Nonlinearity Analysis of Multi-Station Assembly Processes

Dimensional control has a significant impact on the overall product quality and performance in large and complex multi-station assembly systems. From measurement data, the way to identify root causes for large variation of Key Product Characteristics (KPCs) is one of the most critical research topics in dimensional control. This paper proposes a new approach for multiple fault diagnosis in a multi-station assembly process by integrating multivariate statistical analysis with engineering model. Based on product/process information, by using the state space model, a set of fault patterns for multi-station assembly process are developed, which explicitly represent the relationship between the error sources and KPCs. The vectors of these patterns form an affine system. Afterwards, the Principal Component Analysis (PCA) is applied to conduct orthogonal diagonalization of the measurement data. Thus, the measurement data can be easily projected to the axes of the affine system. Whereby, the significance of each fault pattern shall be estimated accurately. Finally, a few case studies are also provided to validate the proposed methodology.

In view of the case that tolerance and maintenance design have been studied separately not to concentrate reflecting the relationship between process cost and product quality in the manufacturing systems, an optimization model of integrated tolerance and maintenance design for two-dimensional multi-station assembly processes is proposed. At first, with the relationship of dimension deviation propagation analyzed, a quality loss model for multi-station assembly processes is built; Then, a statistics feature model of deviation about fixture locating pin pairs is established, with the loss of the fixture process wear considered. At last the lifecycle cost of fixture system including fabrication and maintenance is defined, a two-dimensional multi-station assembly process optimization method was proposed, tolerance and maintenance parameters of the locating pin pairs in two-dimensional multi-station assembly process are optimized and designed. Automotive body side aperture assembly is given as an example to illuminate that the method is reasonable and effective.

This paper presents a novel approach for optimal key characteristics-based sensor distribution in a multi-station assembly process, for the purpose of diagnosing variation sources responsible for product quality defects in a timely manner. Current approaches for sensor distribution are based on the assumption that measurement points can be allocated at arbitrary locations on the part or subassembly. This not only presents challenges in the implementation of these approaches but additionally does not allow required product assurance and quality control standards to be integrated with them, due to lack of explicit relations between measured features and geometric dimensioning and tolerancing (GD&T). Furthermore, it causes difficulty in calibration of measurement system and increases the likelihood of measurement error due to the introduction of measurement points not defined in GD&T. In the proposed approach, we develop methodology for optimal sensor allocation for 6-sigma root cause analysis that maximizes the number of measurement points placed at critical design features called Key Characteristics (KCs) which are classified into: Key Product Characteristics and Key Control Characteristics and represent critical product and process design features, respectively. In particular, KCs have defined dimensional and geometric tolerances which provides necessary design reference model for process control and diagnosis of product 6-sigma variation faults. The proposed approach allows obtaining minimum required production system 6-sigma diagnosability. A feature-based procedure is proposed which includes Genetic Algorithm-based approach (allowing pre-defined KCs as the measurement points) and state-of-the-art approaches (unrestricted location of measurement points) to iteratively include arbitrary measurement points together with KCs in the final sensor layout. A case study of automotive assembly processes is used to illustrate the proposed feature-based approach.

: Manufacturing process models are developed for a data-driven, reconfiguarable manufacturing facility. A human assembly process model is developed to characterize assembly processes. Machine and tool resources are characterized for manufacturing processes to provide a broad characterization of manufacturing process coverage in a hypothetical final assembly factory for infantry fighting vehicles. A sample population of manufacturing process parameters and resources is collected and incorporated into a manufacturing capability and process model library. Requirements for a virtual manufacturing environment are developed, and an initial, prototype version is implemented to facilitate process model validation, configuration, and experimentation.

In assembly process control and design optimization, it is critical to establish a mathematical model that describes the relationship between the dimensional quality of the final product and the various process parameters (e.g., the fixture layout and locator position deviation). This article presents a surrogate modeling methodology for multistage assembly processes to characterize the relationship between fixture layout and product dimensional quality. The mathematical structure of the model is derived from a physical analysis based on first principles and then the parameters of the model are identified using data from computer experiments. The resulting surrogate model can enable fixture layout optimization in process planning. A comprehensive case study of a multistage assembly process is also presented to demonstrate the effectiveness and high fidelity of the developed method.

Shewhart type fuzzy charts have been immensely proposed to deal with several practical problems of fuzzy data in monitoring and examining manufacturing processes. In mediating the shortcomings of indistinguishable classification conditions of a recently proposed fuzzy and s charts, this paper aims at developing a quantitative approach based on the index of optimism for fuzzy judgement of online manufacturing processes. By quantifying the linguistic notion ‘rather’ that is usually mentioned as a qualitative term in previous researches, we make it numerically measured in this study; as such, our thorough evaluation conditions of a manufacturing process, including in control, rather in-control, rather out of control and out of control, are more sufficient and justified. In addition, our proposed approach with the multi-intermittent states certainly overcome the constraints of binary classifications of traditional Shewhart type control charts when fuzzy data inevitably appear in practical manufacturing processes; hence, it fulfils the current literature of control charts.

This paper describes an on going research by the authors who are seeking methods to integrate an earlier developed manufacturing process modeling method in the virtual manufacturing (VM) technology. The primary aim is to attach special manufacturability evaluation to modeling of mechanical products. A more or less generic process model is generated for a cluster of manufacturing tasks then manufacturing process entities are created by evaluation of the manufacturing process model using part, form feature, production environment, production planning and cost information. Petri net manufacturing process model representation is used. It has proved to be a versatile virtual manufacturing tool in manufacturing process planning, evaluation of manufacturability and in manufacturing environment design. A process model evaluation procedure is emphasized in the course of which feasibility and resource requirements of the manufacturing are revealed. Firstly, techniques of evaluation of manufacturability, the applied approach to integration of shape and manufacturing process modeling and the related virtual manufacturing concepts are introduced. The process model is outlined and earlier relevant publications of the authors are cited. Then problems and solutions at evaluating of process model in close connection with design and environmental changes are detailed. Following this, integration of the method into a comprehensive virtual manufacturing concept is explained. Finally, main issues and future research plans are concluded.

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d13128e120">Laser powder bed fusion (LPBF), a typical metal additive manufacturing (MAM) process, generates large thermal gradients and rapid cooling rates, making it challenging to fully understand the relationship between nano-scaled solidification microstructure and process parameters. This study aims to uncover the nano-scaled solidification microstructure characteristics of 316L stainless steel (SS) produced through LPBF. A deeper understanding of microstructural development is crucial for fabricating high-performance materials by effectively controlling the AM process. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d13128e122"> <b>1. Introduction</b> <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d13128e127">Laser powder bed fusion (LPBF) is currently the most versatile metal additive manufacturing (MAM) process, enabling the production of complex parts without the design limitations of traditional manufacturing methods. The rapid and repeated thermal cycles create intense thermal gradients, leading to metastable chemical, structural, and mechanical states, resulting in metallurgical defects that compromise material properties. Extensive research of LPBF-processed 316L SS has been conducted to improve the mechanical properties and highlight the need to correlate different microstructural features with the manufacturing process. Most studies have focused on characterizing microstructures from macro to sub-micrometer scales, with few investigating features at the nano or atomic levels. Therefore, this study aims to explore these nano/atomic scale microstructural features to better understand the relationship between solidification microstructure and process parameters. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d13128e129"> <b>2. Experiment</b> <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d13128e134">The nominal composition of the 316L SS powder was 18Cr-14Ni-2.5Mo-0.03C (wt.%), and the powder size was under 53µm. LPBF fabrication was conducted using a 3D printer (EOSM290, EOS GmbH, Germany) equipped with a Yb-fiber laser by the “X-scan strategy,” i.e., the laser beam was scanned bidirectionally along the X-axis without rotation.

Fuzzy regression is a commonly used approach for modeling manufacturing processes in which the availability of experimental data is limited. Fuzzy regression can address fuzzy nature of experimental data in which fuzziness is not avoidable while carrying experiments. However, fuzzy regression can only address linearity in manufacturing process systems, but nonlinearity, which is unavoidable in the process, cannot be addressed. In this paper, an evolutionary fuzzy regression which integrates the mechanism of a fuzzy regression and genetic programming is proposed to generate manufacturing process models. It intends to overcome the deficiency of the fuzzy regression, which cannot address nonlinearities in manufacturing processes. The evolutionary fuzzy regression uses genetic programming to generate the structural form of the manufacturing process model based on tree representation which can address both linearity and nonlinearities in manufacturing processes. Then it uses a fuzzy regression to determine outliers in experimental data sets. By using experimental data excluding the outliers, the fuzzy regression can determine fuzzy coefficients which indicate the contribution and fuzziness of each term in the structural form of the manufacturing process model. To evaluate the effectiveness of the evolutionary fuzzy regression, a case study regarding modeling of epoxy dispensing process is carried out.

System reliability of a manufacturing process should address effects of both the manufacturing system (MS) component reliability, and the product quality. In a multi-station manufacturing process (MMP), the degradation of MS components at an upstream station can cause the deterioration of the downstream product quality. At the same time, the system component reliability can be affected by the deterioration of the incoming product quality of upstream stations. This kind of quality & reliability interaction characteristics can be observed in many manufacturing processes such as machining, assembly, and stamping. However, there is no available model to describe this complex relationship between product quality, and MS component reliability. This paper, considering the unique complex characteristics of MMP, proposes a new concept of quality & reliability chain (QR-Chain) effect to describe the complex propagation relationship of the interaction between MS component reliability, and product quality across all stations. Based on this, a general QR-chain model for MMP is proposed to integrate the product quality with the MS component reliability information for system reliability analysis. For evaluation of system reliability, both the exact analytic solution, and a simpler upper bound solution are provided. The upper bound is proved to be equal to the exact solution if the product quality does not have self-improvement, which is generally true in many MMP. Therefore, the developed QR-chain model, and its upper bound solution can be applied to many MMP.

The present study investigates the dynamic response of single-cylinder reciprocating engines. In particular, simplified models of the crank-connecting rod mechanism are examined, which take into the flexibility of the crankshaft. Crankshaft, connecting rod and piston are major parts of the crank-connecting rod mechanism, and their size, mass, centroid location parameters are required precisely in manufacturing process. However, manufacturing error cannot be avoided completely in practical manufacturing process, and it would lead to different vibration performance of engine. Therefore, understanding the effect of manufacturing error on the vibration performance of engine is the aim of this paper. In order to obtain dynamic characteristics of engine, both rigid and flexible body dynamics simulation were done by using software ADAMS.

Industrial use of natural resources are increasing at an alarming rate. Engineering and decision support tools are needed for analyzing and curbing industrial consumption of resources. Further, assessment methods to measure and indicate continuous improvement are also needed. Modeling individual manufacturing processes facilitates the generation of quantifiable evidence that improvements are being made. Such a modeling approach is developed and demonstrated in this paper to characterize sustainability performance of two metals inspection processes: magnetic particle inspection for ferromagnetic alloys and penetrant inspection for non-ferromagnetic alloys. Individual unit manufacturing process (UMP) models were developed by observing the inspection practices at an aircraft component manufacturer, and a mathematical basis for comparison with other inspection processes was identified. The paper further demonstrates the aggregation of performance metrics from all UMPs across a manufacturing process flow thus providing a basis for generating detailed sustainability performance assessments of manufactured products. By developing and documenting a comprehensive set of UMP models, more complete knowledge of manufacturing processes can be gained by industry practitioners, leading to continuous improvement of sustainability performance.

Over the past decade, several efforts have characterized manufacturing processes from a sustainability perspective. In addition, frameworks, methodologies, and standards development for characterizing and linking unit manufacturing process (UMP) models to construct manufacturing system models for supporting sustainability assessment have been pursued. In this paper these research efforts are first briefly reviewed, and then, ASTM standards derived from this work are described and built upon. The contribution of this research is to demonstrate how more formalization of these prior efforts will facilitate systematic reuse of developed models by encapsulating different aspects of complex processes into reusable building blocks. The research proposes a methodology to define template UMP information models, which can further be abstracted and customized to represent an application-specific, upgraded manufacturing process. The methodology developed is based on the ASTM standards of characterizing manufacturing process for sustainability characterization. The approach is demonstrated for analyzing manual and computer numerically controlled (CNC) machining processes.

The variation and uncertainty existed in every process and considered as an essential element in precise, accurate and high-quality manufacturing, especially in the Industry 4.0 era. Manufacturing quality and reliability is affected by variations and uncertainties in different process parameters and influencing factors. The variations and uncertainties are considered very critical in measurement and monitoring process, i.e., prediction or prognostics. These variations and subsequent uncertainties are needed to be identified, quantified, analyzed and controlled. This paper presents an approach to uncertainty measurement and analysis in manufacturing. The key characteristic parameter is identified which is critical for final product quality and reliability. To determine the uncertainty in the specific parameter, the uncertainty factors of both the manufacturing process and measurement process are determined. The combined uncertainty is determined by considering the influencing factors both in manufacturing and measurement process. The relation of each factor to the key parameter is analyzed and the sensitivity coefficient is determined mathematically or experimentally. The combined and expanded uncertainty is determined considering all related factors. The proposed approach has been applied to an additive manufacturing technique, and useful results are achieved.

Fatigue data usually display substantial scatter. The goal of this paper is to demonstrate how simulated variation in surface treatment processing parameters and material properties affect the predicted fatigue life (mean and scatter) of a component. This is achieved by applying robust design principles to fatigue life evaluation methods, using shot peening as the representative manufacturing process for this study. Analyzing changes in the appropriate fatigue performance quality characteristic due to variations in the process parameters and material properties will identify levels of the controllable process parameters which maximize the mean fatigue performance and minimize its scatter. The simulation predictions of this study are consistent with past experimental observations which show that compressive residual stress distributions tend to increase mean fatigue life and reduce its scatter for a component. Our results extend these observations by relating the increase in mean life and the reduction in scatter to the controllable manufacturing and design parameters. In addition, the intermediate measure of compressive zone depth is identified as a possible off-line production quality check that relates directly to the component fatigue performance (mean and scatter), as well as an aid to the designer to identify an appropriate surface treatment process. This study serves as an initial step in the development of a generalized methodology that can aid engineers with design for robust fatigue performance for other manufacturing processes.