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Metal additive manufacturing is an active field of innovation. However, for laser power bed fusion (LPBF), supports removal is a major constraint. In this technology, supports are strongly welded to the part to tightly maintain it, avoid distortion, and evacuate thermal load. Although supports are usually optimized for manual removal, machining is often necessary, which can affect post-processing productivity. This paper proposes a comprehensive methodological approach to optimize the selection of cutting parameters, cutting tools, and support structure for LPBF. The aim is to help additive manufacturers find supports that reduce machining costs in terms of time and cutting tool degradation, from among the numerous support designs available. This approach can also optimize the design of lattice structures used inside parts. Our results show that among the 11 designs tested, honeycomb and squared pattern grid supports are the most efficiently machined using the 8-teeth tangential milling of the 3 tools tested, with a good post-machined surface roughness and tools' health. The method considers low magnification optical analysis and an accelerometer sensor, which is easy to use even for small- and medium-sized enterprises. This paper also proposes and analyzes a new kind of porous support using this method. [ABSTRACT FROM AUTHOR]

The stability of the injection molding process is critical to efficient production. However, environment and production disturbances pose challenges to maintaining a stable molding process. A two-stage dynamic adjustment strategy is proposed in this paper to increase weight consistency, considering fluctuation of density and fluidity based on products' types and structure. This study investigates the factors influencing the weight stability of injection molding products during the injection and holding stages. Experiments show that the injection stage is affected by density fluctuations, while melt fluidity fluctuations influence the holding stage. Simulation experiments prove the efficacy of dynamic adjustment strategies for injection-sensitive products (ISP) and holding-sensitive products (HSP). Thinner products as ISPs benefit from adjustments of the injection stage parameters, while thicker products as HSPs require optimization of holding stage parameters. To prove its validity, this strategy was tested on two molds and improved weight stability by 60.0% and 34.8%, respectively. [ABSTRACT FROM AUTHOR]

Energy efficiency is a challenging and relevant research field in modern manufacturing industries, where robotic systems play an essential role in the automation of several industrial operations. In this paper, we present an approach for the energy-efficiency optimization of a 3-DOF parallel robot. The proposed strategy leverages the task placement, the execution time, and the length of the robot lower arms to minimize the energy consumption for the execution of a predefined high-speed pick-and-place operation. To evaluate the actuators energy consumption, the kinematic, dynamic and electro-mechanic mathematical models, as well as an equivalent multibody model, of the parallel robot are implemented. The results of extensive numerical simulations show that the proposed strategy provides notable improvements in the energy efficiency of the parallel robot, with respect to alternative approaches. Starting from a pick-and-place task with optimal task placement with a consumption of 38.2 J (with a cycle time of 0.4 s), the energy expenditure can be reduced to 3.75 J (with a cycle time of 1.86 s), with a reduction percentage of 90.2%, by additionally optimizing the execution time, and the length of the robot lower arms. These results lead to a reduction from 5733 J/min (for 150 cycles/min) to 121 J/min (for 32 cycles/min), allowing to choose the best trade-off between robot productivity and consumed energy. [ABSTRACT FROM AUTHOR]

As a surface-strengthening technique, shot peening increases the compressive residual stress in the workpiece. In this paper, how to obtain specific residual stresses to reduce blade deformation by controlling the shot peening parameters was investigated. Based on the cosine function describing the residual stress depth distribution, the equivalent residual stress was calculated. The effect of shot peening parameters (air pressure, flow rate, nozzle speed, and distance) on the equivalent residual stress was modeled. Then a finite element model of the equivalent residual stress-induced deformation of the blade was established, and the effect of equivalent residual stress on blade deformation was investigated via the model. The residual stress domains that make the deformation less than 0.03 mm were obtained by the inverse solution of the exhaustive method, then the optimized parameter of shot peening was also obtained, and the accuracy of the optimization method was verified by shot peening tests. This method can provide technical support for the control of deformation caused by shot peening of blades. [ABSTRACT FROM AUTHOR]

In the process of directed energy deposition-arc (DED-arc), the geometrical shape of weld bead in arc-striking (AS) and arc-extinguishing (AE) areas are usually inconsistent with that in the middle section, which directly affects the forming accuracy of component thus needs to be optimized. In this paper, high-speed photography is used to observe the molten pool behavior of nickel aluminum bronze (NAB) bead deposition, and the causes of abnormal bead shape in the AS and AE areas are analyzed. On that basis, an optimization method for AS and AE is put forward. In the AS area, the welding current and travel speed are increased. And after the arc strikes, a hovering time is used during which the torch hovers at the AS point. In the AE area, the welding current is gradually reduced while the torch keeps hovering at the AE point. Then, the optimization effect of AS parameters on the bead shape are explained through factorial experiments, and the optimization effects of AE parameters are explained through orthogonal experiments. At the same time, the optimal parameter combination of AS and AE is obtained. With the proposed method and the optimal parameters, the height and width deviation of the deposited single bead are controlled within 5%. After adopting the optimization method, the effective height ratio of the DED-arc–manufactured NAB thin wall is increased from 67.5% to 91.5% under the unidirectional deposition path, which is also increased from 88.9% to 98.3% under the bidirectional deposition path. [ABSTRACT FROM AUTHOR]

Currently, there is a notable attraction within the industry towards biocomposites, driven by the increasing fascination with natural fiber-reinforced composites (NFRCs). These NFRCs offer remarkable benefits, including cost-effectiveness, biodegradability, eco-friendliness, and favorable mechanical properties. As a result, the manufacturing processes of natural fiber reinforced polymer (NFRP) composites have garnered attention from both industrial professionals and scientists. The emergence of these eco-friendly materials in the automotive and aerospace industries has sparked interest in understanding their production techniques. However, the machining processes of NFRP composites pose significant challenges due to the complex structure of natural fibers, necessitating thorough studies to address these issues effectively. This research paper presents a comprehensive investigation on surface roughness during the milling process of Alfa/epoxy biocomposites. A set of 100 experimental trials was conducted to test the surface roughness, and analysis of variance (ANOVA) was used to assess the impact of cutting parameters and chemical treatment on surface quality. To develop a predictive model for surface roughness, a hybrid approach called ANN-GA (artificial neural networks-genetic algorithms) is proposed in this research. This approach combines ANN and GA to determine an optimal neural network architecture. The performance of the ANN-GA model is compared to the Levenberg–Marquardt backpropagation (LM) algorithm. ANOVA results show that the feed per revolution have a significant influence on surface roughness, followed by the chemical treatment of fibers, while machining direction has a smaller effect. The ANN-GA model demonstrates good accuracy in surface roughness prediction compared to the LM algorithm. [ABSTRACT FROM AUTHOR]

Titanium alloys are categorized under difficult to machine materials. The machinability of titanium alloy Ti6Al4V using statistical methods such as analysis of variance is investigated in this paper. Titanium alloy Ti6Al4V is the most widely used in aerospace and biomedical application due to its advantageous material properties. However, despite its wide-ranging applications, there is a lack of clarity concerning its ideal machining parameters. This ambiguity primarily stems from titanium alloys’ inherent properties, notably their low thermal conductivity and high chemical reactivity. Understanding and optimizing the machining parameters to get the right combination of speed, feed, depth of cut, and coolant condition are vital. Furthermore, to decipher the collected data and interpret the results, analysis of variance techniques were utilized with the help of software R-programming. The insights garnered can lead to more streamlined machining strategies, ensuring higher productivity and efficiency. By bridging the knowledge gap, this research seeks to make machining titanium alloys simpler, cost-effective, and more efficient for manufacturers. The paper output shows that the mean square values range from nearly zero for cutting speed, feed rate, and depth of cut to around 11.817 for coolant respectively. The paper concludes with the various P-values obtained for the cutting parameters influencing the surface roughness using the analysis of variance technique. The effect of coolant on the surface roughness has been significant with a P-value of 0.000117. [ABSTRACT FROM AUTHOR]

Optimization of industrial processes is a constant concern and a main task for many companies in several industry sectors. Based on optimized solutions, the company can decrease manufacturing costs, lead times or, more generally, increase benefit. However, when developing a new process, you must be sure that the new solution will add more value to the initial solution. This, among many others, is one of the main reasons why companies opt, in addition to experimentation, for the use of tools that allow for the virtual prototyping, simulation, and integration of complex and sophisticated processes. Experimentation requires approximation testing, searching for the ideal values of quantity, temperature, etc.; simulation provides industrial processes with test support ranging from flows to robotic systems such as the intelligent software. This simulation software allows to develop programs, add Computer Aided Design (CAD) layouts, and simulate the cycle times of the different tasks to be performed. In this paper, an experimental approach is presented with atomization and fan spray pressure as the study basis and a simulation of the robot-based handling solution used in a tyre production line. The result is an improvement of the painting process, optimisation of the amount of fluid deposited on the tyre, of the processed product, as well as of the running times of the robot. [ABSTRACT FROM AUTHOR]