Citation

Kuppulakshmi, Vadivelu and Sugapriya, Chandrasekar and Nagarajan, Deivanayagampillai and Vidhya, Srinivasaraghavan and Shanfari, Shaima Al (2026) Enhancing advanced additive manufacturing for uncertain and complex domains. In: Data-driven Decision Making and Soft Computing. CRC Press, pp. 92-104. ISBN 978-100363483-6, 978-104106297-4, 978-104106314-8

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Abstract

3D printing has revolutionized modern manufacturing. Products are built layer by layer, making it feasible to design custom products, minimize waste, and have more flexibility in manufacturing. This new technology demands new production models that go with its capabilities, such as just-in-time (JIT) production, where manufacturers can immediately respond to shifts in demand while minimizing inventory costs. Stochastic modelling has played a very important role in this regard, as it provides probabilistic tools for forecasting demand, managing inventory, and handling uncertainties in production and supply. Fuzzy logic is also very flexible, since systems can process imprecise or ambiguous information, which is really crucial for refining quality control, optimizing processes, and making adaptable decisions in real time. Together, these approaches permit manufacturers to tap into the full ability of 3D printing along the lines of modern industrial production needs, ultimately coming to a more responsive, efficient manufacturing ecosystem. In this chapter, we will propose and explore a new model on the production line, blending stochastic and fuzzy logic methods with the specific challenges and opportunities in 3D printing. This model aims to enhance the accuracy and responsiveness of production planning, considering variable demand and complex decision-making that often occurs in additive manufacturing (AM) environments. By incorporating stochastic and fuzzy logic, our model will enable dynamic adjustments in production, greater flexibility in handling uncertainties, and enhanced adaptability to fluctuating market demands. This approach not only optimizes production efficiency but also makes the manufacturing process sustainable; hence, it forms a valuable framework for further developments in the field.

Item Type:	Book Section
Uncontrolled Keywords:	Fuzzy logic, Product design, Production control
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	03 Sep 2026 07:22
Last Modified:	03 Sep 2026 07:22
URII:	http://shdl.mmu.edu.my/id/eprint/16638

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