Journal of Manufacturing and Materials Processing — Open Access Journal

Keyhole laser welding experiments with 1.5 mm thick aluminum sheets (EN AW-6082) were carried out with transversal beam oscillation and wire feeding. A circular cavity, which was named buttonhole, formed directly behind the laser spot at certain oscillation frequencies. The welding states “no buttonhole”, “unstable buttonhole”, and “stable buttonhole” were distinguished. The melt pool dynamics were experimentally analyzed and correlated with the resulting roughness and waviness of the seam surfaces. Criteria for stable buttonhole welding were derived. On the basis of the cavity radii relations, it is shown that capillary pressure conditions can explain the movement of the buttonhole with the process. Full article

Remanufacturing is a viable option to extend the useful life of an end-of-use product or its parts, ensuring sustainable competitive advantages under the current global economic climate. Challenges typical to remanufacturing still persist, despite its many benefits. According to the European Remanufacturing Network, a key challenge is the lack of accurate, timely and consistent product knowledge as highlighted in a 2015 survey of 188 European remanufacturers. With more data being produced by electric and hybrid vehicles, this adds to the information complexity challenge already experienced in remanufacturing. Therefore, it is difficult to implement real-time and accurate remanufacturing for the shop floor; there are no papers that focus on this within an electric and hybrid vehicle environment. To address this problem, this paper attempts to: (1) identify the required parameters/variables needed for fuel cell remanufacturing by means of interviews; (2) rank the variables by Pareto analysis; (3) develop a casual loop diagram for the identified parameters/variables to visualise their impact on remanufacturing; and (4) model a simple stock and flow diagram to simulate and understand data and information-driven schemes in remanufacturing. Full article

Hand scraping is a manual surface finishing process that, despite its low productivity and high cost, is still applied in many industries because of its advantages concerning accuracy and tribology. In the presented microanalysis forces, movement patterns and tool orientation of individual hand scraping strokes were measured using a test stand, specifically designed for this purpose. It utilizes a camera, a three dimensional dynamometer, and an inertial measurement unit (IMU). The results show the basic characteristics of hand scraping. Typical courses of relevant quantities like cutting force, passive force, clearance, and directional angle are shown. In addition, the movement pattern of the tool during individual scraping strokes is analyzed. This research aims to contribute to a later implementation of automated scraping. The conducted research creates a base for future research regarding different scraping methods and achieved results. Full article

Flame spray pyrolysis, widely used in chemical industries, is a technology to synthesize nanoparticles. While the flame spray pyrolysis uses fuels as a solution liquid, the flame-assisted spray pyrolysis method uses aqueous solutions. Since process parameters such as concentration of precursor, size of droplets, and ratio of the air–gas mixture affect the size of nanoparticles, developing a flexible system to control these parameters is required. This paper proposes a new type of nozzle system to produce nanoparticles using flame-assisted spray pyrolysis. The annular nozzle design allows flexible control of particle flow and temperature, and an ultrasonic nebulizer was used to produce droplets with different size. Experiments were conducted to analyze the relationship between nanoparticle size and process parameters, concentration of precursor, frequency of the atomizer, and flame temperature. A precursor solution consisting of silver nitrate (AgNO₃) mixed in deionized water is used. The effects of the process parameters are discussed, and analysis of the nanoparticles shows that silver nanoparticles are deposited with an average size of 25~115 nm. Full article

This paper evaluates a physics-based analytical model in the prediction of machining temperature of AISI 1045 steel and AISI 4340 steel. The prediction model was developed based on the Johnson-Cook constitutive model (J-C model) and mechanics of the orthogonal cutting process. The average temperatures at two shear zones were predicted by minimizing the difference between calculated stresses using the J-C model and calculated stresses using the mechanics model. In this work, (1) the influence of input Johnson-Cook model constants, cutting force, and chip thickness on the accuracy of predictions are investigated with sensitivity analyses, in which multiple sets of available J-C constants and varying cutting force and chip thickness are used for the temperature prediction in machining AISI 1045 steel. The larger the input deviation, the larger prediction deviation. The temperature at the primary shear zone is more susceptible to the deviation of inputs than the temperature at the secondary shear zone. (2) The machining temperatures are also predicted in machining AISI 4340 steel using cutting tools with various specifications to demonstrate its predictive capability. Good agreements are observed upon validation to available experimental data in the literature. (3) Lastly, the advantage and limitation of the temperature model are discussed with comparison other analytical temperature models. Considering the reliable and easily measurable input requirements and sufficient predictive capability, this temperature model can be employed for effective and efficient machining temperature prediction. Full article

As an important energy conversion mechanism, centrifugal compressors play an important role in the national economy. The blade is one of the most critical components of the compressor. Damaged blades contain extremely high added value for remanufacturing. Thus, remanufacturing research on damaged and retired impeller/blade is getting more and more attention. Laser additive and milling subtractive composite remanufacturing technology is an effective means to achieve metal parts remanufacturing. In this paper, an advanced methodology for the remanufacturing of complex geometry and expensive components via reverse engineering, free-form surface modeling, laser additive repaired and machining is presented. The approach involves the integration of 3D non-contact digitization to obtain the point cloud data of damaged parts, adaptive free-form surface reconstruction to get the digital model of damage location, and laser additive manufacturing process containing slicing and path planning and subsequent multi-axis milling operation. The methodology has been successfully implemented on thin-curved centrifugal compressor blades. The results have shown that the composite remanufacturing method is an effective solution to realize the remanufacturing of damaged blades, and can be applied to the remanufacturing of other complicated parts. Full article

The new generation of ICT solutions applied to the monitoring, adaptation, simulation and optimisation of factories are key enabling technologies for a new level of manufacturing capability and adaptability in the context of Industry 4.0. Given the advances in sensor technologies, factories, as well as machine tools can now be sensorised, and the vast amount of data generated can be exploited by intelligent information processing techniques such as machine learning. This paper presents an online tool wear classification system built in terms of a monitoring infrastructure, dedicated to perform dry milling on steel while capturing force signals, and a computing architecture, assembled for the assessment of the flank wear based on deep learning. In particular, this approach demonstrates that a big data analytics method for classification applied to large volumes of continuously-acquired force signals generated at high speed during milling responds sufficiently well when used as an indicator of the different stages of tool wear. This research presents the design, development and deployment of the system components and an overall evaluation that involves machining experiments, data collection, training and validation, which, as a whole, has shown an accuracy of 78%. Full article

The knowledge of the loads occurring during a manufacturing process (e.g., grinding) and of the modifications remaining in the material is used in the concept of process signatures to optimize the manufacturing process and compare it with others (e.g., laser processing). The prerequisite for creating a process signature is that the loads can be characterized during the running process. Due to the rough process conditions, until now there is no in-process technique to measure the loads in the form of displacements and strains in the machined boundary zone. For this reason, the suitability of speckle photography is demonstrated for in-process measurements of material loads in a grinding process without cooling lubricant and the measurement results are compared with finite element method (FEM) simulations. As working hypothesis for the simulation it is assumed, that dry grinding is a purely thermally driven process. Despite the approximation by a purely thermal model with a constant heat source, the measured displacements differ only by a maximum of approximately 20% from the simulations. In particular, the strain measurements in feed speed direction are in good agreement with the simulation and support the thesis, that the dry grinding conditions used here lead to a primarily thermally affecting process. Full article

The main aim of this study is to determine the best process parameters for the robotized Gas Metal Arc Welding (GMAW) of LNE 700 advanced high-strength steel. This article evaluates some quality criteria such as the microhardness, the heat-affected zone (HAZ) and the convexity in the welded joints. The assays are performed using an experimental design, based on the Taguchi method. The analysis of the results identified some factors of greatest influence and how best to combine them to determine an optimum condition for welding LNE 700 high strength steel. Moreover, the influence of welding parameters on quality criteria is determined. Full article

One of the challenges of additive manufacturing (AM) technology is the inability to generate repeatable microstructure and mechanical properties in different orientations. In this work, the effect of build orientation on the microstructure and mechanical properties of Ti–6Al–4V specimens manufactured by selective laser melting (SLM) was studied. The samples built in the Z orientation showed weaker tensile strength compared to the samples built in X, and Y orientations. Samples built in X and Y orientations exhibited brittle fracture features in areas close to the substrate and ductile fracture features in the area farther from the substrate. Defects including pores, cracks, and unmelted/partially-melted powder particles contributed to lower tensile and fracture toughness properties in different orientations. Full article

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