The addition of the time dimension to three-dimensional (3D) printing has introduced four-dimensional (4D) printing technology, which has gained considerable attention in different fields such as medical, art, and engineering. Nowadays, bioscience has introduced some ideas which can be fulfilled by 4D printing. Blending time with variations caused by the situation has many beneficial aspects such as perceptibility and adaptability. Since 4D printing can create a dynamic structure with stimuli-responsive materials, the applications of smart materials, stimulus, and 3D printing are the effective criteria in 4D printing technology. Smart materials with their flexible properties can reshape, recolor, or change function under the effect of the internal or exterior stimuli. Thus, an attractive prospect in the medical field is the integration of the 4D printing approach along with smart materials. This research aims to show the most recent applications of 4D printing technology and smart materials in medical engineering which can show better prospective of 4D printing applications in the future. Also, it describes smart medical implants, tissue engineering, and bioprinting and how they are being used for the 4D printing approach in medical engineering applications. In this regard, a particular emphasis is dedicated to the latest progress in the innovation and development of stimuli-responsive materials that are activated and respond over time to physical, chemical, and biological stimuli and their exploitation through 3D printing methods to fabrication 4D printing smart parts such as intelligent tissue-engineered scaffolds, smart orthopedic implants, and targeted drug delivery systems. On the other hand, major challenges in this technology are explained along with some suggestions for future works to address existing limitations. It is worth noting that despite significant research that has been carried out into 4D printing, it might be more valuable if some investigation is done into 4D bio-printing applications and how this approach will be developed.

In this paper, the post-processing of 3D-printed poly lactic acid (PLA) parts is investigated. Workpieces are manufactured by fused deposition modeling (FDM) 3D printing, while they may have defects in some areas such as edges. A post-processing is introduced here for 3D-printed samples by low power CO₂ laser. The thickness of the FDM samples are 3.2 mm and printed by optimum conditions. Effects of process parameters such as focal plane position (−3.2–3.2 mm), laser power (20–40 W), and laser cutting speed (1–13 mm/s) are examined based on the design of experiments (DOE). Geometrical features of the kerf; top and bottom kerf; taper; ratio of top to the bottom kerf are considered as output responses. An analysis of the experimental results by statistical software is conducted to survey the effects of process parameters and to obtain regression equations. By optimizing of the laser cutting process; an appropriate kerf quality is obtained and also optimum input parameters are suggested. Experimental verification tests show a good agreement between empirical results and statistical predictions. The best optimum sample with 1.19 mm/s cutting speed, 36.49 W power and 0.53 mm focal plane position shows excellent physical features after the laser cutting process when 276.9 μm top and 261.5 μm bottom kerf width is cut by laser.

Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.

In this paper, the authors introduce the reader to the state of the art of Metal Wire Additive Manufacturing (MWAM) and provide a comparison between Wire Arc Additive Manufacturing (WAAM), Wire Laser Additive Manufacturing (WLAM), and Laser Arc Hybrid Wire Deposition (LAHWD) based on their characteristics and potential future applications, since MWAM is expected to have a promising future in various areas, such as aerospace, automotive, biomedical, and energy fields. A detailed discussion of the benefits and drawbacks of each Metal Wire Additive Manufacturing process can help to improve our understanding of the unique characteristics of metal wire application. Therefore, this paper offers a comprehensive analysis that can serve as a reference for upcoming industrial projects and research initiatives, with the aim of helping industries choose the most appropriate WAM technique for their specific applications.

In this paper, the influence of layer thickness (LT), infill percentage (IP), and extruder temperature (ET) on the maximum failure load, thickness, and build time of bronze polylactic acid (Br-PLA) composites 3D printed by the fused deposition modeling (FDM) was investigated via an optimization method. PLA is a thermoplastic aliphatic polyester obtained from renewable sources, such as fermented plant starch, especially made by corn starch. The design of experiment (DOE) approach was used for optimization parameters, and 3D printings were optimized according to the applied statistical analyses to reach the best features. The maximum value of failure load and minimum value of the build time were considered as optimization criteria. Analysis of variance results identified the layer thickness as the main controlled variable for all responses. Optimum solutions were examined by experimental preparation to assess the efficiency of the optimization method. There was a superb compromise among experimental outcomes and predictions of the response surface method, confirming the reliability of predictive models. The optimum setting for fulfilling the first criterion could result in a sample with more than 1021 N maximum failure load. Finally, a comparison of maximum failure from PLA with Br-PLA was studied.

Metastable austenitic stainless steels (MASS) are widely used in various industrial applications due to their exceptional compromise between mechanical properties and corrosion resistance. However, the mechanical properties of these materials can be further enhanced by surface treatments. This paper reviews various surface treatment methodologies used to improve the mechanical properties of MASS, with particular attention to laser treatments. The effects of these surface treatments on the microstructure and chemical composition in the thermal affected zone of the MASS are discussed, and their impact on the material’s mechanical properties, such as hardness, tensile strength, and fatigue life, are investigated in detail. Additionally, the paper highlights the limitations of these surface treatments and points out some areas where further research is needed. The findings presented can be used to guide the selection of appropriate surface treatment techniques for specific applications, ultimately improving the performance and lifespan of MASS in various industrial settings.