High business competition demands business players to improve quality. The Six Sigma
with DMAIC phases is a strategy that has proven effective in improving product and service quality. This study aims to find the consistency of DMAIC phases implementation and
analyze the objective value in Six Sigma research. By using a number of trusted article
sources during 2005 until 2019, this research finds that 72% research in manufacturing industry consistently implemented DMAIC roadmap especially in case study research type
for problem-solving, while service industry pointed out the fewer number (60%). The causes
of variations and defective products in the manufacturing industry are largely caused by
a 4M 1E factor, while in service industry are caused by human behavior, and it’s system
poorness. Both manufacturing & service industry emphasized standardization & monitoring to control the process which aimed at enhancing process capability and organization
performance to increase customer satisfaction.
This paper presents a new welding quality evaluation approach depending on the analysis
by the fuzzy logic and controlling the process capability of the friction stir welding of
pipes (FSWoP). This technique has been applied in an experimental work developed by
alternating the FSW of pipes process major parameters: rotation speed, pipe wall thickness
and travel speed. variable samples were friction stir welded of pipes using from 485 to 1800
rpm, 4–10 mm/min and 2–4 mm for the rotation speed, the travel speed, and the pipe wall
thickness respectively. DMAIC methodology (Defining, Measuring, Analyzing, Improving,
Control) has been used as an approach to analyze the FSW of pipes, it depends on the
attachment potency and technical commonplace demand of the FSW of pipes process.
The analysis controlled the Al 6061 friction stir welded joints’ tensile strength. To obtain
the best tensile strength, the study determined the optimum values for the parameters from
the corresponding range.
The most important challenges in the construction field is to do the experimentation of the designing at real time. It leads to the wastage of the materials and time consuming process. In this paper, an artificial neural network based model for the verification of sigma section characteristics like shear centre and deflection are designed and verified. The physical properties like weight, depth, flange, lip, outer web, thickness, and area to bring shear centre are used in the model. Similarly, weight, purlin centres with allowable loading of different values used in the model for deflection verification. The overall average error rate as 1.278 percent to the shear centre and 2.967 percent to the deflection are achieved by the model successfully. The proposed model will act as supportive tool to the steel roof constructors, engineers, and designers who are involved in construction as well as in the section fabricators industry.