This paper investigates the effect of different additive manufacturing process parameters such as chamber temperature, Printing temperature, layer thickness, and print speed on five essential parameters that characterize the manufactured components: cylindricity, circularity, strength, and Young’s modulus, and deformation by Gray Relational Analysis method simultaneously. Taguchi method was used to design the experiments, and the PA6 cylindrical parts were fabricated using a German RepRap X500® 3D printer. Then the Gray Relational Grade (GRG) values were calculated for all experiments. In the 8th trial, the highest value of GRG was observed. Then, to discover the optimal parameters, the GRG data were analyzed using ANOVA and S/N analysis, and it was determined that the best conditions for enhancing GRG are 60 °C in the chamber temperature, 270 °C in the printing temperature, 0.1 mm layer thickness, and 600 mm/min print speed. Finally, by using optimal parameters, a verification test was performed, and new components were investigated. Finally, comparing the initial GRG with the GRG of the experiments showed an improvement in the gray relational grade (14%) which is accompanying with improving of GRG value.

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.

The demand for efficient Industry 4.0 systems has driven the need to optimize production systems, where effective scheduling is crucial. In smart manufacturing, robots handle material transfers, making precise scheduling essential for seamless operations. However, research often oversimplifies the Robotic Flexible Job Shop problem by focusing only on transportation time, ignoring resource allocation and robot diversity. This study addresses these gaps, tackling a Multi-Robot Flexible Job Shop (MRFJS) scheduling problem with limited buffers. It involves non-identical parallel machines and robots with varying capabilities overseeing material handling under blocking conditions. The case study is based on a real Industry 4.0 scenario, where the layout restricts each robotic arm’s access, requiring strategic buffer placement for part transfers. A Mixed-Integer Programming (MILP) model aims to minimize makespan, followed by a new Genetic Algorithm (GA) using Roy and Sussman’s Alternative Graph. Computational tests on various scales and real data from a manufacturing plant demonstrate the GA’s efficacy in solving complex scheduling problems in real-world production settings. Based on the data, the Proposed Genetic Algorithm (PGA), with an average Relative Deviation (ARD) of 0.25%, performed approximately 34% better compared to the Basic Genetic Algorithm (BGA), with an average ARD of 0.38%. This percentage indicates that the PGA significantly outperforms in solving complex scheduling problems.

One of the most widely used additive manufacturing (AM) methods is Fused Filament Fabrication (FFF), which can produce complex geometry parts. In this process, a continuous filament of thermoplastic material is deposited layer by layer to make the final piece. One of the essential goals in the production of parts with this method is to produce parts with high mechanical properties and excellent geometrical accuracy at the same time. Among the various methods used to improve the desired properties of produced parts is to determine the optimum process parameters in this process. This paper investigates the effect of different process parameters on four essential parameters: chamber temperature, Printing temperature, layer thickness, and print speed on cylindricity, circularity, strength, Young’s modulus, and deformation by Gray Relational Analysis method simultaneously. Taguchi method was used to design the experiments, and the PA6 cylindrical parts were fabricated using a German RepRap X500^® 3D printer. Then the GRG values were calculated for all experiments. In the 8^th trial, the highest value of GRG was observed. Then, to discover the optimal parameters, the GRG data were analyzed using ANOVA and S/N analysis, and it was determined that the best conditions for enhancing GRG are 60 °C in the chamber temperature, 270 °C in the printing temperature, 0.1 mm layer thickness, and 600 mm/min print speed. Finally, by using optimal parameters, a verification test was performed, and new components were investigated. Finally, by comparing the initial GRG with the GRG of the experiment, it was discovered that the GRG value had improved by 14%.