Inspection Strategies and Defect Prediction Models for quality control in low-volume productions

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A new approach for evaluating experienced assembly complexity based on Multi Expert-Multi Criteria Decision Making method

In manufacturing, complexity is considered a key aspect that should be managed from the early phases of product and system design to improve performance, including productivity, efficiency, quality, and costs. The identification of suitable methods to assess complexity has always been of interest to researchers and practitioners. As complexity is affected by several aspects of different nature, it can be assessed from objective or subjective viewpoints or a combination of both. To assess experienced complexity, the analysis relies on the subjective evaluations given by practitioners, usually expressed on nominal or ordinal scales. However, methods found in the literature often violate the properties of the scales, potentially leading to bias in the results. This paper proposes a methodology based on the analysis of categorical data using the multi expert-multi criteria decision making method. A number of criteria are adopted to assess assembly complexity and, from subjective evaluations of operators, product assembly complexity is assessed at an individual level and then, aggregating results, at a global level. A comparison between experienced complexity and an objective assessment of complexity is also performed, highlighting similarities and differences. The assessment of experienced complexity is much more straightforward and less demanding than objective assessments. However, this study showed that it is preferable to use objective assessments for highly complex products as individuals do not discriminate between different complexity levels. An experimental campaign is conducted regarding a manual assembly of ball-and-stick products to show the applicability of the methodology and discuss the results

Challenges and opportunities of collaborative robots for quality control in manufacturing: evidences from research and industry

Purpose - In the context of Industry 4.0, collaborative robots - which might be equipped with different types of sensors - have been gaining ground, used to cooperate with humans in quality control of finished or semi-finished products. Compared to the various applications of collaborative robotics in manufacturing (e.g., material handling, assembly, pick and place, and positioning), widely studied and adopted in industry, quality control and testing have not yet reached their full potential. This paper aims to study the state-of-the-art collaborative robotics used for quality control purposes in both academia and industry. Design/methodology/approach – This paper analyses in a structured way the scientific literature and some prominent real industrial case studies regarding the state-of-the-art of quality control using collaborative robotic systems in manufacturing. Findings - The analysis enables the identification and definition of the main challenges and opportunities that the manufacturing sector is facing for the large-scale use of the new quality control paradigm. Results show that collaborative robotics in quality still plays a marginal role and is mainly adopted for in-process visual inspections to increase system efficiency. Some barriers still hamper the full adoption of this paradigm, but there is plenty of opportunity for research and economic growth. Originality/value - The innovative aspect of this research is the combined analysis of scientific articles and real-life case studies that provide a comprehensive overview of the research and actual use in industry of this emerging paradigm of quality control

The effect of heat treatment and impact angle on the erosion behavior of nickel-tungsten carbide cold spray coating using response surface methodology

This study elucidates the performance of cold-sprayed tungsten carbide-nickel coating against solid particle impingement erosion using alumina (corundum) particles. After the coating fabrication, part of the specimens followed two different annealing heat treatment cycles with peak temperatures of 600 °C and 800 °C. The coatings were examined in terms of microstructure in the as-sprayed (AS) and the two heat-treated conditions (HT1, HT2). Subsequently, the erosion tests were carried out using design of experiments with two control factors and two replicate measurements in each case. The effect of the heat treatment on the mass loss of the coatings was investigated at the three levels (AS, HT1, HT2), as well as the impact angle of the erodents (30°, 60°, 90°). Finally, the response surface methodology (RSM) was applied to analyze and optimize the results, building the mathematical models that relate the significant variables and their interactions to the output response (mass loss) for each coating condition. The obtained results demonstrated that erosion minimization was achieved when the coating was heat treated at 600 °C and the angle was 90°

Defect prediction models to improve assembly processes in low-volume productions

Assembly processes in low-volume productions, i.e., single-units or small-sized-lots, are often characterized by a high level of customization and complexity. As a consequence, the scarcity of historical data and the difficulty in applying standard statistical techniques make process control extremely challenging. Accordingly, identifying effective diagnostic tools plays a key role in such productions. This paper proposes an innovative method for identifying critical workstations in assembly processes based on defect prediction models. Starting from the level of complexity in terms of assembly process and design, the method allows identifying the workstations whose defectiveness deviates, at a certain confidence level, from the predicted average value. Once the causes leading to significant nonconformities have been detected, appropriate corrective actions may be promptly undertaken to improve the process. An example of implementation of the method in wrapping machines production is presented and discussed

Subjective vs objective assembly complexity assessment: a comparative study in a Human-Robot Collaboration framework

The 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

Defect Probability Estimation for Hardness-Optimised Parts by Selective Laser Melting

The development of reliable additive manufacturing (AM) technologies to process metallic materials, e.g. selective laser melting (SLM), has allowed their adoption for manufacturing fnal components. To date, ensuring part quality and process control for low-volume AM productions is still critical because traditional statistical techniques are often not suitable. To this aim, extensive research has been carried out on the optimisation of material properties of SLM parts to prevent defects and guarantee part quality. Amongst all material properties, defects in surface hardness are of particular concern as they may result in an inadequate tribological and wear resistance behaviour. Despite this general interest, a major void still concerns the quantifcation of their extent in terms of probability of defects occurring during the process, although it is optimised. Considering these issues, this paper proposes a novel approach to quantify the probability of occurrence of defects in hardness-optimised parts by SLM. First, three process variables, i.e. laser power, scan speed and hatching distance, are studied considering their efect on hardness. Design of Experiments and Response Surface Methodology are exploited to achieve hardness optimisation by controlling process variables. Then, hardness defect probability is estimated by composing the uncertainty afecting both process variables and their relationship with the hardness. The overall procedure is applied to AlSi10Mg alloy, which is relevant for both aerospace and automotive applications. The approach this study proposes may be of assistance to inspection designers to efectively and efciently set up quality inspections in early design phases of inspection planning

Investigation of the Effect of Low-Temperature Annealing and Impact Angle on the Erosion Performance of Nickel-Tungsten Carbide Cold Spray Coating Using Design of Experiments

This study investigates the solid particle erosion performance of cold sprayed tungsten carbide-nickel coatings using alumina particles as erodent material. After coating fabrication, specimens were annealed in an electric furnace at a temperature of 600 °C for 1 hour. The coatings were examined in terms of microhardness and microstructure in the as-sprayed (AS) and annealed (AN) conditions. Subsequently, the erosion tests were carried out using a General Full Factorial Design with two control factors and two replicates for each experimental run. The effect of the annealing on the erosion behavior of the coating was investigated at the two levels (AS and AN conditions), along with the impact angle of the erodents at three levels (30°, 60°, 90°). Finally, two regression models that relate the impact angle to the mass loss were separately obtained for the two cold spray coatings

Defect prediction model for wrapping machines assembly

Purpose – Development of a defect prediction model for the assembly of wrapping machines. Design/methodology/approach – The assembly process of wrapping machines is firstly decomposed into several steps, called workstations, each one potentially critical in generating defects. According to previous studies, two assembly complexity factors related to the process and the design are evaluated. Experimental defect rates in each workstation are collected and a bivariate prediction model is developed. Findings – Defects occurring in low-volume production, such as those of wrapping machines, may be predicted by exploiting the complexity based on the process and the design of the assembly. Research limitations/implications – Although the defect prediction model is designed for the assembly of wrapping machines, the research approach can provide a framework for future investigation on other low-volume productions of similar electromechanical and mechanical products. Practical implications – The defect prediction model is a powerful tool for quantitatively estimating defects of newly developed wrapping machines and supporting decisions for assembly quality-oriented design and optimisation. Originality/value – The proposed model is one of the first attempts to predict defects in low-volume production, where the limited historical data available and the inadequacy of traditional statistical approaches make the quality control extremely challenging

Inspection planning by defect prediction models and inspection strategy maps

Designing appropriate quality-inspections in manufacturing processes has always been a challenge to maintain competitiveness in the market. Recent studies have been focused on the design of appropriate in-process inspection strategies for assembly processes based on probabilistic models. Despite this general interest, a practical tool allowing for the assessment of the adequacy of alternative inspection strategies is still lacking. This paper proposes a general framework to assess the efectiveness and cost of inspection strategies. In detail, defect probabilities obtained by prediction models and inspection variables are combined to defne a pair of indicators for developing an inspection strategy map. Such a map acts as an analysis tool, enabling positioning assessment and benchmarking of the strategies adopted by manufacturing companies, but also as a design tool to achieve the desired targets. The approach can assist designers of manufacturing processes, and particularly low-volume productions, in the early stages of inspection planning