Set Of Machines Research Articles

EDM SURFACE ANALYSIS OF MOSI2–SIC INTERMETALLIC CERAMIC COMPOSITE WITH ENHANCED FORM AND ORIENTATION TOLERANCES

Electrical discharge machining (EDM) was performed on a copper electrode and the implications of changing factors such as current, pulse on time, pulse-off time, spark gap voltage and speed have been investigated in this research. The process used an intermetallic MoSi2–SiC ceramic composite. Multiple performance characteristics, including material removal rate, electrode degradation rate and surface roughness, run out, radial overcut, circularity, cylindricity and perpendicularity were considered when optimizing these parameters using the Taguchi-based [Formula: see text]Orthogonal Array with Design of Experiments (DoE). For this purpose, techniques such as analysis of variance (ANOVA), Response Surface Methodologies (RSM) and graphical analysis were utilized. The outcomes revealed that geometric tolerances were decreased as a consequence of significant improvements in the rates of material removal, tool degradation, form tolerance, and orientation tolerance. The optimal machining settings for producing high-quality holes and electrodes in the conductive MoSi2–SiC composite were determined by scanning electron microscopy (SEM) testing the anomalies of the machined composite for various holes of trials. The experimental results suggest that the precision, presence of microvoids and surface roughness of the copper electrode can be enhanced through selecting an appropriate optimal combination.

Optimizing energy efficiency in unrelated parallel machine scheduling problem through reinforcement learning

The industrial sector plays a significant role in global energy consumption and greenhouse gas emissions. To reduce this environmental impact, it's crucial to implement energy-efficient manufacturing systems that utilize sustainable materials and optimize energy usage. This can lead to benefits such as reduced carbon footprints and cost savings.In recent years, metaheuristic approaches have been focused on minimizing energy consumption within the Unrelated Parallel Machine Scheduling Problem (UPMSP). Traditional methods often overlook complex factors like release dates, due dates, and job setup times. This research introduces a novel algorithm that integrates reinforcement learning (RL) with a genetic algorithm (GA) to address this gap.The proposed RLGA algorithm, rooted in the dynamic field of evolutionary reinforcement learning, breaks down policies into smaller components to isolate essential parameters for problem-solving. Through comprehensive analysis, hyperparameters that influence optimal results are identified, facilitating automated hyperparameter selection and optimization. The expert system takes into account problem characteristics such as machine or job saturation, job overlap, and the maximum values of target variables, allowing instances to be grouped into clusters. These clusters are solved using a genetic algorithm with varying combinations of mutation and crossover hyperparameters. The most suitable approach for each cluster is determined by analyzing the results, and this configuration of hyperparameters is applied iteratively to optimize the solution search.The effectiveness of RLGA is evaluated across benchmark instances with different complexities, machine sets, jobs, and constraints. Comprehensive comparisons against existing methods highlight the superior performance and efficiency of RLGA in optimizing energy use and solution quality. Experimental results show that RLGA outperforms well-known solvers like CPO, CPLEX, OR-tools, and Gecode, making it a promising approach for optimizing energy-efficient manufacturing systems.

The Cleaning process simulation by Glass Test Boards

This article explains the utilization of Glass Test boards for different tasks for the cleaning process building, optimization, and monitoring. We have 12 years of experience building and using Glass Test Boards as a precise tool for comparing material properties, machine settings, and process optimization. Some years ago, we decided to do a joint project with the company Magnalytix and build another kind of test board capable of being optical inspected and tested on the surface resistance. We want to share our experiences and show the advantages of utilizing such glass models for enhancing the quality of the cleaning process and lowering the cost of building the process.

Harmony search algorithm to enhance approach to permutation flow shop scheduling problem

The permutation flow-shop scheduling problem (PFSP) is one of the widely analysed and investigated problems within the general field of scheduling, as its major focus is on the allocation and sequencing of a set of jobs across a set of machines in order to minimize the makespan or satisfy other criteria. This paper further generalizes the problem to the distributed permutation flow-shop scheduling problem (DPFSP), namely in a manufacturing system with multiple factories, where every factory employs identical machines; with the objective of achieving the least makespan at the worst factory in terms of processing time. We resolve the issue through a Harmony Search Algorithm (HSA), a metaheuristic model developed from the musical harmonisation concept, together with constraint programming techniques, including interval variables and non-adjacent constraints. The problemsolving algorithm incorporates a lower bound estimation heuristic for efficiently directing the search in the right possible solution zone. Performance evaluation on both the small benchmark and the large benchmark data set showed the ability of the HSA to perform the different assignment in various problem scenarios that include the different number of jobs, machines and factories to support them. Comparisons with the best-known values established the performance of the HSA algorithm in identifying high quality solutions for the PFSP in less computation time.

Multi-objective optimization in EDM of functionally graded Fe-Al using grey relational analysis

Abstract Functionally Graded Materials (FGMs) are multipurpose materials with a specific goal of managing changes in structural, thermal, or functional qualities. They feature a spatial variation in microstructure and/or composition. The current work prepared three layers of sample with different chemical compositions of Fe-Al FGM (50 Al-50 Fe, 45 Al-55 Fe, and 40 Al-60 Fe) at. % produced by powder metallurgy, then studied the machining behavior of these samples. The literature on the machining behavior of FGM was reviewed, and the influence of electrical discharge machine (EDM) process parameters such as voltage (V), current (I), pulse-on time, and pulse-off time on performance characteristics (material removal rate (MRR), tool wear rate (TWR), and surface roughness (Ra)) was investigated. The optimal machining settings for Fe-Al FGM samples will be ascertained by use of Grey Relational Analysis (GRA), which is based on the Taguchi approach, after an experimental investigation of the L18 orthogonal array design of trials. The GRA findings verify that V 140 volts, Ip10 A, Ton 100 μs, and Toff 75 μs is the optimal combination of process parameters. It has been found that the voltage is more significantly affected than the rest of the input parameters to obtain greater material removal rate (MRR) and lower tool wear rate (EWR) and surface roughness (Ra) through the response table. According to the study, choosing the right process parameters can improve the multi-performance feature.

Convergence of Hybrid Grey Wolf Optimization with Heuristic Approaches for Enhanced Job Shop Scheduling

This scholarly inquiry examines the utilization of the Hybrid Grey Wolf Optimization Algorithm (HGWOA) in addressing the Job Shop Scheduling Problem (JSSP), a combinatorial optimization problem commonly encountered within production management. The central aim is to minimize makespan, defined as the cumulative duration necessary to finalize all tasks on a designated set of machines while observing precedence constraints. Conventional Optimization methodologies frequently encounter difficulties with intricate instances of JSSP owing to its NP-hard classification. We introduce a ground-breaking method the Grey Wolf Optimization Algorithm (GWOA) with various meta-heuristic strategies to augment its fruitfulness in resolving JSSP. The multiple findings underscore the usefulness of HGWOA, highlighting its prospective applicability in real-world contexts of production scheduling and management.

Assessment of supply chain efficiency through a sustainable supply chain management strategy in the textile sector

The textile industry processes fiber into yarn or fabric. Problems often faced by textile companies are delays in raw materials, which result in the production and distribution of fabrics not reaching the target, so consumers are unsatisfied and switch to competing companies. In addition, solid waste from the production process is still disposed of, potentially polluting the environment. This study uses the Green Supply Chain Operation Reference (Green SCOR) approach to evaluate the performance of green supply chain management (GSCM) in textile companies located in Yogyakarta. The findings of this study could significantly impact the textile industry as they provide a comprehensive evaluation of GSCM performance. Twenty-two performance indicators have been validated using the Item-Content Validity Index (I-CVI) method and weighted using the Analytical Hierarchy Process (AHP) method. Based on the calculation results, the GSCM performance value is 60.208. Improvement recommendations are given for three indicators that need to be prioritized based on Importance Performance Analysis (IPA) matrix mapping and the actual condition of the company. Improvements can be made by using the Vacuum Flash Evaporation method so that recycled PVA can be reused, using natural materials or alternative auxiliary materials that are more environmentally friendly, increasing cooperation with companies engaged in processing weaving waste or textile waste appropriately and effectively, checking the concentration of starch solution not only when changing construction but every beam change, and forming a checklist form that is filled in for monitoring after machine settings and preparation of the production process.

Improved of Process Production Disc Car Whell Type PSD3K (City Car Type)

This research accelerates the production process with the aim of increasing productivity from Manufacturers engaged in the manufacture of automotive parts currently experiencing an increase in sales orders on the other hand facing problems in productivity due to cracked side dies in rim production to 1400 pcs. The cracking of the side die is due to thermal shock that occurs to accelerate the production process, as a result of which cracks occur in the side die. With the concept of conducting research in the field of direct production then analyzing the characteristics of mold materials, die characteristics, process characteristics, characteristics of finished products before and after changes by testing the results of the laboratory of measuring instruments and conducting trials of variations in machine setting parameters, variations in the production process and products produced. The experiment involved changing the standard temperature from 520 °C–545 °C to 532 °C–538 °C and reducing the immersion time from a minimum of 270–540 seconds to 332 seconds. It reduces the soaking time from 69 seconds to 46 seconds and the aging time from 190 seconds to 180 seconds, increasing the casting productivity from 194,870 Pcs/28 days to 213,311 Pcs/28 days from seven machines, thus meeting the customer's requirement of 200,000 Pcs/28 days without cracking side die. Durability testing on five product samples in accordance with TSD5605G standards confirms that the quality meets customer specifications. The results of this study prove that SKD6 matrial is much stronger than FCD550 against thermal shock. Keywords: thermal shock, die disc car wheel, manufacturing, automotive parts, casting productivity

The survey on the dual nature of xAI challenges in intrusion detection and their potential for AI innovation

In the rapidly evolving domain of cybersecurity, the imperative for intrusion detection systems is undeniable; yet, it is increasingly clear that to meet the ever-growing challenges posed by sophisticated threats, intrusion detection itself stands in need of the transformative capabilities offered by the explainable artificial intelligence (xAI). As this concept is still developing, it poses an array of challenges that need addressing. This paper discusses 25 of such challenges of varying research interest, encountered in the domain of xAI, identified in the course of a targeted study. While these challenges may appear as obstacles, they concurrently present as significant research opportunities. These analysed challenges encompass a wide spectrum of concerns spanning the intersection of xAI and cybersecurity. The paper underscores the critical role of xAI in addressing opacity issues within machine learning algorithms and sets the stage for further research and innovation in the quest for transparent and interpretable artificial intelligence that humans are able to trust. In addition to this, by reframing these challenges as opportunities, this study seeks to inspire and guide researchers towards realizing the full potential of xAI in cybersecurity.

REVIEW OF TEXTILE PRINTING: DIFFERENTIATION OF BLOCK, SCREEN & DIGITAL PRINTING

In India is well known for its traditional culture and art features. Hand block printing techniques are practiced in Rajasthan. Hence, the block printing is a slow process. Printing process of desired pattern is defined and repeated. The various styles for block printing are significant of the country in rich culture and heritage. It is a unique of each region will have a different variations of traditional craft. That was crafted for propagating Hindus mythology of depicted folk style story transform of imagination forms of God and goddess in literature. In this article, we will discuss about block printing, the development digital printing and manual of screen printing. The comparison of these three styles of textiles printing. It is a oldest method of art craft. The economics of ink jet printing are the main driver of digital printing technique. The costs of ink jet printing are lower for short run lengths than in traditional screen printing. This switches for longer runs production. Short-run textile printing commissions are the main source of jobs for small to medium scale textile producers in Ghana. And manual screen printing is the main process employed by these small-scale textile printers. These layers of limitations negatively affect the overall outcome of the prints. This is because, the large-scale textile factories in Ghana can print a minimum of 2400 yards due to their machine settings, calibration and running cost to make the least returns. KEYWORDS: Hand block printing, natural dyes, screen printing, techniques, digital printing.

Precision Calibration in Wire-Arc-Directed Energy Deposition Simulations Using a Machine-Learning-Based Multi-Fidelity Model

Thermal simulation is essential in wire-arc-directed energy deposition (W-DED) to accurately estimate temperature distributions, impacting residual stress and distortion in components. Proper calibration of simulation models minimizes inaccuracies caused by varying material properties, machine settings, and environmental conditions. The lack of standardized calibration methods further complicates thermal predictions. This paper introduces a novel calibration method integrating both machine learning, as the high-fidelity (HF) model, and response surface modeling, as the low-fidelity (LF) model, within a multi-fidelity (MF) framework. The approach utilizes Bayesian optimization to effectively explore the search space for optimal solutions. A two-tiered model employs the LF model to identify feasible regions, followed by the HF model to refine calibration parameters, such as thermal efficiency (η), convection coefficient (h), and emissivity (ε), which are difficult to determine experimentally. A three-factor Box–Behnken design (BBD) is applied to explore the design space, requiring only thirteen parameter configurations, conserving resources and enabling robust model training. The efficacy of this MF model is demonstrated in multi-layer W-DED calibration, showing strong alignment between experimental and simulated temperatures, with a mean absolute error (MAE) of 7.47 °C. This method offers a replicable framework for broader additive manufacturing processes.

AUTOMATIC MACHINE TOOL ALIGNMENT ON ITS FOUNDATION

This article deals with the issue of the automatic setting of machines on their base. The focus is on machines with a self-supporting frame, freestanding, on more than three supports. The alignment uses the previously presented method of precise and repeatable machine placement on the foundation. As a result, the machine has the necessary parameters in terms of its geometry, and at the same time a defined weight distribution on the individual building elements is guaranteed. The paper presents a new method for automatic machine alignment using so-called “smart feet”. Due to this, the process of machine alignment is clearly defined, repeatable and does not depend on the experience of the personnel performing the alignment. The presented algorithm makes it possible to automate the entire machine-setting process. The article includes the presentation of a machine placement on its base, which is used for testing and verification of the functionality of the alignment algorithm.

PEMBUATAN BRACKET FINISH PANEL MOUNTING D74 MENGGUNAKAN MESIN PRESS STAMPING PUX 100

This practical work report describes the process of making part bracket finish panel mounting type d74 using PUX 100 stamping press machine and the operating procedures of stamping press machine at PT Persada Selaras Indoensia. The aim is to know directly during the process of making part bracket finish panel mounting type d74, and to know how to operate the stamping press machine. The manufacturing process of bracket finish panel mounting type d74 consists of several stages starting from material checking using SPC440, product design checking, Stamping Press process with four stages of production process (blanking, forming, bending, & Pierching). In addition, this report discusses how to operate the stamping press starting from the machine inspection stage to the stamping press machine setting. Through this practical work report, it is hoped that readers can gain insight into the process of making bracket finish panel mounting type d74 parts and the company's contribution in maintaining product quality. Keywords : Stamping Press, SPC440, Production Process

Optimisation of Sentiment Analysis for E-Commerce

Sentiment analysis is widely used today to make data-driven decisions in different industries, starting from marketing and including brand management, reputation monitoring, and customer satisfaction analysis. Its growing importance is closely linked with so-called ‘word-of-mouth’ communication, from reading online reviews to writing comments on social networks. Effective separation of sentiments ensures that companies' responses are timely and critical patterns are seen in big data sets. Statistical measures, information gain, correlation-based approaches, etc, have been employed for the feature selection. Still, the problem associated with text data mining is that they don’t convey the text's relative difficulty and additional features. To fill this gap, our research proposes a new feature selection technique through Ant Colony Optimization (ACO) and K Nearest Neighbour (KNN) performed on 28,000 customer reviews in different product categories. The results, therefore, showed an overall accuracy of 80.1%, with the Support Vector Machine (SVM) set at 80.5% on each selected feature, which was slightly higher than the Convolutional Neural Network (CNN), which scored a 78.41% accuracy. SVM remains on the mark of 83%, and for CNN, the rate achieved on the same was 80.8% when both were applied to the entire dataset. These facts rejected the infallibility of the simple and complex algorithms used singly in the sentiment classification, indicating that more sophisticated algorithms like ACO and KNN can provide business solutions to improve their service delivery based on customers’ feedback.

Computational Fluid Dynamics Analysis of Erosion in Active Components of Abrasive Water Jet Machine

This study presents a comprehensive three-dimensional computational fluid dynamics (CFD) analysis of abrasive fluid flow and its erosive effects on the active components of the WUXI YCWJ-380-1520 water jet cutting machine. The research investigates the behavior and impact of abrasive particles within the fluid, determining the erosion rates for particles with diameters of 0.19 mm, 0.285 mm, and 0.38 mm (dimensions resulting from the granulometry of the experimentally established sand), considering various abrasive flow rates. The methodology includes a detailed granulometric analysis of the abrasive material, identifying critical particle sizes and distributions, with a focus on M50 granulation (average particle size of 0.285 mm). Additionally, the study employs the Wadell method to determine the shape factor (Ψi = 0.622) of the abrasive particles, which plays a significant role in the erosion process. Experimental determination of the abrasive flow rate is conducted, leading to the development of a second-order parabolic model that accurately predicts flow variations based on the control settings of the AWJ machine. The maximum erosion occurs at the entry surface of the mixing tube’s truncated zone, with a higher intensity as the particle size increases. For the 0.19 mm particles, the erosion rates range from 1.090 × 10−6 kg/m2·s to 2.022 × 10−6 kg/m2·s and follow a parabolic distribution. The particles of 0.285 mm show erosion rates ranging from 2.450 × 10−6 kg/m2·s to 6.119 × 10−6 kg/m2·s, also fitting the second-order parabolic model. The largest particles (0.38 mm) exhibit erosion rates ranging from 3.646 × 10−6 kg/m2·s to 7.123 × 10−6 kg/m2·s, described by a third-order polynomial. The study concludes that larger particle sizes result in higher erosion rates due to their increased mass and kinetic energy. Therefore, the present investigation demonstrates a significant relationship between particle size, abrasive flow rate, and erosion rate, highlighting critical wear points in the machine’s components. The findings contribute to optimizing the design and operational parameters of water jet cutting machines, thereby enhancing their efficiency and lifespan.

Optimasi Human Error pada Proses Produksi Air Minum Dalam Kemasan di PT. Telaga Tanjung Pomosda Nganjuk

PT. Telaga Tanjung Pomosda, a bottled mineral water manufacturer in Nganjuk, East Java, faces challenges in maintaining high production standards and addressing operational issues such as product rejection and production process errors, impacting overall company efficiency and reputation. This study identifies operational error factors and evaluates the effectiveness of improvement methods. Research procedures utilize HTA, SHERPA, and Poka-Yoke to identify and reduce human errors in bottled mineral water production at PT. Telaga Tanjung Pomosda Nganjuk, focusing on error analysis, probability assessment, and Poka-Yoke strategy implementation. The bottled water packaging process involves critical stages such as cup packaging, pressing, and packing. Errors in each stage can result in delays, decreased water volume, imperfectly pressed lids, and packing mistakes, hindering the entire production process. Research at PT. Telaga Tanjung Pomosda identifies operator issues in machine settings, water measurement, lid positioning, task interruptions, packaging, and cup positioning, with solutions including routine checks and training to enhance product quality

Artificial intelligence based experimental investigation of underwater fiber laser transmission process during micro-channelling operation on PMMA

Industries that engage in laser-based material processing are increasingly turning to fiber lasers as one of the most effective laser systems available. Using a pulsed fiber laser system, an experimental investigation of underwater laser micro-channelling on hard to machine thick PMMA has been conducted in this research work. Submerged laser transmission cutting which is also known as underwater laser induced backside machining helps to generate clean kerf edge without presence of mushy region, which is very common during laser machining of thick PMMA with wavelength of near infrared region. Submerged conditions are used here for laser transmission micromachining in order to reduce adverse thermal effects and microcracking or charring inside the material. Pulse frequency, working power, and cutting speed have been selected as the input parameters. As machining responses, the depth of the micro-channel, the width of the kerf, and the width of the heat affected zone (HAZ) have been considered. To find the ideal parametric condition for simultaneously achieving numerous goals, Response Surface Methodology (RSM) and AI-based Teaching Learning-Based Optimisation (TLBO) have been used. The TLBO technique determined that the optimal laser machining settings are a power setting of 13.98 %, a pulse frequency of 50 kHz, and a cutting speed of 0.20 mm/sec. These parameters result in a minimum HAZ width of 4.1874 µm, a minimum kerf width of 22.3698 µm, and a maximum depth of cut of 37.1478 µm. Underwater laser processing reduces heat affected zone and redeposition surrounding micro-channels, creating a cleaner, finer structure.

Stochastic geometry models for texture synthesis of machined metallic surfaces: sandblasting and milling

Training defect detection algorithms for visual surface inspection systems requires a large and representative set of training data. Often there is not enough real data available which additionally cannot cover the variety of possible defects. Synthetic data generated by a synthetic visual surface inspection environment can overcome this problem. Therefore, a digital twin of the object is needed, whose micro-scale surface topography is modeled by texture synthesis models. We develop stochastic texture models for sandblasted and milled surfaces based on topography measurements of such surfaces. As the surface patterns differ significantly, we use separate modeling approaches for the two cases. Sandblasted surfaces are modeled by a combination of data-based texture synthesis methods that rely entirely on the measurements. In contrast, the model for milled surfaces is procedural and includes all process-related parameters known from the machine settings.

A Proven Translation from a UML State Machine Subset to Timed Automata

Although Unified Modeling Language (UML) state machines constitute a convenient modeling formalism that is widely used in many applications, the lack of formal semantics impedes carrying out automatic processing, such as formal verification. In this article, we aim to achieve a proven translation from a subset of UML state machines to timed automata. A generic abstract syntax is defined for state machines that allows us to specify state machines as a tree-like structure, explicitly illustrating the hierarchical relationships within the model. Based on this syntax, a formal asynchronous semantics for state machines and systems of state machines is established. Additionally, the semantics of timed automata is specified. Then, a translation relation from the considered set of state machines to timed automata is defined and a strong equivalence relation — namely, a timed bisimulation between the source and target models — is formally proven. The proof is carried out inductively while considering continuous (time) and discrete transitions separately. This proof allows us to demonstrate a strong similitude between these models.

Quality Control Circles Technique for Reducing Fine Bored Parts Rejects After Tool Change

This study aims to assess reduction of rejects after tool change in a crankshaft manufacturing industry using Quality Control Circles (QCC) technique. Four months production data of crankshaft was collected and analyzed in 2022 in one of the largest automotive industries in Indonesia. Fine boring process was identified as the largest contributor of rejects. QCC activities were conducted to streamline the type of rejects in fine boring. QCC introduced a new intermediate action in calibrating machine setting after tool change. The results show a significant reduction in the level of rejection from 35% to just 5% of total rejects in the fine boring process after this QCC method was implemented. This result shows that this QCC method, by brainstorming among employees, can continuously improve and solve the problem in the workplace. This QCC method not just reduce the reject rate but also increasing morale, safety maintenance for employee, smoothness in supplying the next process, and reducing the cost (QCDSM). The limitation of this method is high dependency to the knowledge, experience and skill of the QCC team. Development of QCC team in term of new knowledge may lead to future improvement opportunity through development of quality control techniques with incorporation of internet of things or smart devices.