Machine Setup Research Articles

CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical Researcher

Penetration testing, a critical component of cybersecurity, typically requires extensive time and effort to find vulnerabilities. Beginners in this field often benefit from collaborative approaches with the community or experts. To address this, we develop Cybersecurity Intelligent Penetration-testing Helper for Ethical Researchers (CIPHER), a large language model specifically trained to assist in penetration testing tasks as a chatbot. Unlike software development, penetration testing involves domain-specific knowledge that is not widely documented or easily accessible, necessitating a specialized training approach for AI language models. CIPHER was trained using over 300 high-quality write-ups of vulnerable machines, hacking techniques, and documentation of open-source penetration testing tools augmented in an expert response structure. Additionally, we introduced the Findings, Action, Reasoning, and Results (FARR) Flow augmentation, a novel method to augment penetration testing write-ups to establish a fully automated pentesting simulation benchmark tailored for large language models. This approach fills a significant gap in traditional cybersecurity Q&A benchmarks and provides a realistic and rigorous standard for evaluating LLM’s technical knowledge, reasoning capabilities, and practical utility in dynamic penetration testing scenarios. In our assessments, CIPHER achieved the best overall performance in providing accurate suggestion responses compared to other open-source penetration testing models of similar size and even larger state-of-the-art models like Llama 3 70B and Qwen1.5 72B Chat, particularly on insane difficulty machine setups. This demonstrates that the current capabilities of general large language models (LLMs) are insufficient for effectively guiding users through the penetration testing process. We also discuss the potential for improvement through scaling and the development of better benchmarks using FARR Flow augmentation results.

Development and implementation of a simulated microgravity setup for edible cyanobacteria

Regenerative life support systems for space crews recycle waste into water, food, and oxygen using different organisms. The European Space Agency’s MELiSSA program uses the cyanobacterium Limnospira indica PCC8005 for air revitalization and food production. Before space use, components’ compatibility with reduced gravity was tested. This study introduced a ground analog for microgravity experiments with oxygenic cyanobacteria under continuous illumination, using a random positioning machine (RPM) setup. L. indica PCC8005 grew slower under low-shear simulated microgravity, with proteome analysis revealing downregulation of ribosomal proteins, glutamine synthase, and nitrate uptake transporters, and upregulation of gas vesicle, photosystem I and II, and carboxysome proteins. Results suggested inhibition due to high oxygen partial pressure, causing carbon limitation when cultivated in low-shear simulated microgravity. A thicker stagnant fluid boundary layer reducing oxygen release in simulated microgravity was observed. These findings validate this RPM setup for testing the effects of non-terrestrial gravity on photosynthetic microorganisms.

Novel processing of micro-channels in micro-grinding with automated laser-assistance

AbstractMicro-structures are widely utilized in various fields such as optical, medical, defense, and aerospace. The accuracy and performance of micro-structures depend on fabrication methods, including conventional and non-conventional machining. High-quality micro-channel fabrication is challenging by conventional micro-grinding (CMG) which uses a micro-pencil grinding tool during micro-machining. However, in CMG, high-cutting forces act in the machining zone leading to tool deflection, tool failure, high temperature, and poor surface finish. The challenges of CMG can be overcome by using automated laser-assisted micro-grinding (LAMG), one of the modern micro-machining processes for fabricating high-quality, precise micro-channels. The novel processing of micro-channels in this work is through the in tandem use of LAMG and CMG. In this, the laser structuring is performed using LAMG followed by a micro-pencil grinding tool processing in a sequential manner on a same machine tool to achive high-quality parts and accuracy. This helps in fabrication of a micro-channel directly in one go without the need to remove the part to perform the two operations on two different machining setups, thereby reducing systematic fixturing errors which are significant when we are working at the micro-scale. This paper thus investigates the effect of various parameters viz. laser input energy densities and laser power on cutting forces and surface roughness on the tool as well as the work surface with two distinct scan patterns. In LAMG, at lower laser input energy density (pattern 1), the tangential and normal forces decreased by 10.3% and 20.5%, and surface roughness Sa and Sq reduced by 24% and 22% compared to CMG. Similarly, a normal force decreased by 20.5% in pattern 1 and 38% less in pattern 2. Still, the higher laser energy density (pattern 2) is unsuitable for structure due to an increase in Sa and Sq by 11% and 14.6%, respectively. Results have shown a maximum reduction in the normal and tangential force magnitude by 31 and 44% at 25 W compared to CMG. The work concludes that LAMG with novel laser assistance scan patterns has lower dynamic deflections resulting in better dimensional accuracy and surface finish compared to CMG with a lower tool.

Sustainable scheduling of TFT-LCD cell production: A hybrid dispatching rule and two-phase genetic algorithm

TFT-LCD manufacturing process involves Array, Color Filter (CF), Cell, and Module. The Cell process is pivotal in the TFT-LCD production supply chain; it bridges the front-end (Array and CF) and back-end (Module), maintaining a balance of production capacities. Supply from the front-end is transmitted to the back-end, ensuring prompt responsiveness to customer demands. As awareness of environmental conservation grows, implementing sustainable development in the TFT-LCD manufacturing industry has become crucial. Specifically, reducing the machine setup frequency in TFT-LCD scheduling decreases energy consumption during machine operation and wait times and prolongs equipment lifespan by minimizing wear from repeated shutdowns. To tackle the intricate scheduling challenges within TFT-LCD production, this study proposes an intelligent, sustainable scheduling approach that employs a rolling dispatching and line balancing mechanism and a two-phase genetic algorithm (HDTPGA) for Cell scheduling. The goal is to minimize makespan considering product diversity, production constraints, work-in-process balancing, and machine setup frequency. This holistic approach promotes energy-efficient and sustainable manufacturing practices. According to the empirical study, the proposed HDTPGA reduces the makespan by approximately 10% compared to the baseline. Additionally, it reduces the average daily setup frequency of bottleneck stages (ODF and CCT) in the Cell process by approximately 68.25% and 66.25%, respectively. The main contribution of this paper is to propose an intelligent scheduling algorithm to optimize Cell production scheduling in practice. The HDTPGA achieves equilibrium between front-end and back-end production, utilizing an efficient line change mechanism to reduce energy consumption. The enhancement in efficiency not only restricts material waste but also reduces the environmental impact, enabling manufacturers to adopt green production practices and promoting a transition of the entire panel industry toward sustainability.

Meso-level surface modification of Hastelloy C276 by WS2 powder mixed electrical discharge alloying process through micro-EDM setup

The present research provides a rigid, wear-resistant lubricating localized layer on Hastelloy C276 (HC 276) surface at meso level by tungsten disulphide (WS2) powder suspended electrical discharge alloying (EDA) process through micro-electrical discharge machining (μ-EDM) setup. The performance characteristics of the coated surface were evaluated in terms of material deposition rate (MDR), surface morphology, surface roughness (Ra, Rz and Rq), recast layer thickness (RLT), compositional analysis, micro-hardness & indentation depth, wear test and water contact angle analysis. Maximum MDR has been achieved at 15 g/L WS2 concentration and 80 V of gap voltage whereas The RLT varies from 6.87 μm to 18.91 μm and is enhanced with the increase in WS2 concentration. The critical surface characterization confirms higher WS2 concentration reduces the crater dimensions and the presence of micro-cracks and micro-voids on the coated surface. The surface roughness has been increased with the gap voltage and capacitance, whereas higher WS2 concentration provides lower surface roughness. X-Ray diffraction analysis (XRD) confirms the presence of hard and wear resistance phases like WC, WO3, ZnO, and MoO2 on the coated surface. Energy-Dispersive X-Ray Spectroscopy (EDS) analysis along with elemental mapping justifies tool, dielectric materials, and powder particles migration on the coated surface. The micro-hardness of the coated surface (402 HV to 1166 HV) is enhanced by 3–4 times compared to the base material (299 HV) whereas the indentation depth has been reduced with the rise in WS2 concentration and lies within the RLT of the coated surface. The wear rate of the WS2 coated surface is drastically reduced compared to the base material with a lower value of coefficient of friction (COF). The coated surface became hydrophobic in nature with a contact angle of 109.6° compared to the hydrophilic base HC 276.

Development and parametric study of reversible motion-wire electrochemical spark machining (RM-WECSM) setup for micro-width curve profile cutting on Al2O3 epoxy nanocomposites

The development of wire electrochemical spark machining setup is always challenging in the form of industrial viable for machining advanced engineering materials such as polymer nanocomposites, quartz, glass, ceramics, etc. Because of some drawbacks associated with it, like irregularity in kerf width, poor surface finish, no spark formed at high wire velocity, wire breaking at high voltage, etc. In the present work, a newly developed hybrid machining process that is reversible motion-wire electrochemical spark machining (RM-WECSM) has been developed for curve profile cutting, especially on non-conducting workpieces such as aluminum oxide epoxy nanocomposite of 3.5 mm thickness. The effect of NaOH concentration, applied voltage, reversible wire velocity, pulse on-and-off time, on material removal rate and average surface roughness have been studied using a one-parameter-at-a-time approach. Sparking occurred from the electrolyte surface to 10 mm above the electrolyte surface to cut the workpiece. The spark length is equal to the workpiece thickness. A continuous, stable spark has been formed at a high reversible wire velocity (19.8 m/min). Simultaneously, self-electrolyte flow has been generated, due to which melted materials have been washed out of the cut-surface and provided the smooth (1.987 µm) cut-surface of the workpiece. The maximum MRR is found as 1.63 mg/min. Thermal cracks, heat-affected zones and recast layers were not found on the curve cut-surface, and it was analyzed by a field emission scanning electron microscope.

Automatic Machining Setup via Deep Learning and Image Processing

Automatic Machining Setup via Deep Learning and Image Processing

Can Simulated Microgravity and Darkness Conditions Influence the Phytochemical Content and Bioactivity of the Sprouts?-A Preliminary Study on Selected Fabaceae Species.

Sprouts' consumption has become popular due to their wide availability, easy cultivation process, and proven biological activity. Moreover, stress factors, such as limited access to light or disturbed gravity during growth, may contribute to the increased activity and the synthesis of bioactive compounds. In this study, for the first time, the examination of the impact of darkness and simulated microgravity conditions on the white clover sprouts from the Fabaceae family was conducted. Among several species, used in the preliminary attempts, only white clover was satisfactory sprouting in the disturbed gravity conditions, and thus was chosen for further examination. A random positioning machine setup was used during the cultivation process to simulate microgravity conditions. Additionally, the sprouts were cultivated in total darkness. Simulated microgravity and/or darkness during the first few days of the sprouts' growth caused biomass reduction, the increased synthesis of bioactive compounds (isoflavones and phenolics), and changes in the level of abscisic acid and phenylalanine ammonia-lyase. Moreover, it increased the antioxidant properties of the sprouts, while the enhancement of their cytotoxic impact was observed only for androgen-dependent prostate cancer LNCaP cells. To conclude, the presented results are promising in searching for novel functional food candidates and further studies are necessary, directed at other plant families.

Condition monitoring of electric vehicle motor testing machine’s Vital components using bagged trees and quadratic SVM: a comparative study

The future of the automotive industry lies in the utilization of electric vehicles (EVs). A crucial component of these EVs is the drive motor. The verification of critical parameters for the electrical motor is considered to be of utmost importance, and this is achieved through the use of a motor testing machine. The motor testing machine is utilized frequently to determine characteristic curve points and the electromagnetic behavior of electric motors under various conditions. However, it has been observed that the presence of a fault in the helical gear setup of the testing machine leads to frequent breakdowns and suboptimal performance, thereby affecting the efficiency of the assembly line in the production of electric vehicles. In light of these observations, the main objective of this research endeavor is to improve the motor test bench apparatus by altering essential design parameters through the utilization of vibration signal analysis and machine learning techniques. Prior to extracting statistical features, vibration signals are obtained at different gear settings. These signals are subsequently categorized using classifiers such as Bagged Trees and Quadratic SVM. Machine learning techniques are utilized to classify the gathered signals as either normal or faulty, both with and without the inclusion of a 0.25 KW load for each condition. The performance of multiple algorithms is evaluated and scrutinized. The results have demonstrated that the Bagged Trees technique outperforms the other algorithm, achieving an impressive accuracy of 95.3%. Consequently, the proposed method presents an adept solution for augmenting the motor test bench’s capability by modifying crucial design parameters.

Research on Multi-Objective Flexible Job Shop Scheduling Problem with Setup and Handling Based on an Improved Shuffled Frog Leaping Algorithm

Flexible job shop scheduling problem (FJSP), widely prevalent in many intelligent manufacturing industries, is one of the most classic problems of production scheduling and combinatorial optimization. In actual manufacturing enterprises, the setup of machines and the handling of jobs have an important impact on the scheduling plan. Furthermore, there is a trend for a cluster of machines with similar functionalities to form a work center. Considering the above constraints, a new order-driven multi-equipment work center FJSP model with setup and handling including multiple objectives encompassing the minimization of the makespan, the number of machine shutdowns, and the number of handling batches is established. An improved shuffled frog leading algorithm is designed to solve it through the optimization of the initial solution population, the improvement of evolutionary operations, and the incorporation of Pareto sorting. The algorithm also combines the speed calculation method in the gravity search algorithm to enhance the stability of the solution search. Some standard FJSP data benchmarks have been selected to evaluate the effectiveness of the algorithm, and the experimental results confirm the satisfactory performance of the proposed algorithm. Finally, a problem example is designed to demonstrate the algorithm’s capability to generate an excellent scheduling plan.

Effective optimization strategies for abrasive water jet machining of glass-carbon fiber reinforced composites: A comparative study of evolutionary optimization techniques

This study applied evolutionary optimization techniques and neural networks to predict optimum machining parameters of Abrasive Water Jet Machining (AWJM) for machining Glass-Carbon Fiber Reinforced Composite (GCFRC) materials. Several researchers have employed different optimization techniques; however, evolving computational capabilities further open avenues to optimize such parameters. Five evolutionary techniques, namely Artificial Neural Network (ANN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Nonlinear Least Square Error (LSE), were applied to minimize surface roughness (Ra) and maximize kerf width (Kw) and material removal rate (MRR). The output performance parameters (Ra, Kw, and MRR) were formulated as a linear mathematical function of machining parameters: tool feed rate (TFR), cutting speed rate (CSR), and stand-off distance (SOD). Though this study solves linear optimization problems, the proposed technique will be a strong tool for solving complex associations of machining and performance parameters in the future. A dataset of machining parameters and subsequent performance parameters was adopted from the available literature. The results indicated that the LSE method outperformed other techniques, yielding the lowest Root Mean Square Error (RMSE) in predicting Ra, Kw, and MRR, thus ensuring high machining accuracy. LSE technique reported relatively least RMSE values of 0.37 µm, 0.149Mm, and 237.23 mm3/min for Ra, Kw, and MRR, respectively. SA and PSO displayed identical and competitive RMSE values, slightly higher than LSE (up to 20 % higher). ANN and GA techniques were not effective relative to other considered techniques. LSE, SA, and PSO provide superior performance in optimizing AWJM parameters. The significant contribution of this research is the proposed optimization technique, offering a clear direction for solving complex associations between the performances and machining parameters of AWJM. This work also provides a foundation for future research to optimize such associations for other machining setups.

Facile fabrication of hierarchical micro/nano aluminum alloy surfaces with enhanced hydrophobicity and surface quality by single point diamond cutting under a magnetic field influence

This study proposes an effective fabrication method for creating a hierarchical micro/nano structured aluminum alloy surface to enhance its hydrophobicity through single point diamond cutting using magnetophoresis. Magnetophoresis is a technique that manipulates metallic particles in metallic fluids using magnetic fields, and this study applies it to single-point diamond cutting by incorporating permanent magnets into the machining setup. The main grooves are cut to create a nano-grooved pattern on the first layer of the surface, while secondary grooves are cut on top of the first layer to form a micro pattern on the surface for two samples, one with and one without magnetophoresis. For magnetophoresis-fabricated samples, the first and second layers are cut in the presence of a magnetic field that is oriented perpendicular to the cutting direction of the first layer. Atomic force microscopy and an optical surface profiler reveal that the metallic marks appear on the surfaces that are parallel to the applied magnetic field for the magnetophoresis sample, which have been integrated with nano grooves to form a nano-textured surface on top of the microstructures. The sample fabricated under the influence of a magnetic field with magnetophoresis exhibits improved surface hydrophobicity, quality, and durability. This study highlights that magnetophoresis has the potential to fabricate metallic hydrophobic surfaces more efficiently than traditional methods for hierarchical micro/nano structured metallic surfaces, given that it does not necessitate the use of complex machining systems or advanced equipment.

Improving Maraging Steel 350 Machinability via Wiper Insert-Enhanced Face Milling

Despite the prevalent application of 18% Ni maraging steel in critical sectors such as aerospace and automotive due to its unique characteristics, including high ductility, yield strength, and hardenability, its machining presents enormous challenges, categorizing it as a difficult-to-machine material. The cutting tool’s geometry is crucial in machining, significantly affecting chip formation, cutting forces, power consumption, and obtainable surface quality. In particular, wiper insert technology, characterized by its multi-radius design, offers an increased contact area compared to conventional inserts, potentially enhancing the quality of the machined surface. This study explores the effectiveness of wiper inserts in the face-milling of maraging steel 350, conducting a comparative analysis across three distinct machining setups. These setups vary by alternating the number of wiper and conventional inserts within the same cutter, thereby examining the influence of insert configuration on machining outcomes. The research employs a reliable and well-established statistical approach to evaluate how different variables, such as cutting speed and feed rate, affect surface quality, power consumption, and material removal rate (MRR). It also sheds light on the material removal mechanisms facilitated by each type of insert. The findings reveal that incorporating a higher number of wiper inserts significantly enhances the surface finish but concurrently increases power consumption. Thus, the study successfully identifies an optimal set of process parameters that attain a balance between achieving superior surface quality and maintaining energy efficiency in the machining of maraging steel 350. This balance is crucial for optimizing manufacturing processes while adhering to the stringent quality and sustainability standards required in aerospace and automotive manufacturing.

History of contrast enhanced ultrasound (CEUS).

The 50th year of the European Federation of Societies in Ultrasound in Medicine and Biology (EFSUMB) has been celebrated 2022 publishing articles on the history of US. Contrast enhanced ultrasound (CEUS) allows to visualize blood flow and tissue perfusion. CEUS has proven to be safe without risk of nephrotoxicity. The availability of a contrast agent (tracer) for ultrasound imaging allows for the first time a dynamic assessment of tissue perfusion (blood flow and wash-in/wash-out pattern) which is an essential part for the detection and characterisation of pathological tissue and abnormal organ function. It was an outstanding achievement of academic centers in close cooperation with EFSUMB to investigate and validate the clinical potential of this new technology for the diagnosis and monitoring of various diseases and to develop clinical guidelines based on an in-depth assessment of the existing scientific publications. An important part of the implementation of CEUS in clinical practice was the development of contrast-specific imaging modes on the ultrasound scanners (in close cooperation with the machine manufacturers), the optimization of the machine setups for contrast imaging and the education provided to clinical users in form of workshops, webinars, textbooks and scientific congresses.

Experimental investigation of micro-machining of borosilicate glass using an ultrasonic assisted rotary electrochemical discharge machining (UA-RECDM) process

Electrochemical discharge machining is an advanced micro-machining process for machining of conductive as well as non-conductive hard and brittle materials, e.g. glass, ceramics, silicon wafer, etc. The present work explores the machining of glass using an in-house developed novel ultrasonic assisted rotary electrochemical discharge machining setup. The setup has specialized features, such as using ultrasonic vibrations to tool the electrode and incorporating rotary motion for manipulating the workpiece. Experiments were conducted using a one factor at a time approach by varying the tool feed rate, the amplitude of the vibrations and the rotation of the workpiece as process parameters. The quality of the machining output was evaluated by observing two key parameters: the overcut and the circularity of the hole. It was observed that as the workpiece rotation speed increased from 40 rpm to 60 rpm, the overcut in the machined samples decreased from 181.378 μm to 163.564 μm. The rotary motion of the workpiece caused a seeping action of the electrolyte in the hydrodynamic regime, leading to the formation of a thin gas film and the stabilization of the discharging process. The morphology of the machined hole exhibits better circularity, low heat-affected zones, minimum micro-cracks and smooth edges at its periphery due to stable discharge formation.

Les défis de la transplantation rénale avec artères multiples

Renal transplantation is a crucial procedure for treating end-stage chronic kidney disease, but the shortage of grafts has led to the utilization of kidneys with more complex anatomical features, such as multiple arteries. This article aims to provide surgeons with various surgical strategies based on the number of arteries, their topography, as well as the presence or absence of an associated patch. It also offers guidance on adapting organ retrieval techniques and perfusion machine setup in this type of complex anatomy.

Continuously extruded wrapped tow reinforced truss beams

Recent advancements in composite truss structures have achieved high structural efficiency by combining truss geometries and composite materials within scalable manufacturing processes. Filament winding-based approaches, such as the Wrapped Tow Reinforced (WrapToR) truss process, allow for simpler machine design compared to braiding or pultrusion but face limitations as production rates and truss beam lengths increase. We herein introduce WrapToR Trusstrusion, a manufacturing concept using coaxial winding heads to wrap multiple pre-wetted tows around continuously fed chord members, forming a complete wound truss structure in one pass. This eliminates the need for reciprocating motion and fixed mandrel lengths of conventional winding machines. We present the WrapToR Trusstrusion concept, the first prototype machine setup, and the numerical analysis and mechanical characterisation of produced specimens, aiming to assess the quality of the produced trusses. This innovative machine concept progresses towards high-throughput, continuous production of mechanically superior WrapToR truss beams while trading off process and geometry versatility for standardisation and production rate.

Deep reinforcement learning for adaptive flexible job shop scheduling: coping with variability and uncertainty

ABSTRACT The frequently evolving manufacturing system necessitates real-time large-scale data analytics, surpassing the capabilities of classical systems or human skills, necessitating knowledge development and self-adaptive control. Unpredictable real-time events in smart factories cause changes in the effectiveness of planned schedules or tasks; even slight interruptions can accumulate to make the preplanned schedule unoptimized, if not impossible. To meet the required production efficiency, typical dynamic scenarios on the shop floor, such as failures, random job arrivals, and machine setup, must be addressed quickly. Hence, this study proposes a novel Triple Deep Q Network (TDQN) approach for learning high-quality dispatching rules for addressing the Flexible Job Shop Scheduling Problem (FJSSP) in uncertainty. For the dispatching rule-based FJSSP, the Markov Decision Process (MDP) to choose a suitable operation-machine (O-M) pair is formulated to allow operation selection and resource assignment resolutions to be made simultaneously. The performance of the TDQN method is investigated against the Double Deep Q Network (DDQN) and Deep Q Network (DQN) approach and found to be more stable. Moreover, among the 29 different shopfloor configuration settings, TDQN performed better in 62.07% of the overall instances, while DDQN and DQN performed better in only 31.03% and 6.9% of instances, respectively.

Intelligent parameters reconfiguration system for enhancing machine tools sustainability using real-time data-driven: an experimental cutting speed investigation

ABSTRACT Industrial manufacturing is not trivial and complex since there are dimensional and tolerance product changes, differences in raw material and variations in machine models. Hence, this research proposes an Intelligent Parameters Reconfiguration System (IPRS) approach for enhancing manufacturing performance and extending cutting tool lifespan through detailed machining parameter setup. The approach joins real-time data acquisition and cutting-edge machine learning techniques to improve the turning machining setup. This research uses related works to conceptualise the IPRS structure in four main steps: machining sensing, data acquisition, data processing and parameters prediction, and automatic machine reconfiguration. The approach enabled dynamic adjustments in cutting speed based on predicted wear, resulting in a notable reduction of 5.6 minutes in manufacturing time and an improvement of 0.2 µm in surface finish. However, it is important to highlight that the experimental solution evaluation was carried out in a controlled scenario using a potentiometer to control the cutting speed on CNC lathes. Therefore, the applicability and scalability of the solution in a real scenario have significant limitations, and the cutting speed control must have direct integration with the machine’s numerical control. As future research, the IPRS approach should consider other influential parameters like cutting depth and feed rate.

Setup Time Reduction for Toothpaste Tube Filling Machine: A Case Study of a Contract Toothpaste Manufacturing Company

This research aimed to find ways to reduce the idle time and improve the toothpaste tube filling machine setup procedure. The toothpaste manufacturing industry has many kinds of products nowadays, so the production process would need to frequently change its production plan, thus causing the number of machine setup times to increase. As such, the toothpaste tube filling machine would be unable to run simultaneously. According to the study, the average machine setup time on the filling machine was 157.1 minutes per time. This was more than the planned machine setup time of about 67.1 minutes, or 74.55 percent. The researchers presented the single-minute exchange of die and the eliminate, combine, reduce, and simplify approaches to reduce the average time of the machine setting by about 124.6 minutes per time, or 20.69 percent. The evaluation results of the process performance before and after the improvement by measuring the overall equipment efficiency from 41.66 percent to 58.61 percent increased by 16.95 percent.