A Detailed Machinability Assessment of DC53 Steel for Die and Mold Industry through Wire Electric Discharge Machining

Recently, DC53 die steel was introduced to the die and mold industry because of its excellent characteristics i.e., very good machinability and better engineering properties. DC53 demonstrates a strong capability to retain a near-net shape profile of the die, which is a very challenging process with materials. To produce complex and accurate die features, the use of the wire electric discharge machining (WEDM) process takes the lead in the manufacturing industry. However, the challenge is to understand the physical science of the process to improve surface features and service properties. In this study, a detailed yet systematic evaluation of process parameters investigation is made on the influence of a wire feed, pulse on duration, open voltage, and servo voltage on the productivity (material removal rate) and material quality (surface roughness, recast layer thickness, kerf width) against the requirements of mechanical-tooling industry. Based on parametric exploration, wire feed was found the most influential parameter on kerf width: KW (45.64%), pulse on time on surface roughness: SR (84.83%), open voltage on material removal rate: MRR (49.07%) and recast layer thickness: RLT (52.06%). Also, the optimized process parameters resulted in 1.710 µm SR, 10.367 mm3/min MRR, 0.327 mm KW, and 10.443 µm RLT. Moreover, the evolution of surface features and process complexities are thoroughly discussed based on the involved physical science. The recast layer, often considered as a process limitation, was explored with the aim of minimizing the layers’ depth, as well as the recast layer and heat-affected zone. The research provides regression models based on thorough investigation to support machinists for achieving required features.

Programming for Machining in Electrical Discharge Machine

The correct selection of manufacturing conditions is one of the most important aspects to take into consideration in most manufacturing processes and, particularly, in processes related to electrical discharge machining (EDM). It is a capable of machining geometrically complex or hard material components that are precise and difficult-to-machine such as heat-treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels, etc. being widely used in die and mold making industries, aerospace, aeronautics, and nuclear industries. This chapter highlights the programming for machining in electrical discharge machine.

Process design for 5-axis ball end milling using a real-time capable dynamic material removal simulation

For repairing turbine blades or die and mold forms, additive manufacturing processes are commonly used to build-up new material to damaged sections. Afterwards, a subsequent re-contouring process such as 5-axis ball end milling is required to remove the excess material restoring the often complex original geometries. The process design of the re-contouring operation has to be done virtually, because the individuality of the repair cases prevents actual running-in processes. Hard-to-cut materials e.g. titanium or nickel alloys, parts prone to vibration and long tool holders complicate the repair even further. Thus, a fast and flexible material removal simulation is needed. The simulation has to predict suitable processes focusing shape deviations under consideration of process stability for arbitrary complex engagement conditions. In this paper, a dynamic multi-dexel based material removal simulation is presented, which is able to predict high-resolution surface topography and stable parameters for arbitrary processes such as 5-axis ball end milling. In contrast to other works, the simulation is able to simulate an unstable process using discrete cutting edges in real-time.

Enabling Artificial Intelligence as Input Variable Control to Prevent Package Thickness Related Defect in Compression Molding

This paper aims to identify the causes of package thickness related defects in compression mold process. Related defects include wrong package thickness, exposed wire and/or die and mold bleed out. There are three scenarios why package thickness problem is encountered in compression molding. These include wrong mold recipe selected against the actual lot, wrong lot loaded against the current recipe loaded and product input to mold having irregularities such as presence of stray die or damage on strip side rails and end rails. Applying artificial intelligence (AI) the mold machine to detect all abnormalities identified at input and prevent it from proceeding to molding. Applying AI was able to eliminate occurrence of all package thickness related defects and machine related downtimes.

Rapid Estimation of Die and Mold Machining Time Without NC Data by AI Based on Shape Data

Rapid estimation of machining time is necessary for flexible production scheduling and early responses regarding delivery date. It is also important for selecting the most suitable of a factory’s many machine tools. Usually, machining time is estimated based on an NC program. However, this takes time to generate and its estimation accuracy is not ideal because it cannot consider the control characteristics of the machine tool. This study proposes a new method for rapidly estimating die and mold machining time without generating an NC program: inputting curvature and machining depth distributions into AI as color information.

Ultrasonic-Assisted Innovative Polyurethane Tool to Polish Mold Steel

High-quality die and mold production is becoming increasingly important in modern mass production. Surface quality is one of the most frequent and stringent customer specifications for machined parts, of which the major consideration and indication of quality is their surface roughness. In this study, a novel ball-ended polishing tool made of polyurethane impregnated with micro cubic boron nitride (CBN) was developed. The polishing tool was mounted on a three-axis machining center; the rotary polishing action was achieved via ultrasonics. Polishing experiments were conducted on specimens of hardened Stavax stainless mold steel. Four types of polishing tools – containing 10 wt% of pure CBN particles and Al2O3coated CBN with two different degrees of hardness (Shore 25 and 45) – were fabricated; the results of the experiments were compared with those obtained using a traditional elastic-ball polishing tool. It was found that the surface quality achieved using the Al2O3coated CBN tool was superior to that with pure CBN particles because the hardness of the polishing tool increased with the increase in the surface roughness of the workpiece. In addition, ultrasonic-assisted polishing yielded a better surface finish.

A Neural Network Based Process Planning System to Infer Tool Path Pattern for Complicated Surface Machining

Die and mold are necessary for the manufacture of present industrial products. In recent years, the requirement of high quality and low cost machining of complicated surfaces has increased. However, it is difficult to generalize process planning that depends on skillful experts and decreases the efficiency of preparation in die and mold machining. To overcome an issue that is difficult to generalize, it is well known that neural networks may have the ability to infer a valid value based on past case data. Therefore, this study aims at developing a neural network based process planning system to infer the required process parameters for complicated surface machining by using past machining information. The result of the conducted case studies demonstrates that the developed process planning system is helpful for determining the tool path pattern for complicated surface machining according to the implicit machining knowhow.

Regression Equations to Determine the Stages of Electric Current in Electrical Discharge Machining (EDM) According to the Level of Desired Surface Roughness with Shortest Processing Time

Electrical Discharge Machining (EDM) is one of the most common non-conventional machining processes used in the manufacturing of die and mold. In the process of EDM, practitioners usually face a problem, which is how to shorten process time and determine the point where the current should be changed so that the resulted surface roughness is not too high due to the use of large current at the beginning of the process. The purpose of this study is to determine the point when to change (reduce) the current in order to obtain the desired surface roughness and shortest processing time. From analysis of data, experiment was obtained some regression equations, those are: average surface roughness (Ra = 5424 + 0.698 I) which is used to find the final current to obtain the desired final surface roughness, peaks to valleys average roughness (Rz = 5.73 + 3.418 I) which is used to find the changing point for initial currentand duration of processing time (t =103.164 + 0.4714 I) which is used to estimate the duration of processing time with the input of initial and final currents.