1,721,004 research outputs found
Inspection Strategies and Defect Prediction Models for quality control in low-volume productions
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A benchmark analysis of the quality of distributed additive manufacturing centers
Full text linkPurpose: Nowadays, companies are increasingly adopting additive manufacturing (AM) technologies due to their flexibility and product customization, combined with non-dramatic increases in per unit cost. Moreover, many companies deploy a plurality of distributed AM centers to enhance flexibility and customer proximity. Although AM centers are characterized by similar equipment and working methods, their production mix and volumes may be variable. The purpose of this paper is to propose a novel methodology to (1) monitor the quality of the production of individual AM centers and (2) perform a benchmarking of different AM centers. Design/methodology/approach: This paper analyzes the quality of the production output of AM centers in terms of compliance with specifications. Quality is assessed through a multivariate statistical analysis of measurement data concerning several geometric quality characteristics. A novel operational methodology is suggested to estimate the fraction nonconforming of each AM center at three different levels: (1) overall production, (2) individual product typologies in the production mix and (3) individual quality characteristics. Findings: The proposed methodology allows performing a benchmark analysis on the quality performance of distributed AM centers during regular production, without requiring any ad hoc experimental test. Originality/value: This research assesses the capability of distributed AM centers to meet crucial quality requirements. The results can guide production managers toward improving the quality of the production of AM centers, in order to meet customer expectations and enhance business performance
Effect of process parameters on parts quality and process efficiency of Fused Deposition Modeling
Full text linkFused Deposition Modeling (FDM) is an additive manufacturing technique for fabricating parts directly from computer-aided design data by melting, extruding, and resolidifying a thermoplastic filament. This paper presents a methodology for optimizing both process efficiency, i.e., time and energy consumption, and part quality, i.e., surface roughness and dimensional accuracy, of Polylactic Acid (PLA) components produced by FDM. In this work, a Design of Experiments (DoE) approach is adopted to quantify the effects of deposition parameters on process efficiency and part quality outputs. Specifically, the investigated input parameters are layer height, fill density, extruder temperature, part orientation, number of shells, print speed and retraction speed. The mathematical models relating the significant process parameters to the output responses are developed and the responses are optimized considering different scenarios. An experimental validation is performed to test the adequacy of such optimizations. These experimental results showed that, according to the context, different parameter settings pursue different goals in terms of part quality and process efficiency. The proposed approach may effectively help designers determine process parameters’ settings to optimize both part quality and process efficiency and can be applied to either prototype or part production
Enhanced Food Quality by Digital Traceability in Food Processing Industry
No full textThis study explores the enhancement of quality traceability in the food processing industry through the integration of modern digital tools, specifically blockchain technology. By combining a thorough literature review with the analysis of real-case studies, the research investigates current digital trends and their practical applications in the food processing sector. The findings show that blockchain-based approaches significantly improve supply chain transparency and quality management. Despite the potential benefits, the study also identifies challenges in practical implementations, such as resistance to adoption and the need for substantial investment in digital infrastructure. The research highlights the limited cultural attitude within the industry towards the comprehensive adoption of these modern tools, with their usage mostly confined to isolated case studies rather than a structured, widespread experimental orientation. Practical implications include providing businesses with guidelines for implementing digital tools to enhance quality traceability and management. Social implications underscore the critical role of these tools in meeting societal demands for food safety and transparency, particularly regarding information on raw materials, processing, and preservation methods. Thus, this paper offers a comprehensive overview of the use of blockchain and other digital tools to improve quality traceability in the food processing industry, contributing valuable insights and guidelines for future implementations
Machine vision techniques for quality control in the wine industry
Full text linkThe wine industry is undergoing a significant digital transformation with the integration of Machine Vision Systems (MVS) for automated, precise quality control across various production stages. Despite increasing interest in MVS applications, the literature lacks a comprehensive synthesis of how these technologies are integrated throughout the winemaking process. This systematic review addresses this gap by categorizing MVS applications according to their technological approach - Stereo Vision (SV), Remote Sensing (RS), Hyperspectral Imaging (HSI), X-ray Imaging (XRI), Thermal Imaging (TI), and Magnetic Resonance Imaging (MRI) - and mapping their deployment across distinct phases of wine production. A total of 77 studies published between 2013 and 2025 were selected based on PRISMA guidelines and clearly defined inclusion criteria. The findings reveal significant advances in vineyard monitoring, grape sorting, fermentation tracking, and bottling inspection, with MVS technologies enhancing operational efficiency, sustainability, and precision in quality assessment. Nonetheless, challenges persist, particularly in mid-stage processes such as crushing and filtration, and in transitioning laboratory innovations to industrial scales due to economic and infrastructural constraints. This review not only consolidates current knowledge but also outlines critical research gaps and future directions for the integration of MVS within a broader framework of smart and sustainable viticulture. The results are intended to inform researchers, technology developers, and policymakers engaged in the digital transformation of the agri-food sector
Subjective vs objective assembly complexity assessment: a comparative study in a Human-Robot Collaboration framework
Full text linkThe impact of manufacturing complexity on company performance can be significant, affecting productivity, efficiency, affordability, and quality if not managed correctly. Assessing and managing manufacturing complexity is a multifaceted task that involves both objective and subjective features, such as product complexity, assembly sequence, operator factors, and operation/management strategies. This study proposes a structured methodology to assess the perceived complexity of human-robot collaboration assembly processes. The methodology is based on 16 assembly complexity criteria and a multi-expert decision-making method for evaluation. Operators assign importance scores and agreement levels to each criterion using a five-level ordinal scale, and the linguistic data is processed using the Multi-Expert Multi-Criteria Decision Making (ME-MCDM) method [Yager(1993)]. This approach combines linguistic information provided for non-equally important criteria using maximum, minimum, and negation operators to obtain an overall synthetic linguistic value of perceived complexity using fuzzy logic. The proposed approach provides an assessment of perceived complexity at both individual and overall levels, aggregating all individual complexity assessments by the operator Ordered Weighted Average (OWA) [Yager(1993); Filev(1994)]. The proposed approach for assessing perceived complexity of assembly is compared with a purely objective assessment method, firstly proposed by Sinha et al. [Sinha(2012)]. This model was validated in various studies, and its effectiveness in quantifying the complexity of industrial products was demonstrated [Verna(2022)]. It is based on the molecular orbital theory and is applied to the engineering domain to analyse the complexity of cyber-physical systems. The model represents a cyber-physical system as several connected components where each component can be thought of as an atom, and the interfaces between them as inter-atomic interactions or chemical bonds. The complexity of the assembly is defined as the combination of three complexity components: handling complexity (C1), connections complexity (C2), and topological complexity (C3), as follows C=C1+C2∙C3. This objective model, based on structural characteristics of the assembly process, was used as a reference model for the subjective complexity model.
The comparison between subjective and objective assessment of complexity was performed in a real-world production environment, using a human-robot collaboration process for manufacturing custom electronic boards with different levels of complexity. The results showed a significant correlation between individual perceived complexity and objective complexity, indicating that the proposed perceived complexity model can be linked to the objective model. As structural complexity increases, higher levels of individual perceived complexity become more likely, but the variability in perceived complexity varies with structural complexity. These findings suggest that individual operator ability and cognitive factors, such as training, knowledge, and cultural and organisational factors, play a role in perceived complexity and require further investigation. The study also suggests that using perceived complexity to assess assembly complexity is suitable for low- and medium-complexity products, but not for high-complexity products, where objective complexity models may be more appropriate, since after a certain point operators do not distinguish between different levels of complexity.
The proposed methodology and data analysis approach offer a new perspective on assessing perceived complexity, relying solely on synthesis operators and statistical tools suitable for categorical data. Engineers can use the study's results to minimise perceived complexity and ensure alignment between perceived and objective complexity
Real‐Time Monitoring of Human and Process Performance Parameters in Collaborative Assembly Systems using Multivariate Control Charts
Full text linkWith the rise in customized product demands, the production of small batches with a wide variety of products is becoming more common. A high degree of fexibility is required from operators to manage changes in volumes and products, which has led to the use of Human-Robot Collaboration (HRC) systems for custom manufacturing. However, this variety introduces complexity that afects production time, cost, and quality. To address this issue, multivariate control charts are used as diagnostic tools to evaluate the stability of several parameters related to both product/process and human well-being in HRC systems. These key parameters monitored include assembly time, quality control time, total defects, and operator stress, providing a more holistic view of system performance. Real-time monitoring of process performance along with humanrelated factors, which is rarely considered in statistical process control, provides comprehensive stability control over all customized product variants produced in the HRC system. The proposed approach includes defning the parameters to be monitored, constructing control charts, collecting data after product variant assembly, and verifying that the set of parameters is under control via control charts. This increases the system's responsiveness to both process inefciencies and human well-being. The procedure can be automated by embedding control chart routines in the software of the HRC system or its digital twin, without adding additional tasks to the operator's workload. Its practicality and efectiveness are evidenced in custom electronic board assembly, highlighting its role in optimizing HRC system performance
Impact of product family complexity on process performance in electronic component assembly
Full text linkWith the advent of Industry 4.0 and the impending shift towards Industry 5.0, the integration of human-robot collaboration (HRC) into production systems has become increasingly widespread. This paradigm shift leverages collaborative robots, or cobots, to mitigate physical and mental strain on human workers, thereby increasing productivity and improving overall quality performance. This paper investigates the interplay of productivity and quality factors with assembly complexity in both manual and collaborative assembly systems. The focus is placed on a product family of electronic boards with varying levels of assembly complexity to provide a comprehensive comparison between manual assembly and two different collaborative assembly scenarios. Key performance metrics such as assembly time and total defects are evaluated. This case study, rooted in the electronics industry, seeks to provide a valuable perspective on how assembly complexity influences productivity and quality in product family assembly systems. The results of this study aim to contribute to the growing body of knowledge on the implementation of HRC in manufacturing, facilitate informed decision-making and encourage further advances in this rapidly evolving field
Collaborative robots for quality control: an overview of recent studies and emerging trends
No full textOver the past few decades, collaborative robots (cobots) have emerged as a key element in the advancement of smart industries and the transition to Industry 5.0, facilitating operations alongside human workers to reduce both cognitive and physical strain. Cobots have primarily been used for tasks such as material handling, assembly and precise positioning, but their integration into quality control and inspection remains underexplored. Using a mixed-methods approach, this paper conducts a thorough investigation of current applications of cobots in quality assurance, both in academic research and in industrial practice. Through a systematic review of the academic literature and analysis of real-world industrial case studies, the paper examines the current state and potential advances in manufacturing quality control facilitated by cobots. The findings suggest that while cobots are currently being used primarily to improve efficiency through in-process visual inspection, there are significant barriers to their widespread adoption in quality control. These barriers include high initial costs, lack of technical expertise among workers, integration challenges with existing systems, data security concerns and regulatory compliance issues. Nevertheless, the potential for research and industrial growth through the use of cobots in quality control is considerable. By drawing on insights from academic research and practical implementations, this study provides researchers, practitioners and policy makers with a comprehensive perspective on the innovative use of cobots to improve quality control in manufacturing
Integrating Economic Analysis and Reliability Assessment for Sustainable Management in the Italian Used Car Market
Full text linkThe used vehicle market has increasingly been recognised as a critical component in advancing sustainability objectives, particularly within the framework of a circular economy. In this study, a comprehensive assessment of the Italian used car sector has been conducted, with emphasis placed on its economic viability, environmental implications, and role in promoting resource efficiency through extended product life cycles. Economic indicators demonstrate that the reuse of vehicles not only reduces material waste and energy consumption associated with new car production, but also enhances accessibility and cost-effectiveness for consumers. To quantify the reliability of used vehicles and support informed decision-making among stakeholders, a predictive model was developed employing a dataset comprising over 100,000 pre-owned vehicles. Reliability was evaluated through the estimation of the Percentage of Residual Life (PRL), derived using a hybrid approach that integrates Weibull distribution-based survival analysis with multivariate regression techniques, calibrated against vehicle age and mileage. This modelling framework enables the estimation of remaining service life with high granularity, offering a standardised metric to assess vehicle longevity and performance risk. The integration of economic and reliability analyses provides a multidimensional understanding of the market, addressing both financial sustainability and operational dependability. Through this dual approach, a pathway has been proposed for enhancing the transparency, sustainability, and efficiency of used vehicle transactions in Italy. The findings are intended to inform policymakers, manufacturers, and consumers by highlighting the strategic potential of second-hand vehicles in reducing lifecycle emissions and promoting circularity in the automotive industry. Broader implications for sustainable transport policy, second-hand asset valuation, and market regulation are also discussed, situating the Italian used car market as a replicable model for sustainable vehicle ecosystem management in Europe and beyond
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