Modern Manufacturing Environments Research Articles

Quality 4.0: Learning quality control, the evolution of SQC/SPC

This article marks a significant advancement in the field of quality management, specifically focusing on the evolution from traditional Statistical Quality Control (SQC) and Statistical Process Control (SPC) methods to a more advanced Learning Quality Control (LQC) approach. The research introduces Quality 4.0 (Q4.0) as a novel paradigm that fuses the technologies of the fourth industrial revolution, Manufacturing Big Data (MBD), Industrial Internet of Things (IIoT), Cloud Storage and Computing (CSC) and Artificial Intelligence (AI), with traditional quality management practices. The central theme of this study is exploring the limitations inherent in conventional quality control methods when faced with the complexities of modern manufacturing environments. The authors propose LQC systems as a solution, employing binary classification algorithms to predict and detect defects in manufacturing processes. This represents a shift from reactive to proactive quality measures enabled by AI’s real-time data processing capabilities. The document delves into the evolution of manufacturing data across industrial revolutions, highlighting the exponential growth of unstructured data and its challenges. Through case studies, the authors illustrate the practical applications of LQC systems, demonstrating their ability to learn complex patterns in hyperdimensional spaces and automate tasks traditionally performed visually.

Health Monitoring System for CNC Machine

This paper introduces a novel Health Monitoring System (HMS) tailored for CNC machines, addressing the critical need for maintaining their optimal performance and preventing unexpected breakdowns in modern manufacturing settings. The system integrates various sensors and data acquisition methods to continuously monitor key parameters like temperature, vibration, and tool wear. By employing advanced data analytics and machine learning algorithms, the HMS can swiftly identify anomalies in real-time, facilitating proactive maintenance and minimizing operational downtime. Additionally, the system features a user- friendly interface for visualizing machine health status and creating predictive maintenance schedules. Experimental validation conducted on a CNC machining center validates the efficacy and reliability of the developed HMS in enhancing machine efficiency, prolonging equipment lifespan, and curbing maintenance expenses. In summary, this Health Monitoring System offers a robust solution for ensuring the seamless operation and longevity of CNC machines in modern manufacturing environments.

THE ROLE OF PRECISION METROLOGY IN ENHANCING MANUFACTURING QUALITY: A COMPREHENSIVE REVIEW

Precision metrology plays a pivotal role in modern manufacturing processes by ensuring the attainment of high-quality standards and the optimization of production efficiency. This comprehensive review delves into the significance of precision metrology in enhancing manufacturing quality across diverse industries. The review encompasses an examination of advanced metrological techniques, their applications, challenges, and future prospects. The review begins by elucidating the fundamental principles of precision metrology, emphasizing its critical role in quantifying dimensional accuracy, surface finish, and other crucial parameters in manufacturing. It explores the evolution of metrology from conventional methods to state-of-the-art technologies such as coordinate measuring machines (CMMs), optical metrology, and 3D scanning systems. The integration of these techniques into modern manufacturing environments has revolutionized quality control processes, enabling real-time monitoring and rapid feedback mechanisms. Furthermore, the review highlights the multifaceted applications of precision metrology across various industries, including automotive, aerospace, electronics, and medical devices. It elucidates how metrological advancements have facilitated the production of intricate components with tight tolerances, thereby enhancing product performance, reliability, and safety. Additionally, the review examines the role of metrology in ensuring compliance with regulatory standards and certifications, crucial for industries with stringent quality requirements. Despite its numerous benefits, precision metrology also poses challenges, such as measurement uncertainties, calibration complexities, and data interpretation issues. The review discusses these challenges in detail and explores emerging strategies and technologies aimed at mitigating them. Furthermore, it addresses the need for standardized metrological practices and international collaboration to foster interoperability and harmonization in global manufacturing ecosystems. Looking ahead, the review outlines future directions and opportunities in precision metrology, including advancements in artificial intelligence, machine learning, and Internet of Things (IoT) integration for predictive maintenance and autonomous quality control systems. It underscores the importance of continued research and innovation in metrology to address evolving manufacturing demands and ensure sustained improvements in product quality and competitiveness. In conclusion, this comprehensive review underscores the indispensable role of precision metrology in enhancing manufacturing quality, driving innovation, and shaping the future of modern industry.
 Keywords: Precision, Metrology, Manufacturing, Quality, Review.

Evaluating the Efficiency of Activity-Based Costing in Modern Manufacturing Enterprises

This research paper investigates the efficacy of Activity-Based Costing (ABC) in modern manufacturing enterprises, aiming to enhance cost accuracy and decision-making processes. Through a mixed-methods approach, including literature review and case study analysis, the study evaluates the implementation of ABC in manufacturing enterprises and compares its results with traditional costing methods. Findings reveal that ABC provides more accurate cost allocations and overhead rates, leading to improved profitability measures for products. The analysis underscores the significance of ABC in enabling better resource allocation, pricing strategies, and product mix optimization, thereby enhancing competitiveness and profitability in modern manufacturing environments. However, challenges such as implementation complexity are acknowledged, suggesting the need for investment in training and resources to maximize the benefits of ABC. The implications of the study highlight the importance of adopting ABC for sustainable growth and performance excellence in manufacturing enterprises. Future research directions include exploring advanced technologies to enhance ABC's effectiveness, longitudinal studies to assess long-term impact, and comparative analyses across industries and regions to guide cost management strategies for future challenges.

Virtual Engineering and Commissioning to Support the Lifecycle of a Manufacturing Assembly System

Prior to the physical build of the industrial automation system, some challenges arise, such as processes’ cycle times calculations, ergonomics and safety evaluation, and the integration of separate machines to the complete production shops. This, in turn, requires reconfiguring the processes and component parameters. As a result, the lifecycle of the system development is prolonged, and the potential for erroneous performance increases. In modern digital manufacturing environments, virtual engineering (VE) and virtual commissioning (VC) serve as effective tools to tackle the aforementioned problems and their consequences. The virtual models developed for VE and VC not only assist system developers in the physical build stage but also in the following stages of the system lifecycle by providing a common virtual model, a digital twin (DT), of the manufacturing processes and the product. This developed model should possess the ability to simulate the system behaviour, e.g., the mechanics, kinematics, speed and acceleration profiles. Three stakeholders are involved in the development process: the machine builder, system integrator and end user. The current work focuses on the virtual engineering approach to support the entire lifecycle of a manufacturing system from the machine builder, system integrator and end user perspectives. For this purpose, it puts forward a systematic methodology of implementing VC and VE using a toolset developed by the Automation Systems Group at the University of Warwick within an industrial project. The suggested methodology is illustrated in a case study where a digital twin of a physical station was modelled, developed and tested in parallel with the physical machine development and build. Finally, the benefits and limitations are highlighted based on the gained outcomes and the implemented activities.

New weighted adaptive CUSUM charts for monitoring the generalized variance of a bivariate normal process

In modern and high volume manufacturing environments, it is vital to early detect a small sustained shift in a short period of time as it may have a serious impact on a manufacturing process. Adaptive memory-type control charts have the ability to swiftly detect a range of the shift sizes, and one such control chart is a weighted adaptive CUSUM (abbreviated as C) chart. In this paper, using a simple likelihood ratio test-statistic, we propose two types of the C charts (with and without the normalizing transformation) using three different shift estimators for monitoring the generalized variance (GV) of a bivariate normally distributed production process. In addition, the sensitivities of these control charts are also enhanced with the variable sampling interval feature. Monte Carlo simulations are used to compute the zero-state and steady-state run-length characteristics of the proposed charts. Based on detailed run-length comparisons, it is observed that the proposed charts may uniformly and substantially outperform the existing charts when detecting different kinds of shifts in the GV. A real dataset is also selected to illustrate the implementation of the newly proposed charts.

Process Monitoring: Distinguishing Defect Shapes by Strain Field Signatures

In this research, a theoretic physics-based framework for identification of defects via analysis of strain fields is presented. This framework comprises identification of self-similarity of strain fields followed by their dimensionality reduction using kernel based principal component analysis. The efficacy of this framework is tested qualitatively, by visual analysis, and quantitatively, using numerical classification algorithms. We see high (>95%) accuracy of classification via cross-validation studies using support vector machine algorithm. These results suggest that strain field can provide a viable approach for constructing highly robust in line defect detection system in modern manufacturing environments.

Integrated Quality, Maintenance and Production model for multivariate processes: A Bayesian Approach

In the era of modern manufacturing, automated inspection systems can facilitate online product quality assessment, generating data that can be used not only in a process control context but for equipment maintenance purposes, as well. The challenge is to develop appropriate tools to efficiently exploit the collected data by transforming them into optimal decisions that add value to the monitoring process. To this end, this paper proposes a Bayesian model that jointly optimises the interrelated process aspects of production, maintenance, and quality in modern manufacturing environments. The process output is characterised by multiple correlated quality characteristics and subject to multiple quality shifts and failures. Optimal real-time decisions are made based on a cost optimisation criterion. Finally, a numerical investigation is conducted to evaluate the performance of the proposed data-driven and widely applicable tool for contemporary processes.

Intelligent Small Object Detection for Digital Twin in Smart Manufacturing With Industrial Cyber-Physical Systems

Recently, along with several technological advancements in cyber-physical systems, the revolution of Industry 4.0 has brought in an emerging concept named digital twin (DT), which shows its potential to break the barrier between the physical and cyber space in smart manufacturing. However, it is still difficult to analyze and estimate the real-time structural and environmental parameters in terms of their dynamic changes in digital twinning, especially when facing detection tasks of multiple small objects from a large-scale scene with complex contexts in modern manufacturing environments. In this article, we focus on a small object detection model for DT, aiming to realize the dynamic synchronization between a physical manufacturing system and its virtual representation. Three significant elements, including equipment, product, and operator, are considered as the basic environmental parameters to represent and estimate the dynamic characteristics and real-time changes in building a generic DT system of smart manufacturing workshop. A hybrid deep neural network model, based on the integration of MobileNetv2, YOLOv4, and Openpose, is constructed to identify the real-time status from physical manufacturing environment to virtual space. A learning algorithm is then developed to realize the efficient multitype small object detection based on the feature integration and fusion from both shallow and deep layers, in order to facilitate the modeling, monitoring, and optimizing of the whole manufacturing process in the DT system. Experiments and evaluations conducted in three different use cases demonstrate the effectiveness and usefulness of our proposed method, which can achieve a higher detection accuracy for DT in smart manufacturing.

Job Shop Sequencing in Manufacturing Plants by Timed Coloured Petri Nets and Particle Swarm Optimization

The increasing diversity of products that can be machined in a plant poses scheduling challenges in modern manufacturing environments. The problem, which is known in literature as Flexible Job Shop Scheduling, is NP-Hard in general and can be efficiently solved by meta-heuristic approaches. This paper models the production plant in a Timed Coloured Petri Nets (TCPN) framework to describe production systems including flexible Computerized Numerical Control machines. Then, an algorithm is introduced to simulate the TCPN and compute the system throughput. Finally, the throughput is maximized by optimizing the job type sequencing and the amount of units of each job type that enters the system by implementing a Particle Swarm Optimization algorithm. The proposed simulation and optimization approaches are applied to a real manufacturing system producing ophthalmic lenses in order to show the effectiveness and the benefits of the proposed method.

كلية الإدارة والاقتصاد/ جامعة كربلاء/ العراق

The research aims to clarify the concept of achievement accounting, the requirements for its application and its mechanism of action, and to assess the extent of its activation of cost systems by providing information to improve cost systems and evaluate performance and to identify the reality of the existing cost system in the company (General Dairy Products). The research concluded that the traditional cost systems do not show the real result of the activity, while the achievement accounting shows the reality of the losses incurred by the company during the period. (Abu Ghraib Factory) by applying and using various modern manufacturing systems in production, as well as keeping pace with the rapid technological developments witnessed by the modern manufacturing environment to face the intense competition that characterizes the markets by providing products that meet the needs of customers quickly, at the lowest cost and with high quality.

Adaptive multivariate EWMA charts for monitoring sparse mean shifts based on parameter optimization design

High-dimensional data is becoming increasingly important in modern manufacturing environments, while brings difficulties for process monitoring at the same time, especially for the detection of sparse mean shifts. Several control schemes like VS-MEWMA and LEWMA have been proposed on basis of variable selection techniques recently. However, these schemes with constant smoothing parameters may perform poorly when the actual magnitude of mean shifts is significantly different from the assumed one. In this paper, to solve this problem, we focus on obtaining the optimal smoothing parameter of a specific shift range. It is proposed to minimize the expectation weighted run length (EWRL) by assigning a probability distribution to the shift magnitude. Therefore two improved MEWMA control charts that adaptively obtain the optimal parameters are proposed, leading to adaptive VS-MEWMA (AVS-MEWMA) and adaptive LEWMA (ALEWMA) schemes for succeeding in monitoring sparse mean shifts. The superiorities of the proposed scheme are illustrated by a real data example.

Research on Workers Integration in Smart Factories With Multi-Agent Control System

In today’s modern manufacturing environments, Multi-Agent Manufacturing System (MAMS) has been a fundamental approach for developing industrial applications that can cope with complexity, uncertainty, and dynamicity. It has become a significant resource for improving smart factories. However, little attention has been paid to integrate human workers into these overall control systems efficiently. This article is intended to take a step forward and propose a human worker integration scheme under MAMS. The multi-skilled feature of workers and the collaboration between workers are considered in this research. First, a Worker Agent (WoA) model with wearable devices is proposed as a critical design building block. After that, based on the conventional hybrid manufacturing control architecture (HyMCA), a WoA integration interface is investigated to implement the MAMS model. Then, a working mechanism of WoA in the task allocation process is studied. After that, a matching algorithm that considers different scenarios between workers and tasks is proposed based on the Hungarian Algorithm and Genetic Algorithm. Finally, program simulations and actual experiments are carried out to verify the effectiveness of the worker integration mechanism proposed in this paper.

A neural integrator model for planning and value-based decision making of a robotics assistant

Modern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human–robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that apply brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over a longer period reveals that the robot achieves a high search efficiency in stationary as well as dynamic environments.

The role of technical information in scheduling problems with sequence dependent setup times

Scheduling with setup times is one of the key points to jointly exploit the combination of mathematical models and Industry 4.0 principles in today’s modern manufacturing environments. The setup process is not a value added factor, and hence, setup times need to be explicitly considered. In this paper, the authors show the importance of technical data and details in the computing of setup times matrices. A general knowledge representation model is proposed in order to automatically derive the setup matrices from the BOM and materials-related data structures. The production planning solved is |Sij|Cmax. The production problem was solved under the hypothesis (A) of class of setup times estimated on the basis of historical data or (B) of setup matrices calculated taking into consideration technical data. An extensive experimental study was conducted in order to measure the difference between case (A) and case(B). The authors numerically show the great inefficiencies in the operating plan that a wrong hypothesis may lead to. An Italian specialized firm, Fiorini Industrial Packaging, is the reference industrial case for the paper.

Process Parameter Optimization for Minimizing Vibrations and Surface Roughness During Turning EN19 Steel Using Coated Carbide Tool

In modern manufacturing environments, the quality assurance of machined parts has attracted great attention from manufacturers. Surface finish and vibrations during turning play an important role during this process. Comparatively less work is done in the field of process parameter optimization. EN19 is a hard steel which finds its application in shafts, gears, spindles, oil and gas industries. Due to the hardness of the material, 223-277 BHN, turning this material is a difficult task. Thus, optimization of various cutting parameters is required in this situation. In the current work, an attempt has been made to optimize machining parameters when turning EN19 with coated carbide inserts (Titanium Nitride coated CCMT060204-HM YBC251 inserts) on conventional lathe machine. During this process, three factors, namely, MQL Condition (2 levels), Depth of cut (3 levels) and Spindle Speed (3 levels) are considered. Mixed level optimization is performed using Taguchi analysis with L18 orthogonal array. The considered factors/parameters are optimized for Surface Roughness and Vibrations with the objective functions being to minimize the same

THE IMPACT OF THE USE OF TARGET COSTING IN LIGHT OF MODERN MANUFACTURING ENVIRONMENT ON PERFORMANCE EVALUATION STANDARDS (A FIELD STUDY ON THE CEMENT SECTOR)

The study seeks to identify the extent to which employees are aware of the target cost method and its application as a modern cost management tool in the modern manufacturing environment in the cement sector, The extent to which performance appraisal metrics are used and used, Changes in the manufacturing environment and their impact on performance measures, And test the relationship between the use of the target cost method and performance appraisal metrics, and testing the relationship between changes in the modern manufacturing environment and performance appraisal metrics, The questionnaire was distributed to a sample of (57). senior, middle and technical officials at Kuwait Cement Factory in Kuwait. The results showed that there is a significant effect on the use of target costs on performance evaluation measures in the modern manufacturing environment. , In terms of the components and dimensions of each, which leads us to conclude that the target cost and modern manufacturing environment have an impact on the evaluation criteria. The study recommended: - Follow scientific methods and programs in cost management, especially methods and programs of operations research, which determine the optimal cost of production under the constraints of available resources. - The need to raise the efficiency of workers in cost accounting through the holding of training courses to introduce modern methods of cost management. - The need to integrate financial and non-financial performance indicators with a strategic dimension in performance evaluation. - The duty to develop measures of financial performance to keep pace with the rapid changes in the modern business environment.

Opportunities and Challenges for Statisticians in Advanced Pharmaceutical Manufacturing

Advanced pharmaceutical manufacturing technologies have the potential to make substantial contributions toward reducing waste, cost, and cycle time. However, the steady introduction of advanced manufacturing systems such as continuous manufacturing processes, automated microbioreactors, robotics, etc. create both challenges and opportunities for nonclinical statisticians who support chemistry and manufacturing control (CMC) activities in the pharmaceutical industry. Not only will closer collaboration be essential for optimal utilization of statistical tools, but CMC statisticians may need to acquire new engineering statistical skills as well as some knowledge of how to deal with “big data” issues. It is also expected that the confluence of advanced manufacturing technologies and statistical methods will require input from nonclinical statisticians on some regulatory issues. The purpose of this article is not only to alert CMC statisticians to these opportunities and challenges, but to provide a call-to-action for the industry and academia to better prepare statisticians for careers in modern manufacturing environments.

Perceived stress in human-machine interaction in modern manufacturing environments-Results of a qualitative interview study.

Rapid advances in digitization technologies are changing modern working conditions especially in industrial settings. Consequently, employees are confronted with new forms of human-machine interaction. Whether changes in working conditions in general, and the increasing relevance of human-machine interaction in particular, affect psychosocial working conditions, and employee's health is currently matter of debate, but empirical data are lacking. Therefore, we conducted semistructured interviews with 36 employees working in five different companies. The interviews were aimed at identifying potential stressors associated with the introduction and use of modern technologies in the manufacturing industry. The results show that stressors linked to human-machine interaction are technical problems, poor usability, low situation awareness, and increased requirements on employees' qualification. For example, technical problems such as breakdowns or slowdowns were described as a main stressor when employees were not qualified to handle these problems on their own, thus decelerating work flows and causing additional time pressure. Overall, the results show that problems in human-machine interaction, which have been observed in laboratory and nonindustrial settings, also apply to industrial work places with highly automated working conditions and are a potential source of stress. These factors should be considered in psychosocial risk assessment of work-related stressors.

Linked Data Architecture for Assistance and Traceability in Smart Manufacturing

Traceability systems and digital assistance solutions are becoming increasingly vital parts of modern manufacturing environments. They help tracking quality-related information throughout the production process and support workers and maintenance personnel to cope with the increasing complexity of manufacturing technologies. In order to support these use cases, the integration of information from different data sources is required to create the necessary insights into processes, equipment and production quality. Common challenges for such integration scenarios are the various data formats, encodings and software interfaces that are involved in the acquisition, transmission, management and retrieval of relevant product and process data. This paper proposes a Linked Data based system architecture for modular and decoupled assistance software. Its web-oriented approach allows to connect two usually disparate data sets: semantic descriptions of complex production systems on the one hand and high-volume and high-velocity production data on the other hand. The proposed concept is illustrated with a typical example from the manufacturing domain. The described End-of-Line quality assessment on forming machines is used for traceability and product monitoring.