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

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

Prediction of Defect Propensity for the Manual Assembly of Automotive Electrical Connectors

Assembly for automotive production represents a significant proportion of total manufacturing cost, manufacturing time, and overall product cost. Humans remain a cost effective solution to adapt to the requirements of increasing product complexity and variety present in today\u27s flexible manufacturing systems. The human element present in the manufacturing system necessitates a better understanding of the human role in manufacturing complexity. Presented herein is a framework for enumerating assembly variables correlated with the potential for quality defect, presented in the design, process, and human factors domain. A case study is offered that illustrates on a manual assembly process the effect that complexity variables have on assembly quality

A Study of the Effects of Manufacturing Complexity on Product Quality in Mixed-Model Automotive Assembly

The objective of this research is to test the hypothesis that manufacturing complexity can reliably predict product quality in mixed-model automotive assembly. Originally, assembly lines were developed for cost efficient mass-production of standardized products. Today, in order to respond to diversified customer needs, companies have to allow for an individualization of their products, leading to the development of the Flexible Manufacturing Systems (FMS). Assembly line balancing problems (ALBP) consist of assigning the total workload for manufacturing a product to stations of an assembly line as typically applied in the automotive industry. Precedence relationships among tasks are required to conduct partly or fully automated Assembly Line Balancing. Efforts associated with manual precedence graph generation at a major automotive manufacturer have highlighted a potential relationship between manufacturing complexity (driven by product design, assembly process, and human factors) and product quality, a potential link that is usually ignored during Assembly Line Balancing and one that has received very little research focus so far. The methodology used in this research will potentially help develop a new set of constraints for an optimization model that can be used to minimize manufacturing complexity and maximize product quality, while satisfying the precedence constraints. This research aims to validate the hypothesis that the contribution of design variables, process variables, and human-factors can be represented by a complexity metric that can be used to predict their contribution on product quality. The research will also identify how classes of defect prevention methods can be incorporated in the predictive model to prevent defects in applications that exhibit high level of complexity. The manufacturing complexity model is applied to mechanical fastening processes which are accountable for the top 28% of defects found in automotive assembly, according to statistical analysis of historical data collected over the course of one year of vehicle production at a major automotive assembly plant. The predictive model is validated using mechanical fastening processes at an independent automotive assembly plant. This complexity-based predictive model will be the first of its kind that will take into account design, process, and human factors to define complexity and validate it using a real-world automotive manufacturing process. The model will have the potential to be utilized by design and process engineers to evaluate the effect of manufacturing complexity on product quality before implementing the process in a real-world assembly environment

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

An experimental investigation on the relationship between perceived assembly complexity and product design complexity

Complexity is one of the main drivers inducing increased assembly cost, operational issues and increased lead time for product realisation, and continues to pose challenges to manual assembly operations. In the literature, assembly complexity is widely viewed from both objective and subjective perspectives. The objective perspective relates complexity directly to the characteristics of a process without accounting the characteristics of performers, whereas, subjective perspective considers complexity as a conjunction between process and performer characteristics. This article aims to investigate the link between perceived assembly complexity and product complexity by providing a prediction model relying on a series of natural experiments. In these experiments, the participants were asked to assemble a series of ball-and-stick models with varying degree of product complexity based on a clear 2D assembly work instruction. Complexity of each model was objectively estimated by considering structural properties associated with handling and insertion of assembly parts and their connectivity pattern. Moreover, perceived complexity is approached based on the subjective interpretations of the participants on the difficulty associated with the assembly operation of each model. The results showed that product complexity and assembly time is super-linearly correlated; an increase in the product complexity is accompanied with an increase in assembly time, rework rate and human errors. Moreover, a sigmoid curve is proposed for the relationship between perceived assembly complexity and product complexity indicating that human workers start to perceive assembly operation of a particular product as complex if the product complexity reaches a critical threshold which can vary among individuals with different skill sets, experience, training levels and assembly preferences

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

A method to assess assembly complexity of industrial products in early design phase

Complexity is one of the factors, inducing high cost, operational issues, and increased lead time for product realization and continues to pose challenges to manufacturing systems. One solution to reduce the negative impacts of complexity is its assessment, which can help designers to compare and rationalize various designs that meet the functional requirements. In this paper, a systemic approach is proposed to assess complexity of a product's assembly. The approach is based on Hückel's molecular orbital theory and defines complexity as a combination of both the complexity of product entities and their topological connections. In this model, the complexity of product entities (i.e., components and liaisons) is defined as the degree to which the entity comprises structural characteristics that lead to challenges during handling or fitting operations. The characterization of entity complexities is carried out based on the widely used DFA principles. Moreover, the proposed approach is tested on two case studies from electronics industry for its validity. The results showed that the approach can be used at initial design stages to improve both the quality and assemblability of products by reducing their complexity and accompanying risks

A method to assess assembly complexity of industrial products in early design phase

Complexity is one of the factors, inducing high cost, operational issues, and increased lead time for product realization and continues to pose challenges to manufacturing systems. One solution to reduce the negative impacts of complexity is its assessment, which can help designers to compare and rationalize various designs that meet the functional requirements. In this paper, a systemic approach is proposed to assess complexity of a product's assembly. The approach is based on Hückel's molecular orbital theory and defines complexity as a combination of both the complexity of product entities and their topological connections. In this model, the complexity of product entities (i.e., components and liaisons) is defined as the degree to which the entity comprises structural characteristics that lead to challenges during handling or fitting operations. The characterization of entity complexities is carried out based on the widely used DFA principles. Moreover, the proposed approach is tested on two case studies from electronics industry for its validity. The results showed that the approach can be used at initial design stages to improve both the quality and assemblability of products by reducing their complexity and accompanying risks