• International Journal of Technology (IJTech)
  • Vol 12, No 1 (2021)

Quality Prediction Modeling of a Preform Fastener Process using Fuzzy Logic and DEFORM Simulation

Quality Prediction Modeling of a Preform Fastener Process using Fuzzy Logic and DEFORM Simulation

Title: Quality Prediction Modeling of a Preform Fastener Process using Fuzzy Logic and DEFORM Simulation
Suthep Butdee, Uten Khanawapee

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Butdee, S., Khanawapee, U., 2021. Quality Prediction Modeling of a Preform Fastener Process using Fuzzy Logic and DEFORM Simulation. International Journal of Technology. Volume 12(1), pp. 33-42

Suthep Butdee Laboratory Department of Production Engineering, Faculty of Engineering, King Mongkut’s University of Technology North Bangkok, Pracharaj 1, Bang Sue, Bangkok, 10800, Thailand
Uten Khanawapee Department of Mechanical Engineering Technology, College of Industrial Technology, King Mongkut’s University of Technology North Bangkok, Pracharaj 1, Bang Sue, Bangkok, 10800, Thailand
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Quality Prediction Modeling of a Preform Fastener Process using Fuzzy Logic and DEFORM Simulation

Quality is the most important aspect of fastener production for maintaining competitiveness and customer satisfaction. Nowadays, the quality control process is uncertain and complicated. Cold forging is used to produce preform fasteners via various processes. As a traditional measure of cold forging, quality prediction can be done using the normal probability method, but this approach is not effective or accurate enough. This paper proposes a novel quality prediction for preform fasteners using a fuzzy inference system and DEFORM simulation using an engineering software called DEFORM® system that can analyze metal forming. Multi-factors and criteria are considered, consisting of deformability, defects, stress, and time. The developed modeling can predict the cold forging quality at the stage of product design, which can associate decision making with production control. It can eliminate defects and reduce cost and time to rework. The case study is illustrated on the cold heading of stainless 341 using DEFORM, whereas the modeling is simulated by MATLAB.

Cold forging stainless 341; DEFORM; Fuzzy logic; Preform fastener; Quality prediction


Cold forging is used as a preform fastener production process; it involves several complex processes and parameters using punch and die sets that apply forces and heading speed. Several parameters and conditions are considered from design to production, including materials, machine capacity, punch and die design, and production processes. Fasteners are the most commonly used cold heading process in the automotive industry. Therefore, material flow is the main constraint under cold forging conditions; the difficulty of production when complex shapes are required is also an important factor. High-quality finished products are presently requested by customers. However, the quality of a new model cannot be ensured until the product is completely produced. As a result, it is necessary to develop quality prediction methodologies with multi-decision-making criteria using scientific methods, such as the analytic hierarchy process (AHP), fuzzy AHP, fuzzy logic, and rule-based expert systems. In addition, knowledge and experience must be captured from knowledge library experts. One of the most popular methods used to access and investigate knowledge is simulated by analysis with engineering software, for example, DEFORM, finite element, and other computer-aided engineering (CAE) software. However, such tools have rarely been used to predict the quality of cold forging. In addition, DEFORM® system is a time-consuming and knowledge base is limited as well as it does not have a mechanism to learn from experience. The relevant previous works are delineated in the next section.

The fastener starts from the preform forging and heading process, which consists of upsetting and/or extruding. The heading process can be accomplished via a high-speed operation. Moreover, it can comprise single or multiple processes. Raw material is normally in a round shape, whereas the material types can be mild steel, tool steel, stainless steel, and other modern materials. The combination of material shape and types can make the process more complex; in this scenario, a suitable punch and die set is required that may contain a simple set of the two punches and two dies up to the more complex set using five punches and five dies. The raw material must be prepared to match the volume of die-controlled conditions as well as the relationship between the diameters of the wire rod of the upsetting process is significant. Cold heading proceeds with forward or backward extrusion; forward extrusion is used when the raw material is smaller than the die, and the length and diameter must be related. Backward extrusion is used to form nuts, sleeves, and rivets.  Bolts, screws, and stepped shafts which are major components affecting the automotive industry supply chain are produced by forward extrusion (Ashari et al., 2018). However, present industry requires very short time production with high quality products and more complex shapes. Therefore, this study aims to determine the effects of cold forging criteria, including shape complexity, degree of formability, upsetting ratio, and extrusion ratio, and to develop the quality prediction modeling of a preform fastener for cold forging with fuzzy logic compared and verified with DEFORM simulation.


In this study, quality prediction of fastener processing using fuzzy logic was applied and presented with practical factor selection. The fuzzy approach can be used by the MATLAB toolbox. The prediction is shown as the percentage of successful forging. If the prediction quality is more than 70%, it is acceptable. In the present results, the quality of completion and perfection are more than 90%, which is satisfactory. The simulation experiment was verified using DEFORM. It was found that the two-step simulation generated the highest maximum load (70,000 kgf), whereas the four-step simulation gave the lowest maximum load (53,000 kgf). The final result confirmed that four-step forging is workable, whereas five-step cold forging gives better quality.


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