the LA-LBMM process, aiding in higher material removal. The findings of this study provide further insights to strengthen the knowledge base of laser beam micromachining technology. Full article

Difficult-to-cut materials have been widely employed in many engineering applications, including automotive and aeronautical designs because of their effective properties. However, other characteristics; for example, high hardness and low thermal conductivity has negatively affected the induced surface quality and tool life, and consequently the overall machinability of such materials. Inconel 718, is widely used in many industries including aerospace; however, the high temperature generated during machining is negatively affecting its machinability. Flood cooling is a commonly used remedy to improve machinability problems; however, government regulation has called for further alternatives to reduce the environmental and health impacts of flood cooling. This work aimed to investigate the influence of dispersed multi-wall carbon nanotubes (MWCNTs) and aluminum oxide (Al₂O₃) gamma nanoparticles, on enhancing the minimum quantity lubrication (MQL) technique cooling and lubrication capabilities during turning of Inconel 718. Machining tests were conducted, the generated surfaces were examined, and the energy consumption data were recorded. The study was conducted under different design variables including cutting speed, percentage of added nano-additives (wt.%), and feed velocity. The study revealed that the nano-fluids usage, generally improved the machining performance when cutting Inconel 718. In addition, it was shown that the nanotubes additives provided better improvements than Al₂O₃ nanoparticles. Full article