In the rapidly evolving field of advanced manufacturing, the take-off of 3D printing technology has led to groundbreaking innovations that are reshaping various industries. Among these advancements is the use of Hybrid Intelligence Systems, specifically the development of a Taguchi grey-based hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) for the fused deposition modeling (FDM) process of High Impact Polystyrene (HIPS). This compelling study, conducted by renowned researchers N. Manikandan, P. Thejasree, and S. Marimuthu, delves into the intricate relationships between the parameters of FDM and the resulting quality of printed components, aiming to enhance efficiency and productivity in manufacturing.
The FDM technique, a cornerstone of additive manufacturing, has garnered attention for its capability to produce geometrically complex parts with a diverse range of materials. Among these materials, HIPS has gained popularity due to its excellent impact resistance and versatility. However, achieving optimal printing conditions for HIPS has long presented a challenge for manufacturers. The researchers set out to address these challenges by developing a predictive model that integrates Taguchi methods with grey relational analysis and ANFIS to optimize parameters such as nozzle temperature, bed temperature, and print speed.
Incorporating the Taguchi methodology allows the research team to systematically investigate the effects of various printing parameters while minimizing variability. This robust methodology is particularly effective in creating a cost-effective experimental design, enabling the optimization of multiple factors simultaneously. By employing the Taguchi framework, the researchers aimed to identify settings that yield the best mechanical properties and surface quality for HIPS parts produced via FDM.
The study also introduces grey relational analysis, a technique that is adept at handling multiple response variables. In the context of 3D printing, numerous output characteristics, such as tensile strength, surface roughness, and layer adhesion, need to be considered. The grey relational analysis provides a functional framework to evaluate the relative performance of different settings, allowing for an integrated approach to optimization.
Crucially, the research introduces the hybrid ANFIS model, which combines the strengths of neural networks and fuzzy logic. This model efficiently translates the complex interactions among the FDM parameters into a user-friendly predictive tool. The ANFIS framework enhances the learning process, enabling the model to generalize from experimental data and make accurate predictions regarding the quality of printed parts based on input parameters. This capability is invaluable in the FDM landscape, where precision and quality control are paramount.
The researchers implemented their methods in a structured experimental setup, carefully monitoring a variety of parameters during the FDM process. Through iterative testing and model refinement, they established a correlation between the input parameters and the desired mechanical properties of the printed HIPS samples. The results indicate a strong predictive capability of the hybrid ANFIS model, demonstrating tangible improvements in performance over traditional statistical approaches.
The implications of this research extend beyond mere academic interest. By optimizing the FDM process for HIPS using advanced predictive models, manufacturers can significantly reduce production costs and time while improving the mechanical properties of their products. This is particularly relevant in sectors that demand high-quality prototypes and end-use parts, including aerospace, automotive, and healthcare industries.
Another noteworthy aspect of this research is its alignment with the principles of sustainable manufacturing. By enhancing the efficiency of the FDM process, manufacturers can minimize waste generation, optimize material usage, and foster a circular economy approach. The integration of AI-driven predictive models in manufacturing processes is thus seen as a pivotal step forward toward sustainability in production methodologies.
In conclusion, the groundbreaking work conducted by Manikandan, Thejasree, and Marimuthu marks a significant evolution in the field of additive manufacturing. Their exploration of Taguchi grey-based hybrid ANFIS for the FDM of HIPS not only addresses pressing challenges in 3D printing but also sets a precedent for future research endeavors. As industries continue to embrace smart manufacturing techniques, the importance of such innovative models in optimizing production processes cannot be overstated. This pioneering research offers practical solutions that could drive the next wave of advancements in manufacturing efficiency and sustainability.
This study serves as an inspiration for future research projects. As technology continues to evolve, there is an ever-increasing demand for innovative solutions to enhance manufacturing processes. Researchers and practitioners in the field are urged to explore the potential of hybrid models and integrate modern methodologies that promote efficiency, sustainability, and product quality. The ongoing evolution of 3D printing technology and materials sciences presents an exciting frontier for exploration and development, wherein such advanced models may soon become industry standards.
As the field evolves, a culture of continuous improvement and experimentation will undoubtedly foster further breakthroughs. The integration of AI technologies, like the hybrid ANFIS model, into manufacturing environments signifies not only a change in approach but also a transformational moment that could redefine the manufacturing landscape. Understanding and harnessing the synergies between artificial intelligence and traditional manufacturing techniques will be paramount for those looking to make their mark in the future of industrial production.
A new era in manufacturing is upon us, driven by innovation and the relentless pursuit of improvement. The results presented by Manikandan and his team lay the groundwork for subsequent innovations that will streamline workflows, enhance material performance, and ultimately contribute to more sustainable manufacturing practices. As industries around the world rally to keep pace with advancements, the focus will increasingly be on leveraging such predictive models for competitive advantage, efficiency, and a sustainable future.
In the end, embracing these innovations and understanding their significance can empower businesses to lead the charge in the 4th Industrial Revolution. The marriage of artificial intelligence with manufacturing processes promises a horizon brimming with potential. The trajectory of this research underlines the importance of interdisciplinary approaches, blending insights from engineering, materials science, and data analytics to solve complex manufacturing problems and push the boundaries of what is possible in 3D printing.
The realm of 3D printing is evolving rapidly, and studies like these contribute to a better understanding of how advanced technologies can harmonize with traditional methods to create superior products. As industries navigate the path toward greater efficiency and better performance, it is essential to ensure that innovations are not only technically feasible but also economically viable and aligned with sustainability principles.
Each advancement strengthens the potential for a future in which manufacturing is smarter, cleaner, and more responsive to the needs of society. The research community’s contributions in terms of optimizing processes and improving material properties will play a vital role in ensuring that industries can thrive in an increasingly competitive landscape.
Amidst challenges, there are ample opportunities for researchers and industry leaders to join forces and collaborate on future endeavors that promise to transform manufacturing. The establishment of such hybrid intelligence systems is a testament to the power of collaboration, as disciplines converge to foster meaningful innovation. Stakeholders are encouraged to stay abreast of these developments and actively pursue integration strategies that maximize value and quality in production processes, ultimately benefitting end-users and the industry at large.
As we look ahead, the outlook for FDM and the application of hybrid intelligence systems is bright. Embracing such technologies will enable manufacturers to produce higher quality components at a faster pace, supporting the growing demands of diverse industries while upholding the values of sustainability and efficiency. The evolution of additive manufacturing continues to garner interest and excitement, paving the way for further exploration and advancements that will redefine the future of production.
Subject of Research: Fused deposition modeling (FDM) of High Impact Polystyrene (HIPS) using a hybrid ANFIS model.
Article Title: Development of Taguchi grey-based hybrid ANFIS prediction model for fused deposition modelling of HIPS.
Article References:
Manikandan, N., Thejasree, P., Marimuthu, S. et al. Development of Taguchi grey-based hybrid ANFIS prediction model for fused deposition modelling of HIPS.
Discov Sustain 6, 1184 (2025). https://doi.org/10.1007/s43621-025-02049-0
Image Credits: AI Generated
DOI: 10.1007/s43621-025-02049-0
Keywords: Hybrid ANFIS, Fused deposition modeling, High Impact Polystyrene, Taguchi methodology, Grey relational analysis, Additive manufacturing.
