ARTIFICIAL INTELLIGENCE AS ENHANCEMENT TOOL FOR WELDING TECHNICIANS ON IMPROVING WELD JOINTS FOR QUALITY JOBS

ABSTRACT

Weld defects reduce structural strength, integrity, and reliability, leading to failures, reduced performance, and safety hazards for end-users. Inclusion of AI systems in welding processes aids in eliminating weld defects as well as improving jobs quality. This study investigated Artificial Intelligence as Enhancement Tool for Welding Technicians on Improving Weld Joints for Quality Jobs. This study used descriptive survey research design. The area of the study was, Benue State of Nigeria. The population of the study was 1600. A sample of 48 welding technicians was selected using Purposive sampling technique. The instrument used for data collection for this study was a self-structured questionnaire titled Artificial Intelligence Enhancement tool for Quality Jobs (AIETQJ) and was validated by 3 experts in varying disciplines. The Cronbach Alpha reliability coefficient was computed and the results obtained yielded a reliability coefficient alpha (α) values of; 0.85, and 0.83.Two research questions guided the study. Mean scores was used to answer the research questions and the standard deviation was used to check spread of the mean scores among the respondents. The mean scores and standard deviation were calculated using the Statistical Package for the Social Sciences (SPSS) version 21. The mean response were weighed with real limit of numbers as follows: Strongly Agree (SA) = 4 (3.50-4.00), Agree (A) = 3 (2.50- 3.49), -3.49), Disagree (D) = 2 (1.00-2.49) and Strongly Disagree (SD) = 1 (0.50-1.49). The items with mean scores between 0.50 to 1.49 were considered Strongly Disagree, items with mean scores between 1.50 to 2.49 were  considered Disagree, items with mean scores between 2.50 to 3.49 were considered Agree and items with mean scores between 3.50 to 4.00 were considered strongly Agree. The researcher distributed the 48 copies of AIETQJ questionnaires to the respondents and only 44 copies of AIETQJQ were returned. The null hypothesis formulated was tested using Chi-square and decisions were taken based on P-values and Alpha values.  When P < 0.5, the null hypothesis was rejected and considered significant and when P > 0.5, the null hypothesis was not rejected and considered not significant. The study concludes that AI is a groundbreaking technology and makes it the reason why a growing number of production and manufacturing companies have integrated it in their businesses to improve practices with good quality jobs, make better decisions, and build emerging technologies. Application of AI systems in welding processes will solve many problems including weld defects and other related issues affecting jobs quality. It recommends that welding technicians should try as much as they could to prevent weld defects on their finished jobs. Welding Technicians must prepare joints properly (clean, fit-up), select the right materials and equipment. The study also recommends integration of AI systems in welding process for optimum productivity.