Manufacturing Environment Research Articles

Production routing problem in shared manufacturing: Robust chance-constrained formulation and multi-diversification based matheuristic

Building on the concept of the sharing economy, which capitalizes on the advancements in information and manufacturing technologies, the shared manufacturing paradigm has tremendous potential for enhancing the performance of production–distribution systems and has become prevalent in the today’s manufacturing industry. Despite the extensive research on the production routing problem, which integrates decisions of production, inventory, and distribution routing, there is a notable absence of investigation within the context of shared manufacturing. This paper aims to bridge the above-mentioned research gap and develop valuable decision-support tools for managers, assisting efficient management of production–distribution systems in shared manufacturing environments. To achieve this, this paper introduces a general model for the production routing problem with shared manufacturing resources. The problem is formulated as a robust chance-constrained model to address uncertainties arising in customer demand and the demand and supply of shared manufacturing resources. Subsequently, a computationally tractable formulation is derived, for which a matheuristic that incorporates three diversification techniques is proposed to solve. Extensive computational experiments are conducted to validate the feasibility and effectiveness of both the model and the matheuristic, as well as to evaluate the benefits of manufacturing resource sharing. The experimental results demonstrate that our matheuristic outperforms state-of-the-art solution approaches. Moreover, manufacturing resource sharing can lead to significant reductions in operational costs and improvements in service levels. Finally, the advantages are found to increase as manufacturing resource unit purchasing costs decrease, demand fluctuations decrease, and production interruption severity increases.

Smart and user-centric manufacturing information recommendation using multimodal learning to support human-robot collaboration in mixed reality environments

The future manufacturing system must be capable of supporting customized mass production while reducing cost and must be flexible enough to accommodate market demands. Additionally, workers must possess the knowledge and skills to adapt to the evolving manufacturing environment. Previous studies have been conducted to provide customized manufacturing information to the worker. However, most have not considered the worker's situation or region of interest (ROI), so they had difficulty providing information tailored to the worker. Thus, a manufacturing information recommendation system should utilize not only manufacturing data but also the worker's situational information and intent to assist the worker in adjusting to the evolving working environment. This study presents a smart and user-centric manufacturing information recommendation system that harnesses the vision and text dual encoder-based multimodal deep learning model to offer the most relevant information based on the worker's vision and query, which can support human-robot collaboration (HRC) in a mixed reality (MR) environment. The proposed recommendation model can assist the worker by analyzing the manufacturing environment image acquired from smart glasses, the worker's specific question, and the related manufacturing document. By establishing correlations between the MR-based visual information and the worker's query using the multimodal deep learning model, the proposed approach identifies the most suitable information to be recommended. Furthermore, the recommended information can be visualized through MR smart glasses to support HRC. For quantitative and qualitative evaluation, we compared the proposed model with existing vision-text dual models, and the results demonstrated that the proposed approach outperformed previous studies. Thus, the proposed approach has the potential to assist workers more effectively in MR-based manufacturing environments, enhancing their overall productivity and adaptability.

Machine identity authentication via unobservable fingerprinting signature: A functional data analysis approach for MQTT 5.0 protocol

The Industrial Internet of Things (IIoT) and Smart Manufacturing environments face significant challenges in machine identity authentication, crucial for ensuring data integrity and cybersecurity. Traditional methods like One-Class Support Vector Machines (OCSVM) often fall short in sensitivity, specificity, and explainability. This research introduces a novel Functional Data Analysis (FDA)-based model that leverages unique temporal patterns in machine-generated data to authenticate machine identities, adapting to equipment wear and tear. Through a comprehensive comparative analysis, the FDA model demonstrates superior performance and robustness in industrial settings. Additionally, we propose integrating this FDA-based approach with the MQTT 5.0 protocol, enhancing data security in IIoT communications. This work not only advances IIoT cybersecurity but also supports digital twins and predictive maintenance, contributing to a more secure and efficient Smart Manufacturing ecosystem.

A novel system for the classification of zinc-plated components by benchmarking deep neural networks

Artificial intelligence (AI) has become the new technique for solving the most complicated challenges related to big data, image recognition, object detection, and prediction issues due to the fourth industrial revolution. This article introduces the development and implementation of a zinc-plated component recognition system within a manufacturing process using deep learning (DL) techniques. This paper aims to train and evaluate different DL algorithms to recognize five zinc-plated components and one assembly tray under different ambient light conditions and finishing. The proposed method begins with creating a custom dataset of 5.5K images, with six classes that match the assembly components. However, before the training stage starts, a subset between 5% and 10% is created. Eight deep neural networks have been benchmarked: Swing Transformer, Convnextv2, Xception41, Data-efficient image Transformer, Inception_v4, ViT, Resnet50 and Efficientnet_b0. The training stage is performed with the k-fold method using k = 6 and a progressive data size between different runs with an average of data utilized during training of 8%. In this study, convnextv2_tiny exhibits the highest F1-Score with 99.177%, followed by swin_tiny_patch4_window7_224 with 99.175%, while resnet50 achieved the lowest F1-Score at 95.886%. Therefore, the new generation of convolutional architectures is still better for classifying images. Thus, these algorithms can be adopted as good classifier models for zinc-plated component classification in the industrial manufacturing environment.

Prospective modeling and estimating the epidemiologically informative match rate within large foodborne pathogen genomic databases

ObjectivesMuch has been written about the utility of genomic databases to public health. Within food safety these databases contain data from two types of isolates—those from patients (i.e., clinical) and those from non-clinical sources (e.g., a food manufacturing environment). A genetic match between isolates from these sources represents a signal of interest. We investigate the match rate within three large genomic databases (Listeria monocytogenes, Escherichia coli, and Salmonella) and the smaller Cronobacter database; the databases are part of the Pathogen Detection project at NCBI (National Center for Biotechnology Information).ResultsCurrently, the match rate of clinical isolates to non-clinical isolates is 33% for L. monocytogenes, 46% for Salmonella, and 7% for E. coli. These match rates are associated with several database features including the diversity of the organism, the database size, and the proportion of non-clinical BioSamples. Modeling match rate via logistic regression showed relatively good performance. Our prediction model illustrates the importance of populating databases with non-clinical isolates to better identify a match for clinical samples. Such information should help public health officials prioritize surveillance strategies and show the critical need to populate fledgling databases (e.g., Cronobacter sakazakii).

Design of multi-modal feedback channel of human–robot cognitive interface for teleoperation in manufacturing

AbstractTeleoperation, which is a specific mode of human–robot collaboration enabling a human operator to provide instructions and monitor the actions of the robot remotely, has proved beneficial for application to hazardous and unstructured manufacturing environments. Despite the design of a command channel from human operators to robots, most existing studies on teleoperation fail to focus on the design of the feedback channel from the robot to the human operator, which plays a crucial role in reducing the cognitive load, particularly in precise and concentrated manufacturing tasks. This paper focuses on designing a feedback channel for the cognitive interface between a human operator and a robot considering human cognition. Current studies on human–robot cognitive interfaces in robot teleoperation are extensively surveyed. Further, the modalities of human cognition that foster understanding and transparency during teleoperation are identified. In addition, the human–robot cognitive interface, which utilizes the proposed multi-modal feedback channel, is developed on a teleoperated robotic grasping system as a case study. Finally, a series of experiments based on different modal feedback channels are conducted to demonstrate the effectiveness of enhancing the performance of the teleoperated grasping of fragile products and reducing the cognitive load via the objective aspects of experimental results and the subjective aspects of operator feedback.

Diversification of Pharmaceutical Manufacturing Processes: Taking the Plunge into the Non-PGM Catalyst Pool.

Recent global events have led to the cost of platinum group metals (PGMs) reaching unprecedented heights. Many chemical companies are therefore starting to seriously consider and evaluate if and where they can substitute PGMs for non-PGMs in their catalytic processes. This review covers recent highly relevant applications of non-PGM catalysts in the modern pharmaceutical industry. By highlighting these selected successful examples of non-PGM-catalyzed processes from the literature, we hope to emphasize the enormous potential of non-PGM catalysis and inspire further development within this field to enable this technology to progress toward manufacturing processes. We also present some historical contexts and review the perceived advantages and challenges of implementing non-PGM catalysts in the pharmaceutical manufacturing environment.

Systems Engineering Application for Better Design and Analysis of an Assembly Process

AbstractThe use of systems engineering has proven effective in managing complexity and improving system design. Model based system engineering utilizing the Systems Modeling Language (SysML) especially helps in multidisciplinary environments where engineering data needs to be transformed and integrated between environments. Manufacturing is another discipline same as systems engineering is on active digitalization transformation. In the paper, we propose method and solution to apply Model based system engineering (MBSE) for improvement of process planning of assembly lines leveraging model based approach. SysML modeling and execution enable automation of analysis activities as trade‐off, where the behavior of various assembly scenarios of an assembly line can be captured using SysML behavioral diagrams and compared based on various evaluation criteria. However, relying solely on descriptive SysML system models without integrating the virtual representation of the assembly line is insufficient to verify all aspects of system behavior, such as ergonomics and collision avoidance. The main objective of this work is to present a concept for transforming SysML assembly scenarios into the process and resources models of the computer‐aided design and manufacturing (CAD/CAM) environment. This yields a holistic view that serves as a foundation for further production‐related simulations and analyses, enhancing efficiency, ergonomic design, factory layout, and material flow, ensuring effective assembly workstation design optimized at systems engineering level.

A Hybrid Approach for the Multi-Criteria-Based Optimization of Sequence-Dependent Setup-Based Flow Shop Scheduling

A key challenge in production management and operational research is the flow shop scheduling problem, characterized by its complexity in manufacturing processes. Traditional models often assume deterministic conditions, overlooking real-world uncertainties like fluctuating demand, variable processing times, and equipment failures, significantly impacting productivity and efficiency. The increasing demand for more adaptive and robust scheduling frameworks that can handle these uncertainties effectively drives the need for research in this area. Existing methods do not adequately capture modern manufacturing environments’ dynamic and unpredictable nature, resulting in inefficiencies and higher operational costs; they do not employ a fuzzy approach to benefit from human intuition. This study successfully demonstrates the application of Hexagonal Type-2 Fuzzy Sets (HT2FS) for the accurate modeling of the importance of jobs, thereby advancing fuzzy logic applications in scheduling problems. Additionally, it employs a novel Multi-Criteria Decision-Making (MCDM) approach employing Proportional Picture Fuzzy AHP (PPF-AHP) for group decision-making in a flow shop scheduling context. The research outlines the methodology involving three stages: group weight assessment through a PPF-AHP for the objectives, weight determination using HT2FS for the jobs, and optimization via Genetic Algorithm (GA), a method that gave us the optimal solution. This study contributes significantly to operational research and production scheduling by proposing a sophisticated, hybrid model that adeptly navigates the complexities of flow shop scheduling. The integration of HT2FS and MCDM techniques, particularly PPF-AHP, offers a novel approach that enhances decision-making accuracy and paves the way for future advancements in manufacturing optimization.

Partnership of Lean Six Sigma and Digital Twin under Type 2 Neutrosophic Mystery Toward Virtual Manufacturing Environment: Real Scenario Application

Partnership of Lean Six Sigma and Digital Twin under Type 2 Neutrosophic Mystery Toward Virtual Manufacturing Environment: Real Scenario Application

Metrics for Sustainability and Circular Economy Practices in Context to Modern Manufacturing Environment

Metrics for Sustainability and Circular Economy Practices in Context to Modern Manufacturing Environment

A review and classification of manufacturing ontologies

AbstractOne core concept of Industry 4.0 is establishing highly autonomous manufacturing environments. In the vision of Industry 4.0, the product leads its way autonomously through the shopfloor by communicating with the production assets. Therefore, a common vocabulary and an understanding of the domain’s structure are mandatory, so foundations in the form of knowledge bases that enable autonomous communication have to be present. Here, ontologies are applicable since they define all assets, their properties, and their interconnection of a specific domain in a standardized manner. Reusing and enlarging existing ontologies instead of building new ontologies facilitates cross-domain and cross-company communication. However, the demand for reusing or enlarging existing ontologies of the manufacturing domain is challenging as no comprehensive review of present manufacturing domain ontologies is available. In this contribution, we provide a holistic review of 65 manufacturing ontologies and their classification into different categories. Based on the results, we introduce a priority guideline and a framework to support engineers in finding and reusing existent ontologies of a specific subdomain in manufacturing. Furthermore, we present 16 supporting ontologies to be considered in the ontology development process and eight catalogs that contain ontologies and vocabulary services.

Bi-level optimization of shared manufacturing service composition based on improved NSGA-II.

To address the issue of insufficiently comprehensive representation of service composition indexes in the shared manufacturing environment, service reliability, confidence, and other indexes are decomposed in detail to establish a composition evaluation system, and a shared manufacturing service composition optimization model based on bi-level programming is proposed. The model takes Quality of Service (QoS) as the upper objective function and service reliability, service confidence and task fit as the lower objective function. The upper objective function needs to be minimized, while the lower objective function needs to be maximized. To achieve the optimal composition scheme of shared manufacturing services, the Criteria Importance Though Intercrieria Correlation (CRITIC) is used to determine the weights of the indicators, and the improved Fast Elitist Non-Dominated Sorting Genetic Algorithm (Improved NSGA-II) is employed to solve the multi-objective optimization problem. Finally, the improved NSGA-II has a 23.33% increase in convergence speed and a 69.99% gain in operational efficiency when compared to the traditional NSGA-II. The viability and effectiveness of the improved NSGA-II have been demonstrated.

Empowering Manufacturing Environments with Process Mining-Based Statistical Process Control

The production of high-quality products and efficient manufacturing processes in modern environments, where processes vary widely, is one of the most crucial issues today. Statistical process control (SPC) and process mining (PM) effectively trace and enhance the manufacturing processes. In this direction, this paper proposes an innovative approach involving SPC and PM strategies to empower the manufacturing environment. SPC monitors key performance indicators (KPIs) and identifies out-of-control processes that deviate from specification limits, while PM discovery techniques are applied for those abnormal processes to extract the actual process flow from event logs and model it using Petri nets. Different enhancement techniques in PM, such as decision rules and root cause analysis, are then used to return the process to control and prevent future deviations. The application of the integrated SPC–PM approach is shown through case studies of production processes. SPC charts found that over 6% of processes exceeded specification limits. At the same time, PM methodologies revealed that prolonged times for the ‘Quality Control’ activity is the fundamental factor increasing the cycle time. Moreover, decision tree analysis provides rules for decreasing the cycle times of unbalanced processes. The absence of a transition from the ‘Return from Waiting’ activity to ‘Packing and Shipment’ is a critical factor in decreasing cycle times, as is the shift information. Our newly proposed methodology, which combines process analysis from PM with statistical monitoring from SPC, ensures operational excellence and consistent quality in manufacturing. This study illustrates the application of the proposed methodology through a case study in production processes, highlighting its effectiveness in identifying and addressing process deviations.

Gauging the Technology Acceptance of Manufacturing Employees: A New Measure for Pre-Implementation

Recent technological advances are bringing about the digitalization of manufacturing, enabled by introducing and integrating new and improved technologies into existing processes and activities. Integrating advanced technologies into the workplace can have a positive effect on manufacturing efficiency and competitiveness, as well as sustainability and environmental impact. Employee acceptance of these new technologies is critical for manufacturing organizations to achieve these goals. Unfortunately, a notable deficiency of tools to assess the readiness of an employee work group or organization to accept a new technology exists. The objective of this study was to develop and validate a new tool for gauging employee technology acceptance in a pre-implementation decision context known as the Technology Acceptance in a Manufacturing Environment (TAME). Statistical validation measures were conducted on survey responses from 823 respondents across seven locations of one large organization. The results indicate that TAME is appropriate for assessing readiness for technology acceptance among manufacturing workers with little to no training or knowledge of the technology being considered for implementation (R2 = 86%). TAME can facilitate the organizational assessment of employee perception of new technologies before implementation, increasing the chances of a successful launch. This research results in the first known application of technology acceptance models in a pre-implementation context in a manufacturing environment.

ადამიანური კაპიტალის მართვა ისტორიული თეორიის ჭრილში

The historical role of human resource management and its importance in effective organizational performance and continuous development is discussed. To understand the historical role of the human resource management, the article reviews the corresponding literature and analyzes management approach based on human capital theory in relation to effective performance of contemporary organizations. At an early stage of scientific research in economic development a human’s role was evaluated as a special resource (human resource) and as a subject of social development. At a later stage a human was considered as a subject of economic development. This gave origin to terms like an “individual” and a “person”. In this case a human is considered as a subject of social and labor relations. Currently, key elements of the social-economic reform, such as stabilization of social-economic and political environment, invention and establishment of new manufacturing and management technologies, rationalization of the finance and banking systems and improvement of social welfare and quality of lifestyle are still critical challenges facing our country. These challenges can only be addressed by implementation of consistent policy in social and labor relations. Establishment of social market economy in the country and its well-organized functioning is possible with integration into global production. This primarily refers to adaptation of domestic labor relations with the internationally recognized social and labor relation system. This goal can be achieved by continuous organizational development efforts. For this reason, it is essential for each organization to develop and periodically renew its human resource management strategy and action plan that will ensure alignment of its personnel conduct and behavior and entire progress with its organizational needs. For organizational development it is essential to develop and periodically renew employee performance evaluation, motivation, personnel development policy and procedures. Staff motivation policy shall define both monetary and non-monetary incentives and be targeted at maintaining best human capital crucial for the long-term success of any organization. Personnel development policy should reflect the correlation between the results of performance evaluation and personnel development plan. The policy should prescribe rules for developing an individual development plan and setting career development criteria. In addition, a personnel career development policy and procedures should prescribe organizational training needs identification methodology, priority trainings, training opportunities and respective actions. Knowledge gained at the trainings should be expanded throughout the organization. Concentrating on above processes will turn an organization into an attractive employer with desirable working environment and organizational culture.

Breaking Down the Barriers between the Digital and the Real: Mixed Reality Applied to Battery Manufacturing R&D and Training

AbstractIn a scenario in which the manufacturing of high‐performance, safe batteries on an unprecedented large scale is crucial for the energy transition and fight against climate change, research laboratories and cell production industries are facing challenges due to the lack of efficient data management and training tools. In this context, the use of intelligent devices plays an important role on the path towards the optimization of the manufacturing process and the enhancement of the battery performance while reducing production costs. In this Concept, we present an innovative Mixed Reality tool for efficient data collection and training in real‐time in battery research laboratories and battery manufacturing pilot lines, which runs on Microsoft HoloLens 2 glasses. We report a deep analysis on its ergonomic and usability aspects, and describe how we solved the problems found during its development. Thanks to this tool, users can collect data while keeping their hands free and receive advice in real time to design and build batteries with tailored properties. This optimizes data management in battery manufacturing environments. Now, thanks to our Mixed Reality application, users can collect data in the place of work, save this data automatically on a server and exploit it to receive advice and feedback to support their decision‐making and learning of the manufacturing process.

Accurate measurement method for parallel edge spacing of sheet metal parts in aviation manufacturing

A new method, to our knowledge, is proposed to achieve high-precision measurement of parallel edge spacing for sheet metal parts in the complex industrial environment of aviation manufacturing. First, the sub-pixel edges of sheet metal parts are extracted by combining a what we believe to be a novel adaptive rolling bilateral filter and a sub-pixel edge detection algorithm based on the Canny–Steger algorithm. Then, the acquired edge data are denoised by using the clustering algorithm. Finally, a parallel line fitting algorithm, which combines an improved K-medoids algorithm with composite constraints on points and slopes, is proposed to calculate the parallel edge spacing. The results show that the method is robust to introducing noise in the edge data due to uneven illumination and various types of defects such as wear, scratches, and stains. The detection accuracy is high, with an average detection error of only 0.015 mm.

Optimisation of process plan generation for reconfigurable manufacturing systems: efficient heuristics and lower bounds

ABSTRACT We address the process plan generation problem in a reconfigurable manufacturing environment. The objective is to minimise the total production time of a given part which includes the processing times of operations, as well as the changeover times for machines, configurations, and tools. A new efficient 0–1 mathematical programming formulation is proposed, together with a heuristic and two lower bounds. The formulation is based on one main decision variable (1MDV). It outperforms a well known two-main decision variable formulation (2MDV) from the literature. Using a commercial solver, the 1MDV formulation is on average almost five times faster than the 2MDV formulation across all instances where optimality was achieved. The proposed heuristic is a decomposition mathematical programming-based heuristic. For small benchmark instances solved to optimality using 1MDV formulation, the heuristic obtains solutions at less than 1% distance on average from optimum in few seconds. The best of the two proposed lower bounds, LB2, is obtained in less than 0.56 seconds, across all tested instances, while the 1MDV model needs about 30 seconds on average to reach the same result. These lower bounds are used to measure the performance of the heuristics on large size instances

PUBLIC-PRIVATE PARTNERSHIP MODELS IN THE PROFESSIONAL TRAINING OF SPECIALISTS FOR THE CONTEMPORARY LABOR MARKET NEEDS

The relevance of the article is determined by the need to substantiate the characteristics of the application of public-private partnership models in the vocational training of specialists in institutions of vocational (vocational-technical) education, taking into account the contemporary needs of the labor market, capable of working effectively in high-tech manufacturing environments. Objective: To identify and substantiate the characteristics of the application of public-private partnership models in the vocational training of specialists for the contemporary needs of the labor market. Methods: Analysis of normative and scientific literature regarding the development of public-private partnerships in the field of vocational (vocational-technical) education and the summarization of its results – to identify the state of the problem's development and to clarify the characteristics of the application of public-private partnership models in the vocational training of specialists for the contemporary needs of the labor market. Results: The characteristics of public-private partnership models in conducting the vocational training of specialists for the contemporary needs of the labor market have been determined and substantiated. The participation of private companies in co-financing programs for the vocational training of specialists, the modernization of the material and technical base of vocational (vocational-technical) education institutions, and the creation of conditions for internships for students and teaching staff of vocational (vocational-technical) education institutions at high-tech workplaces have been analyzed. Conclusions: It has been found that the effectiveness of public-private partnerships in the vocational training of specialists is ensured through the implementation of models: joint financing of vocational (vocational-technical) education programs; internships for students of vocational (vocational-technical) education at partner enterprises; organization of dual form of vocational education; training of specialists on the order of enterprises. Among the characteristics of the application of public-private partnership models are: ensuring the quality of vocational training of specialists, reducing expenses from the state budget on the implementation of vocational (vocational-technical) education programs, increasing the level of employment in the profession of graduates, and strengthening the links between vocational (vocational-technical) education and private business. Recommendations have been formulated for activating public-private partnerships in the vocational training of specialists through organizing informational and educational work, improving the regulatory framework, ensuring funding for educational projects, coordinating efforts on the part of all interested partners, and enhancing transparency and accountability of their activities, in order to minimize the risks of corruption in the implementation of public-private partnership projects.