programs for machines Research Articles (Page 1)

ABSTRACT Background Code smells are an indicator that something is misplaced in software systems, which can reduce maintainability and quality of software applications. In the literature, there is much research about code smells where numerous strategies for automating code smell detection have been developed to improve software quality. Objective The purpose of this work is to provide a review of search‐based, heuristic‐based, machine learning‐based, deep learning‐based, and hybrid‐based code smell detection algorithms. Method Concerning the main goals of this research, we have found 38 primary studies. We gathered relevant studies published on this topic between 2017 and 2024. These articles' data were extracted according to some criteria, including code smells, machine learning methods, programming languages, dataset size, evaluation strategy, and statistical tests. Results Machine learning‐based code smell detection methods have been suggested in most empirical investigations. We found that machine learning and deep learning are the most popular approaches for predicting code smells. Researchers typically employ support vector machine and decision tree algorithms. The Arcelli Fontana and Zanoni benchmark dataset was the most frequently investigated dataset. Most of the research community's attention has been focused on a small number of smells, including blob, feature envy, long method, and data class. Researchers also pay more attention to code smells like Long Method and Feature Envy. Deep learning techniques are increasingly used, and most scientists utilize source code metrics as indicators. The standard performance measurements mostly used are F‐measure, recall, and precision. Conclusions The study provides an overview of existing approaches and highlights current research trends in code smell detection, particularly the increasing use of machine learning and deep learning techniques.

Vocational education systems face increasing pressure to deliver high-quality skills training while ensuring resource efficiency, safety, and scalability. In machining programs, traditional hands-on training relies heavily on physical equipment, consumables, and close supervision, posing challenges for sustainable management. This study employs a quasi-experimental design with pretest–posttest measures and a comparison group to examine the effects of VR-based pre-training with 50 first-year vocational students. The findings indicate that VR-based preparation supports learners’ cognitive and experiential readiness and contributes to perceived preparedness for subsequent hands-on activities. No statistically significant differences in posttest performance were observed between groups. VR-based preparatory training supports risk mitigation in learning contexts by enabling cognitive rehearsal and structured procedural familiarization before physical practice. At the system level, VR-based pre-training transforms early-stage trial-and-error learning into a guided virtual environment that incorporates predefined operational sequences, procedural cues, and embedded safety prompts. This approach helps reduce safety risks for inexperienced learners and supports the more strategic use of instructional resources. Rather than establishing generalized or causal effects, the findings provide exploratory, empirically grounded insights derived from a single institutional context, offering a structured reference framework to inform the design, scaling, and validation of future multi-site or longitudinal research in vocational education management. Furthermore, the study explicitly aligns with Sustainable Development Goals 4 (Quality Education) and 8 (Decent Work and Economic Growth). This alignment underscores the study’s relevance to sustainability-focused vocational training initiatives.

ABSTRACT In this study, the simulation of groundwater fluctuations in the Kermanshah Plain is carried out via three advanced machine learning algorithms, i.e., GMDH, weighted extreme learning machine (W‐ELM) and gene expression programming (GEP). The objective of this research is to evaluate and compare the accuracy of different machine learning models in simulating and predicting such fluctuations under different conditions. The data used in this study include the monthly groundwater level, precipitation, evaporation and temperature data from 2002 to 2020. To increase the accuracy of the models, the data are processed via the min–max normalisation approach. The results obtained from the evaluation of the models using different efficiency indices, such as the root mean square error (RMSE), R 2 and NSE, indicate that the W‐ELM model, which has the best performance in both the training and testing phases, is more exact than the GMDH and GEP models. In particular, the W‐ELM achieves R 2 = 0.999, NSE = 0.979, PBIAS = 0.012 and RMSE = 0.093 in the testing phase, which indicates its high accuracy and efficiency. The results of the sensitivity analysis indicate that all the models are highly sensitive to the groundwater level lag variable GWL₋₁.This research shows that machine learning models, especially the W‐ELM model, can be utilised as effective tools for predicting groundwater level variation and the optimal management of water resources in different regions.

One of the most critical aspects of turning, and machining in general, is the surface roughness of the finished product, which directly influences the performance, functionality, and longevity of machined components. The accurate prediction of surface roughness is vital for enhancing component quality and machining efficiency. This study presents a machine learning-driven framework for modeling mean roughness depth (Rz) during the dry machining of super duplex stainless steel (SDSS 2507). SDSS 2507 is known for its exceptional mechanical strength and corrosion resistance, but it poses significant challenges in machinability. To address this, this study employs flank-face textured cutting tools to enhance machining performance. Experiments were designed using the L27 orthogonal array with three continuous factors, cutting speed, feed rate, and depth of cut, and one categorical factor, tool texture type (dimple, groove, and wave), along with surface roughness as an output parameter. Gaussian Data Augmentation (GDA) was employed to enrich data variability and strengthen model generalization, resulting in the improved predictive performance of the machine learning models. MATLAB R2021a was employed for preprocessing, the normalization of datasets, and model development. Two models, Least-Squares Support Vector Machine (LSSVM) and Multi-Gene Genetic Programming (MGGP), were trained and evaluated on various statistical metrics. The results showed that both LSSVM and MGGP models learned well from the training data and accurately predicted Rz on the testing data, demonstrating their reliability and strong performance. Of the two models, LSSVM demonstrated superior performance, achieving a training accuracy of 98.14%, a coefficient of determination (R2) of 0.9959, and a root mean squared error (RMSE) of 0.1528. It also maintained strong generalization on the testing data, with 94.36% accuracy and 0.9391 R2 and 0.6730 RMSE values. The high predictive accuracy of the LSSVM model highlights its potential for identifying optimal machining parameters and integrating into intelligent process control systems to enhance surface quality and efficiency in the complex machining of materials like SDSS.

This letter introduces Robotic Augmented Reality for Machine Programming by Demonstration (<sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rampa</small>), the first ML-integrated, XR-driven end-to-end robotic system, allowing training and deployment of ML models such as ProMPs on the fly, and utilizing the capabilities of state-of-the-art and commercially available AR headsets, e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Meta Quest 3</i>, to facilitate the application of Programming by Demonstration (PbD) approaches on industrial robotic arms, e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Universal Robots UR10</i>. Our approach enables <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> data recording, visualization, and fine-tuning of skill demonstrations directly within the user's physical environment. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rampa</small> addresses critical challenges of PbD, such as safety concerns, programming barriers, and the inefficiency of collecting demonstrations on the actual hardware. The performance of our system is evaluated against the traditional method of kinesthetic control in teaching three different robotic manipulation tasks and analyzed with quantitative metrics, measuring task performance and completion time, trajectory smoothness, system usability, user experience, and task load using standardized surveys. Our findings indicate a substantial advancement in how robotic tasks are taught and refined, promising improvements in operational safety, efficiency, and user engagement in robotic programming.

Opening an e-commerce business offers tremendous flexibility and convenience. Unlike traditional stores, an online business allows entrepreneurs to operate from anywhere at any time. The purpose of this research is to assist people who wish to start an e-commerce business without having to invest a lot of money and time in the website building. For the building of the e-commerce website in our research, we use the Drupal open-source framework and Cloud Platform as a service (PaaS). Cloud PaaS provides a dependable, effective, secure, and stable business.Then, we perform the network simulations using a virtual machine program known as GNS3. Additionally, Cloud PaaS gives users flexibility by enabling them to access it from any location and instantly expand the capacity of their server. The result of this research allows the users that is in the inside of the zone and dmz zone to access the Internet. Additionally, users from outside of the zone are able to access the http://192.168.3.3/drupal and the drupal servers

CNC machines allow computer programming to control the movement and actions of the machine, enabling more precise and repeatable production. However, in operating a CNC machine, good and efficient programming is essential. To optimize the use of CNC machines, it is necessary to optimize the number of NC program blocks that include the concept of macros and sub-programs. An effective CNC program, along with the proper use of macros and sub-programs, can speed up the production process, reduce human error, and increase accuracy. The research method used is comparative research by comparing using macro programs and manual programming, in this case comparing the number of solid trajectories and the number of program lines in the design using the simulation method using the Autodesk Fusion 360 application. So that the optimal number of movements will be obtained through the row value in the NC program. Macros and sub- programs allow users to easily change or modify CNC programs without having to rewrite the entire program. This increases flexibility in producing various types of products. It is concluded that optimization using macro and sub-programs provides a significant reduction in the number of program blocks in the case of the machining process for manufacturing the cover of pressure cylinder. This reduction in program blocks can help improve efficiency and sustainability in the development of modified machining processes.

Production costs are always the top concern of company managers in improving production and business efficiency. The cost of energy is one of the major costs that manufacturing companies must pay. This research paper proposes a Hybrid Six Sigma method based on fuzzy Multi-Attribute Decision Making (MADM), Industry 4.0, and digital numerical control (DNC). A fuzzy MADM method to select problems to improve and build an Industry 4.0 system with Internet of Things (IoT) devices, calling for automatic machining programs using Radio Frequency Identification (RFID) systems and management. Manage production equipment maintenance system using a digital numerical control (DNC) system. Measuring industry 4.0 system user satisfaction in manufacturing using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results of research on applying industry 4.0 techniques to the induction heat treatment process eliminate the dependence on worker skills and simplify the operation of the induction heat treatment process. Improve employee satisfaction with process operating conditions. Reduce the cost of electrical energy arising due to the coil maintenance system by applying the Industry 4.0 system. The result after the improvement is that the defect rate decreased from 47.2% to 4.9%. In terms of money, the reduction in losses due to defects is reduced from 6,593 USD per year to 549 USD per year. This research paper builds a sample continuous improvement model to apply to other production processes at other manufacturing companies in terms of applying industry 4.0 systems with IoT devices such as RFID and barcode readers in operations. automatically call the machining program of the machining machine and build an autonomous and preventive maintenance system using the industry 4.0 system to make improvements in process automation, smart data management, and analytics, using Internet of Things (IoT) to connect devices in the production process create a flexible production process.

From logical reasoning to mental simulation, biological and artificial neural systems possess an incredible capacity for computation. Such neural computers offer a fundamentally novel computing paradigm by representing data continuously and processing information in a natively parallel and distributed manner. To harness this computation, prior work has developed extensive training techniques to understand existing neural networks. However, the lack of a concrete and low-level machine code for neural networks precludes us from taking full advantage of a neural computing framework. Here we provide such a machine code along with a programming framework by using a recurrent neural network—a reservoir computer—to decompile, code and compile analogue computations. By decompiling the reservoir’s internal representation and dynamics into an analytic basis of its inputs, we define a low-level neural machine code that we use to program the reservoir to solve complex equations and store chaotic dynamical systems as random-access memory. We further provide a fully distributed neural implementation of software virtualization and logical circuits, and even program a playable game of pong inside of a reservoir computer. Importantly, all of these functions are programmed without requiring any example data or sampling of state space. Finally, we demonstrate that we can accurately decompile the analytic, internal representations of a full-rank reservoir computer that has been conventionally trained using data. Taken together, we define an implementation of neural computation that can both decompile computations from existing neural connectivity and compile distributed programs as new connections.

Optimization of a chemical additive program for a paper machine can require attention to both colloidal charges and kinetic effects. This work considered an additive program with two negatively charged substances (nanofibrillated cellulose [NFC] and colloidal silica) and two positively charged items (cationic starch and cationic acrylamide copolymer retention aid). Results were shown to depend on charge interactions; however, that clearly was not the whole story. Some findings related to cationic demand, dewatering, fine-particle retention, and flocculation among fibers were best explained in terms of at least partly irreversible complexation interactions between the charged entities. Adjustments in ratios between oppositely charged additives, their sequences of addition, and effects of hydrodynamic shear levels all affected the results. In general, the most promising results were obtained at a cationic starch level of 0.25% to 0.5% based on sheet solids in systems where the cationic starch was used as a pretreatment for NFC.

The aviation industry is a symbol of a country&apos;s comprehensive strength. Among the aviation parts, the impeller and blade are the core components of the engine. They are widely used in aerospace, ship navigation, mineral machinery, national defense and military industry. Their material is hard, the profile is complex and distorted, the blade is thin, the spacing is small, and the surface accuracy is streamlined [1], Aeroengine manufacturing technology has become the standard of a country&apos;s scientific and technological strength. Therefore, the research on the manufacturing and processing technology of impellers and blades has important research value for all types of aircraft, military equipment, automobiles, ships and civil equipment, and has important strategic significance for economy and national defense. Based on the analysis of the functions and characteristics of five axis machining and the application of the operating principle and structural characteristics of jet engine impeller, the machining process and program of five axis machining of impeller are compiled in this paper.

Artificial Intelligence (AI) is a branch of software engineering that is basically concerned with the creationof keen machines which fill in as humans. The blend of master and machine is needed to diminish thefunctioning load and understand more cases. AI has gotten vital for practically all zones including softwareengineering, security designing, criminal science, brain research, and advanced mechanics. AI, neuralorganization and profound learning are unified to one another and they all go under the roof of computerizedreasoning. AI is a piece of man-made brainpower which permits a machine or software program to gain fromauthentic exercises or patterns to foresee new yield esteems by utilizing explicit calculations. The stakesare unimaginably high; “shrewd” calculations remain to assume a basic part in choices to automaticallysubmit or sedate people, the arrangement of condemning suggestions, and surprisingly the direction offocused police surveillance. The values frameworks that guide these algorithms, for better or worse, will becontrolled by those that design them.

Numerical assessment of rectangular tunnels configurations using support vector machine (SVM) and gene expression programming (GEP)

Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description. Recent work mainly uses natural language processing techniques to process both query texts (i.e., human natural language) and code snippets (i.e., machine programming language), however, neglecting the deep structured features of query texts and source codes, both of which contain rich semantic information. In this article, we propose an end-to-end deep graph matching and searching (DGMS) model based on graph neural networks for the task of semantic code retrieval. To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet. In particular, DGMS not only captures more structural information for individual query texts or code snippets, but also learns the fine-grained similarity between them by cross-attention based semantic matching operations. We evaluate the proposed DGMS model on two public code retrieval datasets with two representative programming languages (i.e., Java and Python). Experiment results demonstrate that DGMS significantly outperforms state-of-the-art baseline models by a large margin on both datasets. Moreover, our extensive ablation studies systematically investigate and illustrate the impact of each part of DGMS.

An investigation is presented in this paper to predict Groundwater Level (GWL) by Inclusive Multiple Modelling (IMM) practices, introduced recently by the authors, as a strategy by organising multiple models at three hierarchical levels: Artificial Neural Networks (ANNs), Sugeno Fuzzy Logic (SFL), Neuro-Fuzzy (NF), Support Vector Machine (SVM) and Gene Expression Programming (GEP). The IMM novelty of the study is to investigate a modelling strategy at three hierarchical levels, such that any base models at Level 1 are not reused as the combiner model at Levels 2 and Level 3 and this leads to a number of strategies. The results provide some evidence that (i) combining base models at Levels 2 and 3 enhance the performances compared with those of individual base models at Level 1; and (ii) the results at Level 3 become defensible and thereby suitable for the development of management scenarios. The decline in GWLs is investigated through management scenarios, which show that water use has higher impacts on groundwater level variations in the study area than those by climatic variabilities and this underpins the evidence for the necessity of management plans and strategies for the study area.

Direct numerical control is also known as distributed numerical control (also abbreviated DNC), is a common manufacturing term for networking CNC machine tools. On some CNC machine controllers, the available memory is too small to contain the machining program (for example machining complex surfaces), so in this case the program is stored in a separate computer and sent directly to the machine, one block at a time. If the computer is connected to a number of machines it can distribute programs to different machines as required. Usually, the manufacturer of the control provides suitable DNC software. However, if this provision is not possible, some software companies provide DNC applications that fulfill the purpose. DNC networking or DNC communication is always required when Computer-Aided Manufacturing (CAM) programs are to run on some CNC machine control. Wireless DNC is also used in place of hard-wired versions. Controls of this type are very widely used in industries with significant sheet metal fabrication, such as the automotive, appliance, and aerospace industries. A DNC system has to fulfill two basic tasks such as first guaranteeing secure, timely data transfer from and to the CNC controllers, secondly administering many thousands of NC programs.

Digital Manufacturing for Smart Small Satellites Systems

With the rapid development of information, communication, network and computer, the field, depth and breadth of computer applications are also rapidly expanding. The traditional production methods, development techniques and development tools of software development are no longer suitable for the system software development with larger and larger scale and more complex structure. Facing the fast changing demand, how to develop software quickly and quickly has become an urgent problem. This paper takes the agile development methodology as the support point and the core idea, puts forward to adopt the agile development thought as the core, take IDE as the tool, take machine programming as the core technology, carries on the research to the agile thinking machine programming technology, carries on the development in Visual Studio, finally, realizes carries on the programming with the machine.

The quality of the workpiece produced by CNC machine tool is primarily due to the degree of precision and stability of the machines themselves. In general, the quality measures the degree of conformity of a part with predefined dimensions and geometric specifications. However, a simple measure does not identify the contribution of each source of error affecting the room. It is therefore important to analyze the technological aspects of machine tools, numerical control and the influence of the process. In order to give the issues of precision machine tools a conceptual dimension, it is preferable to classify the affecting factors. Errors can be classified according to the working phase during which the sources that generate them are active. During the preparation phase, errors can be associated with setting procedures, errors in the programming, and machining programs converted into machine language. During the machining phase, the sources of error which affect the accuracy are much more varied. Their effects can be considered as the combination of the individual contributions of all the elements constituting the system machine tool cutter, as well as the interaction of this system with the process. The purpose of this article is to present a study on the evaluation of the geometrical errors of a machine tool EMCO PC MILL 155 that exists within our laboratory, which are due to cut efforts.

A programming to make parts are CNC Programming (Computer Numerical Control Programming) for machines. An NC program consists of a sequence of instructions that control the motion and automatic sequences of an NC Machine to perform a particular processing task. In a general sense, the term NC programming refers to the creation of control data for machining workpieces on NC and CNC machines. NC programming has a decisive influence on the cost-effectiveness and profitability of NC manufacturing. The selection of a programming system is mostly guided by the need for software that is suitable for the application at hand, readily available, and as universally applicable as possible.