Manufacturing Environment Research Articles

Parallel batch processing machines scheduling for SLM additive manufacturing in cloud manufacturing

For the scheduling problem of parallel batch processing machines for Selective Laser Melting (SLM) additive manufacturing in cloud manufacturing environments, an Improved Discrete Artificial Bee Colony algorithm(IABC) is proposed by considering the service time of the product as well as introducing the mathematical model of the problem. Firstly, a population initialization strategy is introduced to improve the quality of initial solutions and accelerate the convergence of the population.Secondly, through three neighborhood search operators, the transition from the original nectar source to the new nectar source is achieved for employed bees and following bees. Furthermore, a local search strategy and a modified abandonment pattern are proposed to increase population diversity and prevent the algorithm from falling into local optima.Finally, the performance of the algorithm is analyzed, and the IABC algorithm was applied to 36 examples. The effectiveness and advantages of IABC in solving the studied problem are verified through a large number of simulation experiments.

Susceptibility of Salmonella enterica Typhimurium dry surface biofilms to disinfection

AbstractIn food preparation and manufacturing environments, surfaces contaminated with Salmonella can lead to outbreaks of Salmonellosis. We hypothesise that Salmonella resides on dry surfaces in a biofilm form leading to potential environmental persistence and transfer following contact. This is the first study reporting that Salmonella Typhimurium can form dry surface biofilm (DSB). Six disinfectants commonly used in the food industry were evaluated for their efficacy against the DSB. The two most efficacious formulations reduced bacterial viability in DSB by >99.99% when combined with mechanical removal (5 sec wiping; 300 g weight). Five out of six formulations significantly reduced bacterial transfer when combined with wiping. Complete eradication of Salmonella Typhimurium DSB was challenging, and mechanical removal was essential to produce a >99.99% reduction in bacterial viability within DSB. This study highlights a potential mode of survival of Salmonella Typhimurium on food‐contact surfaces and DSB challenges for disinfection.

Yarn Angle Detection of Glass Fiber Plain Weave Fabric Based on Machine Vision

To address the issue of low accuracy in the yarn angle detection of glass fiber plain weave fabrics, which significantly impacts the quality and performance of the final products, a machine vision-based method for the yarn angle detection of glass fiber fabrics is proposed. The method involves pre-processing the image with brightness calculation, threshold segmentation, and skeleton extraction to identify the feature region. Line segment detection is then performed on this region, using the Hough transform. The concept of a “line segment evaluation index” is introduced, and it was used as a criterion for assessing the quality and relevance of detected line segments. Moreover, the warp and weft yarn extrusion area contours refer to the reconstructed outlines of yarn areas, achieved by combining the center of mass extraction with morphological operations and used to accurately determine the yarn angle. Tested under a range of challenging scenarios, including varied lighting conditions, fabric densities, and levels of image noise, this method has demonstrated robust stability and maintained high accuracy. These tests mimic real-world manufacturing environments, where factors such as ambient light changes and material inconsistencies can affect the quality of image capture and analysis. The proposed method has high accuracy, as shown by MSE and a Pearson’s r of 0.931. By successfully navigating these complexities, the proposed machine vision-based approach offers a significant enhancement in the precision of yarn angle detection for glass fiber fabric manufacturing, thus ensuring improved quality and performance of the final products.

A self-adaptive Gaussian mutation-based arithmetic optimiser algorithm for integrated production scheduling and vehicle routing problem in the distributed manufacturing environment

ABSTRACT In response to the challenges posed by economic globalisation and increasing customer demands, enterprises are compelled to adapt and refine their production models and operational objectives, which motivates this paper to address the integrated production scheduling and vehicle routing problem in the distributed manufacturing environment (IPSVRP-DME). The objective is to simultaneously minimise both total energy consumption and total earliness/tardiness. Initially, a mixed integer programming model is proposed to address small-scale problems using Gurobi. Considering the NP-hardness of the problem, a novel Self-adapted Gaussian Mutation-based Arithmetic Optimiser Algorithm (SGMAOA) is developed to handle medium-scale and large-scale instances. To address the complexity of decision-making, an innovative two-level encoding method that encompasses four decision dimensions is introduced. Additionally, an incremental repair strategy is devised to rectify infeasible solutions caused by unreasonable delivery batching, while a time relaxation strategy is proposed to further enhance service levels without compromising energy consumption. Comparative experiments are conducted to demonstrate the effectiveness of SGMAOA, which is benchmarked against five prominent metaheuristics. In addition, a specific example is applied for the discussion of managerial applications and to illustrate the practicality of the proposed model and solution method.

A hybrid design for jointly optimizing production scheduling, control chart design and maintenance in continuous flow manufacturing

Integrating the three crucial functions of quality control, economic production quantity (EPQ) and maintenance leads to cost savings and increased efficiency for manufacturing processes. While in the literature, they have typically been studied separately. Moreover, most of the existing integration models are designed for the discrete piece-part manufacturing environment. This study, nevertheless, focuses wholly on continuous flow manufacturing (CFM), which is distinctly different from piece-part manufacturing, as the sampling unit is described in light of laboratory requirements instead of the production unit. Based on this fact, this paper proposes an integrated EPQ scheme jointly designed with control-chart design and maintenance planning for the CFM process. A developed genetic algorithm is employed to minimize the expected total production cost under constraints on the economic-statistical quality. Of the costs considered, quality-related costs are quantified via an approximate formula derived from the quality loss curve depending on Taguchi’s quality loss function. A sensitivity analysis is performed on the expected total production cost and variations in the chosen system parameter values to offer insights into the issue. Finally, a comparative investigation of the suggested model and two similar models that contributed to this field is conducted.

Determining Cognitive Workload Using Physiological Measurements: Pupillometry and Heart-Rate Variability.

The adoption of Industry 4.0 technologies in manufacturing systems has accelerated in recent years, with a shift towards understanding operators' well-being and resilience within the context of creating a human-centric manufacturing environment. In addition to measuring physical workload, monitoring operators' cognitive workload is becoming a key element in maintaining a healthy and high-performing working environment in future digitalized manufacturing systems. The current approaches to the measurement of cognitive workload may be inadequate when human operators are faced with a series of new digitalized technologies, where their impact on operators' mental workload and performance needs to be better understood. Therefore, a new method for measuring and determining the cognitive workload is required. Here, we propose a new method for determining cognitive-workload indices in a human-centric environment. The approach provides a method to define and verify the relationships between the factors of task complexity, cognitive workload, operators' level of expertise, and indirectly, the operator performance level in a highly digitalized manufacturing environment. Our strategy is tested in a series of experiments where operators perform assembly tasks on a Wankel Engine block. The physiological signals from heart-rate variability and pupillometry bio-markers of 17 operators were captured and analysed using eye-tracking and electrocardiogram sensors. The experimental results demonstrate statistically significant differences in both cardiac and pupillometry-based cognitive load indices across the four task complexity levels (rest, low, medium, and high). Notably, these developed indices also provide better indications of cognitive load responding to changes in complexity compared to other measures. Additionally, while experts appear to exhibit lower cognitive loads across all complexity levels, further analysis is required to confirm statistically significant differences. In conclusion, the results from both measurement sensors are found to be compatible and in support of the proposed new approach. Our strategy should be useful for designing and optimizing workplace environments based on the cognitive load experienced by operators.

Advanced Refinement Techniques for Optimal Sugar Crystal Production

This study investigates the transformation of sap into crystal sugar, emphasizing the enhancement of sugar quality through meticulous purification processes. The methodology involved a sequential observation of various production stages: milling, refining, evaporation, cooking, and finishing, each integrated with a quality assurance analysis to maintain high product standards. Critical parameters such as Brix, pol, temperature, pH, color balance, phosphate, sulfur content, and turbidity were rigorously monitored. Findings indicate that strategic control of these parameters at the purification stage significantly reduces impurities, thus ensuring the consistency and quality of the final crystal sugar product. The implications of this study suggest that systematic quality controls and precise parameter management may lead to improvements in sugar refinement processes, with potential applications in similar manufacturing environments globally. Highlights: Quality assurance: Regular analysis of sap quality ensures consistent production standards. Purification stage: Vital parameters such as Brix, pH, and color balance are monitored for impurity removal. Product quality: Maintenance and adjustments based on analysis ensure high-quality crystal sugar output. Keywords: Crystal Sugar, Cane, Crystallization

Improving Equipment Effectiveness through Visual Stream Mapping: Some Exploratory Research Findings in the Ready-Made Garment (RMG) Sector

Improving equipment effectiveness is crucial for flexible manufacturing, ensuring that machines and tools perform their functions efficiently and consistently. Our study aimed to enhance the Overall Equipment Effectiveness (OEE) in Bangladesh’s Ready-Made Garments (RMG) manufacturing system. We used the DMADV methodology incorporating Visual Stream Mapping (VSM) and OEE. By utilising VSM, we identified issues, eliminated them in the design phase, and evaluated performance in the verification phase. We automated the material handling system to reduce handling time, and the result was a significantly improved OEE in the automated manufacturing system compared to the manual one. This study has numerous benefits in flexible manufacturing and operations management, from immediate efficiency improvements to long-lasting organisational cultural transformations. Thus, it’s a noteworthy topic for practical applications and research. Enhancing Equipment Effectiveness through Visual Stream Mapping 4.0 has broad-reaching implications, including improved productivity, reduced waste, increased efficiency, better resource utilisation, and a more agile and responsive manufacturing environment. Although OEE and VSM are frequently used separately in different manufacturing systems, this study’s novelty lies in their combined application within garment manufacturing.

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.

Trusted Data Acquisition in Microelectronics Manufacturing as a Basis for ML-Optimized Processing

In the digitization of manufacturing processes, one of the major goals is the connection of production facilities and the use of data to digitize business processes. In order to optimize manufacturing processes and maximize the quality of the resulting product, further process information and data directly from the work piece and from the manufacturing environment are required to achieve a holistic system view in addition to the selected data that the manufacturing systems already provide. Within the SiEvEI 4.0 project, a research consortium from industry and academia works on process digitization for a manufacturing scenario where high value electronic goods are built in a distributed manufacturing environment. The key research topics addressed are the implementation of a Chain of Trust [CoT] for trustworthy distributed manufacturing and the application of artificial intelligence/machine learning to analyze and eventually optimize manufacturing processes. The basic concepts of this approach have been presented at IMAPS 2021. As an update, this paper reports on the actual experimental evaluation of these concepts in two different assembly lines, including data acquisition, data handling and AI processing with the goal to optimize processing targeting higher production yield and product quality. Specific for this work is the focus on high mix/low volume SMD assembly using fully automated equipment and Solder Ball Application. As a result, the paper presents the experimental validation of the manufacturing process digitization and the use this digital description of a process combination to make a distributed manufacturing flow safe and increase product/process quality.

THE ROLE OF LEADERSHIP STYLES IN ENHANCING WORKPLACE EFFECTIVENESS: CASE STUDY OF NIGERIAN MANUFACTURING FIRMS

Nigeria’s diverse manufacturing sector, characterized by varying organizational structures, product portfolios, workforce demographics, and market scope, necessitates nuanced leadership strategies. This study delves into the multifaceted environment of Nigerian manufacturing, emphasizing the importance of adaptive leadership practices amid dynamic industrial demands. This study aims to assess the prevalent leadership styles in Nigerian manufacturing firms, evaluate the impact of leadership styles on workplace effectiveness, and recommend context-specific leadership strategies. A mixed-method approach was employed by combining quantitative and qualitative methods. A structured questionnaire was administered to 320 participants using stratified random sampling. Statistical analyses included chi-square tests and ANOVA. Thematic analysis was applied to the qualitative responses. The results show that there are significant differences in the prevalence of leadership styles, affirming the need for diverse approaches, which significantly impact workplace effectiveness across various dimensions, emphasizing their critical role. Context-specific leadership strategies were identified through thematic analysis, highlighting the importance of tailored approaches. This study reveals the dynamic landscape of leadership styles in Nigerian manufacturing. These findings underscore the necessity of adaptive leadership to optimize workplace effectiveness. Practical recommendations offer valuable insights for leaders and policymakers and promote organizational success in this sector.

New Approach to the Analysis of Manufacturing Processes with the Support of Data Science

This article introduces process mining as an innovative approach to enterprise data analysis, offering a systematic method for extracting, analyzing, and visualizing digital traces within information systems. The technique establishes connections within data, forming intricate process maps that serve as a foundation for the comprehensive analysis, interpretation, and enhancement of internal business processes. The article presents a methodical procedure designed to analyze processes using process mining. This methodology was validated through a case study conducted in the Fluxicon Disco software (version 3.6.7) application environment. The primary objective of this study was to propose and practically validate a methodical procedure applied to industrial practice data. Focusing on the evaluation and optimization of manufacturing processes, the study explored the integration of a software tool to enhance efficiency. The article highlights key trends in the field, providing valuable insights into process flows and identifying areas for improvement. The results contribute to the growing body of knowledge in process mining, emphasizing its applicability in fostering a more efficient and competitive manufacturing environment. In the model example, we successfully achieved a reduction in the time required for production cycles by 15% and improved resource utilization by 20%. This resulted in an increased process efficiency and a potential reduction in the required number of workers by up to 10%. These outcomes offer promising evidence of the advantages of our method and its application in an industrial setting.

A novel method for segmentation of titanium microtexture regions via sensor data fusion

The current nondestructive inspection paradigm of mono-modal testing and signal-over-threshold call criteria is insufficient for characterization of microtexture regions in titanium alloys, which exceeds the capabilities of any single, practical sensing technique that could be deployed to a manufacturing environment for quality control. In this work, we develop a data fusion-based solution to microtexture region characterization. The material problem and potential inspection methods are reviewed, and registered datasets are presented. Next, we develop a data fusion algorithm that combines covariance-generalized matching component analysis and a conditionally Gaussian hypermodel to successfully estimate the boundaries and average crystallographic orientations of microtexture regions from scanning electron microscopy electron backscatter diffraction and eddy current testing data.

Production of α-olefins from biomass gasification: Process development and multi-objective optimization for techno-economic and environmental goals

Efficient utilization of biomass as a substitute for fossil fuels holds great promise for meeting future greener chemical demands in a sustainable manner. This study presents a process simulation and multi-objective optimization for an innovative process of biomass-based Fischer-Tropsch synthesis of value-added linear α-olefins. Optimal design and operation solutions with heat-exchange network integration taken into account are obtained that minimize total annual costs (TAC), generalized energy consumption (GEC), greenhouse gas emissions (GHG), and maximize product yields (η). Compared to the benchmark case, this optimization leads to a 9.93 % reduction in TAC, a 21.76 % reduction in GEC, a 19.23 % reduction in GHG, and increases η by 1.41 %. Additionally, a comprehensive factor analysis of the optimal design and operation parameters in the Pareto front helps to discover patterns related to energy consumption, environmental impact, or economy. This allows to maximize a specific key process performance metric while minimizing the loss in the other, thus enabling flexible process design and operation under diverse manufacturing environments.

From CySkin to ProxySKIN: Design, Implementation and Testing of a Multi-Modal Robotic Skin for Human-Robot Interaction.

The Industry 5.0 paradigm has a human-centered vision of the industrial scenario and foresees a close collaboration between humans and robots. Industrial manufacturing environments must be easily adaptable to different task requirements, possibly taking into account the ergonomics and production line flexibility. Therefore, external sensing infrastructures such as cameras and motion capture systems may not be sufficient or suitable as they limit the shop floor reconfigurability and increase setup costs. In this paper, we present the technological advancements leading to the realization of ProxySKIN, a skin-like sensory system based on networks of distributed proximity sensors and tactile sensors. This technology is designed to cover large areas of the robot body and to provide a comprehensive perception of the surrounding space. ProxySKIN architecture is built on top of CySkin, a flexible artificial skin conceived to provide robots with the sense of touch, and arrays of Time-of-Flight (ToF) sensors. We provide a characterization of the arrays of proximity sensors and we motivate the design choices that lead to ProxySKIN, analyzing the effects of light interference on a ToF, due to the activity of other sensing devices. The obtained results show that a large number of proximity sensors can be embedded in our distributed sensing architecture and incorporated onto the body of a robotic platform, opening new scenarios for complex applications.

The effect of lean manufacturing and physical work environment on labor productivity in the apparel industry in Indonesia: case study

In general, this research aims to analyze and determine the effect of the application of lean manufacturing which is focused on eliminating waste and the influence of the physical work environment on labor productivity. This research used primary data through the distribution of questionnaires conducted to employees of Company X with a total of 230 respondents from sections in departments directly related to research variables. Data analysis methods using the Structural Equation Model (SEM) measured by the Partial Least Square (PLS) model, and data processing using SmartPLS 4.0 Software. Based on the results of calculations, data analysis shows that the application of principles related to lean manufacturing, which in this research is focused on the process of eliminating waste has a positive effect on the productivity of the workforce. The creation of a physical work environment that meets standards also positively affects the productivity of the workforce.

An exact algorithm for unpaired pickup and delivery vehicle routing problem with multiple commodities and multiple visits

This paper addresses a new multi-commodity unpaired pickup and delivery vehicle routing problem in which each vehicle is allowed to visit each customer more than once for both pickup and delivery of each commodity. It is a complex variant of the classical capacitated vehicle routing problem and comes from resource transfer among different plants in a distributed manufacturing environment. Notably, the pairing decisions of pickup and delivery for each commodity and the routing decisions among customers are interdependent, and they need to be made simultaneously and collaboratively. Firstly, a mixed integer programming model is developed, and then new valid inequalities are derived to improve the linear programming relaxation. Afterwards, a branch-and-cut algorithm is proposed by exploring different branching strategies and tailored separation algorithms. Computational results based on real-case instances from an automobile manufacturing company demonstrate that the costs are considerably reduced by considering the multiple visits compared to the single visit of each customer. In addition, our proposed branch-and-cut algorithm outperforms a manual solution method adopted by the company in practice and two solution methods in the literature. Furthermore, two adaptations of our branch-and-cut algorithm also perform better than two state-of-the-art algorithms in solving two closely-related problems in terms of solution quality and computation time. Sensitivity analyses with respect to vehicle capacity, vehicle duration, and vehicle speed are conducted to derive several valuable managerial insights.

Ipar 4.0 szemléletű tesztelés és validálás kialakításának hatásai az elektronikai gyártásban

The continuous demand increase of electronic items challenges the electronic manufacturing companies. The economic and manufacturing challenges, and the Industry 4.0 based solutions of having a great product portfolio of a regional, original equipment manufacturer are presented in this study. The acquirement and the implementation of the principles of Industry 4.0 results a new production environment, but the positive effects of that can be demonstrated from financial and production efficiency sides. The mindset change of the test and validation area of an electronic company, and the steps required to achieve Industry 4.0 based test solutions are presented in this article. You can read about the practical implementation of a pioneering technology in the field of informatics, Big Data oriented data processing in a real manufacturing environment. The changes related to hardware and software design and data acquisition are presented, also you can read about the resulting cost saving, production efficiency improvement and product output growth of the study.

Mixed-Up Experience Replay for Adaptive Online Condition Monitoring

Data-driven predictive maintenance reduces manufacturing downtime, and complex process-sensing relationships encourage the use of Deep Learning to automatically extract features. However, labeled training data is often lacking, and novel fault conditions may occur. Practical deployments must learn from unlabeled data, adapt to emerging conditions, and do so without prior knowledge of when the condition changes. Combining state-of-the-art Self-Supervised Learning (SSL) with Continual Learning (CL) facilitates adaptation as new conditions are observed. This study proposes a framework for adaptive online condition monitoring based on Barlow Twins SSL and novel Mixed-Up Experience Replay (MixER) for unsupervised CL. Tailored for 1D sensing data, Barlow Twins effectively clusters unlabeled data. When combined with MixER, the system outperforms state-of-the-art unsupervised CL on a motor health condition data set, reaching 92.4% classification accuracy. Future work will demonstrate human-in-the-loop integration for real manufacturing environments.

Automated self-optimization, intensification, and scale-up of photocatalysis in flow.

The optimization, intensification, and scale-up of photochemical processes constitute a particular challenge in a manufacturing environment geared primarily toward thermal chemistry. In this work, we present a versatile flow-based robotic platform to address these challenges through the integration of readily available hardware and custom software. Our open-source platform combines a liquid handler, syringe pumps, a tunable continuous-flow photoreactor, inexpensive Internet of Things devices, and an in-line benchtop nuclear magnetic resonance spectrometer to enable automated, data-rich optimization with a closed-loop Bayesian optimization strategy. A user-friendly graphical interface allows chemists without programming or machine learning expertise to easily monitor, analyze, and improve photocatalytic reactions with respect to both continuous and discrete variables. The system's effectiveness was demonstrated by increasing overall reaction yields and improving space-time yields compared with those of previously reported processes.