The fused filament fabrication (FFF) process of polymer-based composites has been developed due to its capability to make complex geometries and shapes with reasonable mechanical properties. However, the improvement of mechanical properties of the obtained parts and products are still under study and are interesting for designers. There are several strategies to enhance these desired properties of produced pieces, for example optimizing the process parameters and/or using different architecting designs. This paper presents the effect of some overriding process parameters (liquefier temperature, print speed, layer height, and platform temperature) on the temperature evolution and mechanical behavior of PA6 reinforced with chopped carbon fibers produced by FFF. Due to deposition of multilayers, there is a cyclic profile of temperature in the FFF process, which is a considerable note related to fabrication and consequently the strength of the manufactured parts. In parallel with the study of process parameters effect, this cyclic temperature profile has been measured. The preliminary results related to physicochemical and mechanical properties revealed that differences in crystallinity percentage exist and failure stress/strain can be considered as an indicator to evaluate the mechanical properties of FFF manufactured products. Moreover, measuring the temperature profile of the deposited filaments revealed that process parameters have a considerable influence on the cooling process of deposited filaments which itself affects the bonding of adjacent filaments. The higher temperatures led to slower cooling rate. Finally, the results confirm the impact of mentioned parameters roles on the bonding formation in the FFF process and also the subsequent obtained mechanical properties of the printed parts. Therefore, selection of the optimized and suitable process parameters is an important design consideration.

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In this work, the rotational molding process is developed to manufacture in one piece an axial-flow turbomachine rotor with hollow blades. Giving to our knowledge, this process has never been employed in the making of these turbomachine components. Indeed, the blades of these rotors are typically solid blades and are making by injection molding, machining, or thermoforming. The effects of three relevant factors of the rotational molding process are studied here: oven temperature, time of heating, and cooling rate. The cooling of the moving mold is managed by simple convection-of-air, or by convection-of-air charged with water particles. For the oven temperature of 285°C, hollow-blades rotors of good quality are gotten in 12 min per cycle. In addition, aerodynamic characteristics of one rotational molded rotor are compared to those of another part machined in aluminum piece. Characteristics of this aluminum rigid-rotor are assumed as reference.

The use of additive manufacturing has been widely developed in the industry due to its ability to make complex shapes. The use of reinforcing fibers has provided a wider design capability in this field. Due to the effect of the number of fibers reinforced used on the mechanical properties, the study of the obtained mechanical properties is of great importance. This paper presents the experimental findings of tensile loading and three points bending fatigue tests performed on polymer-based composites (Onyx (which is CF-PA6) and reinforced Onyx with continuous glass fiber (CF-PA6 + GF) using Fused Filament Fabrication. Tensile properties of various types of printing conditions (Solid, Triangular, Rectangular, and Hexagonal fill patterns) have been compared. The coupled frequency amplitude affects the nature of the overall fatigue response which can be controlled by the damage mechanisms accumulation and/or by the self-heating. For fatigue loading, self-heating has been observed and yielded a temperature rise to about 60°C which is more than the glass transition temperature of the polymer. Multi-scale damage analysis of the sample in fatigue showed that the first observed damage phenomenon corresponds to the debonding of the filaments which leads to the propagation of transverse cracks.

Fused filament fabrication (FFF) is a layer-by-layer additive manufacturing (AM) process for producing parts. For industries to gain a competitive advantage, reducing product development cycle time is a basic goal. As a result, industries’ attention has turned away from traditional product development processes toward rapid prototyping techniques. Because different process parameters employed in this method significantly impact the quality of FFF manufactured parts, it is essential to optimize FFF process parameters to enhance component quality. The paper presents optimization of fused filament fabrication process parameters to improve the shape deviation such as cylindricity and circularity of 3D printed parts with the Taguchi optimization method. The effect of thickness, infill pattern, number of walls, and layer height was investigated as variable parameters for experiments on cylindricity and circularity. The MarkForged® used Nylon White (PA6) to create the parts. ANOVA and the S/N ratio are also used to evaluate and optimize the influence of chosen factors. As a result, it was concluded that the hexagonal infill pattern, the thickness of 5 mm, wall layer of 2, and a layer height of 1.125 mm were known to be the optimal process parameters for circularity and cylindricity in experiments. Then a linear regression model was created to observe the relationship between the control variables with cylindricity and circularity. The results were confirmed by a confirmation test.

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The level of industrial performance is a vital issue for any company wishing to develop and acquire more market share. This article presents a novel approach to integrate intelligent visual inspection into “MES” control systems in order to gain performance. The idea is to adapt an intelligent image processing system via in-situ cameras to monitor the production system. The images are thus analyzed in real time via machine learning interpreting the visualized scene and interacting with some features of the MES system, such as maintenance, quality control, security, operations, etc. This novel technological brick, combined with the flexibility of production, contributes to optimizing the system in terms of autonomy and responsiveness to detect anomalies, already encountered, or even new ones. This smart visual inspection system is considered as a Cyber Physical System CPS brick integrated to the manufacturing system which will be considered an edge computing node in the final architecture of the platform. This smart CPS represents the 1st level of calculation and analysis in real time due to embedded intelligence. Cloud computing will be a perspective for us, which will represent the 2nd level of computation, in deferred time, in order to analyze the new anomalies encountered and identify potential solutions to integrate into MES. Ultimately, this approach strengthens the robustness of the control systems and increases the overall performance of industrial production.

This article describes a new interactive design approach integrating the constraints associated with production include manufacturing and assembling. The proposed method, in the form of an algorithm, allows optimisation of product design by minimizing production costs at each iteration, without compromising its functionality. The novelty of this algorithm in terms of modeling and optimisation of production costs in the design phase is its ability to dynamically evaluate the cumulative costs of production as a function of design and procedural choices. The availability of this information first allows the identification of design points and/or procedural points that generate significant production costs, and second, suggests improvements and recommendations that aim to optimize production costs. These experiments were conducted at a smart factory installed in our organisation. The proposed algorithm involves four steps. To optimise production costs, the designer must input all of the required data into the simulation and thereby identify the most significant cost elements to optimise. Then, the designer uses the suggested recommendation list to modify the relevant design and/or manufacturing parameters, thus obtaining the new, optimised production costs. If the first result is unsatisfactory, other iterations can be performed.