Prediction of optimal mild steel weld parameters using the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique

被引：3

作者：

Lofinmakin, Oladotun Oluyomi ^{[1
]}

Sada, Samuel Oro-oghene ^{[2
]}

Emovon, Ikuobase ^{[1
]}

Samuel, Olusegun David ^{[1
]}

Oke, Sunday Ayoola ^{[3
]}

机构：

[1] Fed Univ Petr Resources, Dept Mech Engn, Effurun, Delta, Nigeria

[2] Delta State Univ, Dept Mech & Prod Engn, Abraka, Delta, Nigeria

[3] Univ Lagos, Dept Mech Engn, Lagos, Nigeria

来源：

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2024年 / 131卷 / 3-4期

关键词：

Welding; Tensile strength; Hardness; Modeling; RESPONSE-SURFACE METHODOLOGY; OPTIMIZATION; SELECTION; MODELS;

D O I：

10.1007/s00170-024-13079-9

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Welding is one of the major operations in many industries as it provides a durable means of joining metals and ensuring that diverse equipments are created to meet the growing needs of the manufacturing industries. To enhance the production of these diverse equipments, studies are continually been performed to identify improved means of obtaining reliable joints. This study applies the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique, in improving the predictability of the optimal weld characteristics for a mild steel welded joints, with focus on tensile strength and hardness as responses. From the study, the variation in tensile strength and hardness as a result of the process parameter effects is illustrated, and it reveals the optimal tensile strength, and hardness is obtained at the combined input parameters: 170 Amp, 20 V, 24 l/min, and 2.2 mm for the tensile strength and 220 Amp, 20 V, 20 l/min, and 2.4 mm for the hardness.

引用

页码：1203 / 1210

页数：8

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