Challenges of numerical simulation technique in mechanical engineering with case studies

• Autorzy: Saleh I. R. , Ali K. A. H. , Hasan H. K.

Identyfikatory

DOI

10.5604/01.3001.0055.0862

• Języki publikacji: EN

Abstrakty

EN

Purpose: This study aims to raise awareness among researchers in many fields and enhance the advanced concepts by prediction methods, especially in the mechanical engineering field. Improving prediction methods for solving engineering problems in many fields of the industry by adopting modelling and simulation techniques is a great tool that can offer deeper insights into how to diagnose problems and errors before further investment. A critical issue in many design and manufacturing processes is predicting and diagnosing errors before actual investment and production. Design/methodology/approach: Testing of conventional structures has many difficulties in accommodating the whole of large-scale systems due to lack of manufacturing cost, laboratory space, and equipment capacity. Digital tools such as models enable engineers to avoid expensive and time-consuming physical prototypes and offer them many options to examine various options and weigh many choices of materials and components against ach other. Findings: In the research, some engineering processes have been simulated as a case study to show the ability to predict. Simulation output and conclusions revealed an excellent ability with high accuracy. The contour plots of the results in all cases of simulation processes revealed a good agreement about the behaviours of material during each individual process, such as forming or extrusion. Research limitations/implications: Predicting outcomes is critical in many industrial research and practice domains. Over the past few decades, there have been substantial advances in predictive modelling and simulation methods and concepts from the computer science, machine learning, and statistics literature that may have potential value for industrial applications in many fields of technology. Nevertheless, the modern methods in major fields of technology are still limited. Practical implications: Numerical modelling has some challenges that need to be addressed and overcome, such as being computationally intensive, requiring high-performance hardware and software, and being affected by errors and uncertainties from the assumptions, simplifications, approximations, and measurements involved. Additionally, numerical modelling depends on the quality and availability of data and information needed to define and validate the models and solutions. It can be influenced by the skills and expertise of the engineers who develop and use the models. Originality/value: Numerical method consistency is a critical element of numerical analysis and engineering computation, and it can provide many benefits for mechanical engineering. It can improve the accuracy and confidence of the numerical solution, as well as reduce the computational cost and time. Using a virtual platform, numerical modelling can test and optimise designs and materials in a more realistic and complex environment. Additionally, it can reveal details and interactions that are difficult or impossible to observe experimentally.

Słowa kluczowe

EN

numerical analysis; modelling and simulation; finite element analysis; prediction method; engineering process

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2025
• Tom: Vol. 128, nr 2
• Strony: 61--71
• Opis fizyczny: Bibliogr. 19 poz., rys.

Twórcy

autor

Saleh I. R.

• Al Furat Al Awsat Technical University, Institute Technical of Karbala, Iraq

autor

Ali K. A. H.

• Al Furat Al Awsat Technical University, Institute Technical of Karbala, Iraq

autor

Hasan H. K.

• Al Furat Al Awsat Technical University, Institute Technical of Karbala, Iraq

• https://orcid.org/0000-0002-0493-0921

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2026).