Numpon Mahayotsanun

A Draw‐In Sensor for Process Control and Optimization

Sheet metal forming is one of the major processes in manufacturing and is broadly used due to its high degree of design flexibility and low cost. In the sheet metal forming process, draw-in (planar movement of a sheet periphery) frequently occurs and is one of the most dominated indicators on the success of a forming process. Currently, monitoring and controlling draw-in during each stamping operation requires either time-consuming setup or a significant die modification. Most devices have been used only in laboratory settings. Our goal is to design a draw-in sensor providing high sensitivity in monitoring; ease of setup, measurement and controlling; and eventually be implemented in industry. Our design is based on the mutual inductance principle, which we considered physical factors affecting the characteristics of the draw-in sensor. Two different configurations, single-transducer and double-transducer of our draw-in sensors have been designed and tested. The results showed good linearity, especially for the double-transducer case. The output of the draw-in sensor was affected by the type of sheet metal, dimension of the transducer, and the distance between the transducer and the testing sheet metal. It was found that the result was insensitive to the waviness of the sheet metal if sheet thickness was thin. The invention, implementation, and integration of the draw-in sensor will have an enormous impact on revolutionizing the control of stamping process, will provide solid ground for process variation and uncertainty studies, and ultimately will affect the design decision process.

Pressure and Draw-In Maps for Stamping Process Monitoring

This paper presents two tooling-integrated sensing techniques for the in situ measurement and analyses of pressure distribution at the tool–workpiece interface and material draw-in during the stamping processes. Specifically, the contact pressure distribution is calculated from the measurements by an array of force sensors embedded in the punch, whereas sheet draw-in is measured by custom-designed thin film sensors integrated in the binder. Quantification of the pressure distribution from spatially distributed sensors has been investigated as a regularization problem and solved through energy minimization. Additionally, a Bayesian framework has been established for combining finite-element analysis (FEA) based estimates of the pressure distribution with experimentally measured evidence, to achieve improved spatiotemporal resolution. A new data visualization technique termed pressure and draw-in (PDI) map has been introduced, which combine spatiotemporal information from the two sensing techniques into an illustrative representation by capturing both the tool–workpiece interaction (dynamic information) and resulting workpiece motion (kinematic information) in a series of time-stamped snap shots. Together, the two separate yet complementary process-embedded sensing methods present an effective tool for quantifying process variations in sheet metal stamping and enable new insight into the underlying physics of the process.

Wear prediction of die coatings in strip ironing by finite element simulation

This study has used a simple method to predict wear of die coatings used in the strip ironing of steel sheet based on Archard’s wear model by using finite element (FE) simulation. This method required the information regarding the quantity of worn material and thickness of die coatings. The die stresses obtained from the FE simulation were used to calculate the wear rate and wear depth per cycle. The die and sheet materials considered were D2 tool steel and S690QL, respectively. Two types of die coatings (CrN and TiN) and their known properties were studied. The studied sliding velocities were 0.1, 1.0 and 5.0 mm s−1. The contact pressures under investigation were 15, 150 and 1500 kPa. The results showed that TiN provided higher wear resistance due to its lower quantity of worn material (1.962 × 10−16 m3). The total wear depth per cycle increased with reducing quantity of worn material (increasing hardness value). Increase in sliding velocity led to increase in wear depth per cycle. The contact pressure (15–1500 kPa) did not significantly affect wear depth per cycle. To illustrate the acquired wear depth per cycle of all the coatings under different conditions, a predictive wear map was developed. In addition, the number of operating cycles based on the die coating thickness could be calculated. Based on the findings, the wear map could be used to help select appropriate die coatings and adjust process parameters in order to extend die service life.

Effects of Friction Models, Geometry and Position of Tool on Curving Tendency of Micro-Extrusion 6063 Aluminum Alloy Pins

Micro-extrusion process is one of the micro-forming technology for fabrication of micro-parts such as micro-gear shaft for microelectromechanical system (MEMS) and micro pins for electronic parts. This paper presents the friction models effects and geometry effects on curving tendency of micro-extrusion 6063 aluminum alloy pins. Three friction models were considered: (1) Coulomb friction, (2) plastic shear friction, and (3) combined (Coulomb & plastic shear) friction. The finite element simulation was carried out and the results showed that the combined friction model accurately predicted the micro-extrusion results. Then, four tool geometry and position effects were investigated: (1) punch shift length, (2) die angle, (3) die shift length, and (4) bearing length. The finite element simulation was carried out to determine these tool geometry and position effects on the curving tendency of micro-extruded pins. The results showed that punch shift length and die angle did not affect the curving tendency. However, die shift length caused the micro-extruded pins to curve. The increase in bearing length helped straighten the micro-extruded pins.

Tribological effects of chlorine-free lubricant in strip drawing of advanced high strength steel

This article investigated the tribological performance of the specially formulated chlorine-free lubricant in strip drawing of advanced high strength steel. Four different lubrication conditions (dry, chlorine-free lubricant, chlorine additive lubricant, and mineral base lubricant) at two sliding speeds (10 and 100 mm/min) were carried out to observe the friction coefficients of the die-workpiece interface in the strip drawing test. The main difference among these lubricants was the contents of chlorine and sulfur additives. The die and workpiece materials were SKD11 and JSH780R, respectively. The results showed that the combination of chlorine and sulfur additives provided the best tribological behaviors. In addition, only the small amount of sulfur content could establish a bond with metal surfaces. However, the higher sulfur content could interact with metal surfaces, because it was influenced by the increased temperature (higher sliding speed) and adsorption.

Influences of contact pressure, sliding velocity, lubricant, bending angle and surface texture on friction behaviours in stainless steel strip drawing

Abstract This article investigates the tribological effects of stainless steel strip drawing by considering the influences of contact pressure, sliding velocity, lubricant, bending angle and surface texture. The die and sheet materials used to carry out the strip drawing tests were SKD11 and AISI 304, respectively. Different values of the contact pressure, sliding velocity and bending angle were tested. Various types of lubricants and die surface textures were also investigated. The results show that there was an optimum value of friction coefficient for each condition. The minimum friction coefficient was found at the condition having 9.375 MPa contact pressure, 100 mm/min sliding velocity, chlorinated oil, 45º bending angle, and smooth coated die surface.

Microimprinting Simulation of Anti-Bacterial Pattern on Stainless Steel Sheet

The aim of this study was to investigate the effects of important factors in microimprinting, which could be used to create the anti-bacterial pattern on stainless steel sheets. The microimprinting process was modeled and simulated by using finite element analysis (FEA). The following factors were considered: forming steps, forming velocity, grain size, and friction coefficient. The simulation results showed that two-step forming helped reduce peak errors. Increasing forming velocity and friction coefficient tended to increase peak errors. The grain size effect was not noticeable because the selected grain sizes were much larger than that of the micro feature.