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This study aims to evaluate the potential of using the in-nozzle impregnation approach to reuse recycled PET (RPET) to develop continuous banana fiber (CBF) reinforced bio-composites. The mechanical properties and fracture morphology behavior are evaluated to establish the relationships between layer spacing–microstructural characteristics–mechanical properties of CBF/RPET composite.

This study uses RPET filament developed from post-consumer PET bottles and CBF extracted from agricultural waste banana sap. RPET serves as the matrix material, while CBF acts as the reinforcement. The test specimens were fabricated using a customized fused deposition modeling 3D printer. In this process, customized 3D printer heads were used, which have a unique capability to extrude and deposit print fibers consisting of a CBF core coated with an RPET matrix. The tensile and flexural samples were 3D printed at varying layer spacing.

The Young’s modulus (E), yield strength (sy) and ultimate tensile strength of the CBF/RPET sample fabricated with 0.7 mm layer spacing are 1.9 times, 1.25 times and 1.8 times greater than neat RPET, respectively. Similarly, the flexural test results showed that the flexural strength of the CBF/RPET sample fabricated at 0.6 mm layer spacing was 47.52 ± 2.00 MPa, which was far greater than the flexural strength of the neat RPET sample (25.12 ± 1.94 MPa).

This study holds significant social implications highlighting the growing environmental sustainability and plastic waste recycling concerns. The use of recycled PET material to develop 3D-printed sustainable structures may reduce resource consumption and encourages responsible production practices.

The key innovation lies in the concept of in-nozzle impregnation approach, where RPET is reinforced with CBF to develop a sustainable composite structure. CBF reinforcement has made RPET a superior, sustainable, environmentally friendly material that can reduce the reliance on virgin plastic material for 3D printing.

The use of additive manufacturing in many branches of industry is increasing significantly because of its many advantages, such as being able to produce complex parts that cannot be produced by classical methods, using fewer materials, easing the supply chain with on-site production, being able to produce with all kinds of materials and producing lighter parts. The binder jetting technique, one of the additive manufacturing methods researched within the scope of this work, is predicted to be the additive manufacturing method that will grow the most in the next decade, according to many economic reports. Although additive manufacturing methods have many advantages, they can be slower than classical manufacturing methods regarding production speed. For this reason, this study aims to increase the manufacturing speed in the binder jetting method.

Adaptive slicing and variable binder amount algorithm (VBAA) were used to increase manufacturing speed in binder jetting. Taguchi method was used to optimize the layer thickness and saturation ratio in VBAA. According to the Taguchi experimental design, 27 samples were produced in nine different conditions, three replicates each. The width of the samples in their raw form was measured. Afterward, the samples were sintered at 1,500 °C for 2 h. After sintering, surface roughness and density tests were performed. Therefore, the methods used have been proven to be successful. In addition, measurement possibilities with image processing were investigated to make surface roughness measurements more accessible and more economical.

As a result of the tests, the optimum printing condition was decided to be 180–250 µm for layer thickness and 50% for saturation. A separate test sample was then designed to implement adaptive slicing. This test sample was produced in three pieces: adaptive (180–250 µm), thin layer (180 µm) and thick layer (250 µm) with the determined parameters. The roughness values of the adaptive sliced sample and the thin layer sample were similar and better than the thick layer sample. A similar result was obtained using 12.31% fewer layers in the adaptive sample than in the thin layer sample.

The use of adaptive slicing in binder jetting has become more efficient. In this way, it will increase the use of adaptive slicing in binder jetting. In addition, a cheap and straightforward image processing method has been developed to calculate the surface roughness of the parts.

Selective jet electrodeposition (SJED) is an emerging additive manufacturing (AM) technology for realizing metallic components of nano and micro sizes. The deposited parts on the substrate form metallurgical bonding, so separating them from the substrate is an unsolved issue. Therefore, this paper aims to propose a method for separating the deposited micro parts from a sacrificial substrate. Furthermore, single and multi-bead optimization is performed to fabricate microparts with varying density.

A typical SJED process consists of a nozzle (to establish a column of electrolytes) retrofitted on a machine tool (to provide relative motion between substrate and nozzle) that deposits material atom-by-atom on a conductive substrate.

A comprehensive study of process parameters affecting the layer height, layer width and morphology of the deposited micro-parts has been provided. The uniformity in the deposited parts can be achieved with the help of low applied voltage and high scanning speed. Multi-bead analysis for the flat surface condition is experimentally performed, and the flat surface condition is achieved when the centre distance between two adjacent beads is kept at half of the width of a single bead.

Although several literatures have demonstrated that the SJED process can be used for the fabrication of parts; however, part fabrication through multi-bead optimization is limited. Moreover, the removal of the fabricated part from the substrate is the novelty of the current work.

This paper aims to present an integrated system designed for quality control and inspection in additive manufacturing (AM) technologies.

The study undertakes a comprehensive examination of the process in three distinct stages. First, the quality of the feedstock material is inspected during the preprocessing step. Subsequently, the main research topic of the study is directed toward the 3D printing process itself with real-time monitoring procedures using computer vision methods. Finally, an evaluation of the 3D printed parts is conducted, using measuring methods and mechanical experiments.

The main results of this technical paper are the development and presentation of an integrated solution for quality control and inspection in AM processes.

The proposed solution entails the development of a promising tool for the optimization of the quality in 3D prints based on machine learning algorithms.

While performance demands in the natural world are varied, graded lattice structures reveal distinctive mechanical properties with tremendous engineering application potential. For biomechanical functions where mechanical qualities are required from supporting under external loading and permeability is crucial which affects bone tissue engineering, the geometric design in lattice structure for bone scaffolds in loading-bearing applications is necessary. However, when tweaking structural traits, these two factors frequently clash. For graded lattice structures, this study aims to develop a design-optimization strategy to attain improved attributes across different domains.

To handle diverse stress states, parametric modeling is used to produce strut-based lattice structures with spatially varied densities. The tailored initial gradients in lattice structure are subject to automatic property evaluation procedure that hinges on finite element method and computational fluid dynamics simulations. The geometric parameters of lattice structures with numerous objectives are then optimized using an iterative optimization process based on a non-dominated genetic algorithm.

The initial stress-based design of graded lattice structure with spatially variable densities is generated based on the stress conditions. The results from subsequent dual-objective optimization show a series of topologies with gradually improved trade-offs between mechanical properties and permeability.

In this study, a novel structural design-optimization methodology is proposed for mathematically optimizing strut-based graded lattice structures to achieve enhanced performance in multiple domains.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.