Search results

This study aims to investigate how printing parameters affect the mechanical properties of specimens produced through fused filament fabrication, using the Erichsen test to assess deformation characteristics and material durability under stress.

Polylactic acid (PLA) specimens were printed and tested in accordance with the ISO 20482 standard. Definitive screening was conducted to identify the most influential process parameters. This study examined the effects of four key process parameters – number of layers, layer height, crossing angle and nozzle diameter – on force, distension, peak energy and energy to break. Each parameter was assessed at three levels and a large number of required experiments was managed by using response surface methodology (RSM).

This study revealed that the number of layers, layer height and crossing angle are the most significant factors that influence the mechanical properties of 3D-printed materials. The number of layers had the greatest impact on the peak force, contributing 44.25%, with thicker layers typically enhancing material strength. The layer height has a significant effect on energy absorption and deformation, with greater layer heights generally improving these properties. Nozzle diameter contributed only 1.10%, making it the least influential factor; however, its impact became more pronounced in interactions with other parameters.

This paper presents a comprehensive experimental investigation into the effects of process parameters on the crack strength and behavior of 3D-printed PLA specimens using the RSM method. The documented results can be used to develop optimization models aimed at achieving desired mechanical properties with reduced variation and uncertainty in the final product.

Adhesively bonded joints are used in many fields, especially in the automotive, marine, aviation, defense and outdoor industries. Adhesive bonding offers advantages over traditional mechanical methods, including the ability to join diverse materials, even load distribution and efficient thermal-electrical insulation. This study aims to investigate the mechanical properties of adhesively bonded joints, focusing on adherends produced with auxetic and flat surfaces adhered with varying adhesive thicknesses.

The research uses three-dimensional (3D)-printed materials, polyethylene terephthalate glycol and polylactic acid, and two adhesive types with ductile and brittle properties for single lap joints, analyzing their mechanical performance through tensile testing. The adhesion region of one of these adherends was formed with a flat surface and the other with an auxetic surface. Adhesively bonded joints were produced with 0.2, 0.3 and 0.4 mm bonding thickness.

Results reveal that auxetic adherends exhibit higher strength compared to flat surfaces. Interestingly, the strength of ductile adhesives in auxetic bonded joints increases with adhesive thickness, while brittle adhesive strength decreases with thicker auxetic bonds. Moreover, the auxetic structure displays reduced elongation under comparable force.

The findings emphasize the intricate interplay between adhesive type, bonded surface configuration of adherend and bonding thickness, crucial for understanding the mechanical behavior of adhesively bonded joints in the context of 3D-printed materials.

The purpose of this paper is to analyze the effect of printing parameters on the mechanical properties of standard dog bone specimens manufactured by fused deposition modeling.

Polylactic acid (PLA) specimens were printed and tested according to the ASTM standard. The effect of five important printing parameters, layer height, raster angle, printing speed, nozzle temperature and nozzle diameter, was examined on ultimate tensile strength (UTS), elongation and apparent density. Five levels were attended for each parameter, and a high number of required experiments were reduced by applying the L₂₅ Taguchi design of the experiment.

The effect of each parameter on outputs and optimal values for maximum tensile strength were determined. The most influential parameter is the raster angle of 64.96%. Nozzle temperature has a low effect of 1.76%, but nozzle diameter contribution is 9.77%. The experiment results are validated by analysis of variance analysis, and the optimal predicted level for parameters is 90° raster angle, 0.2 mm layer height, 100 mm/s printing speed, 200°C nozzle temperature and 0.8 mm nozzle diameter. The maximum UTS observed is 48.70 MPa for 0.8 mm nozzle diameter, whereas the minimum is 18.49 for 0.2 mm nozzle diameter.

This paper is a very extensive experimental research report on the effect of the parameters for the tensile property of 3D printed PLA specimens by the Taguchi method. The documented results can be further developed for an optimization model to obtain a desired mechanical property with less variation and uncertainty in a product.

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.