The integration of Intelligent Welding Systems (IWS) in smart manufacturing leverages advancements in sensors, robotics, and artificial intelligence to optimize welding processes. However, in industry practice, we still face challenges such as sufficient data is not available for every manufacturing task, the costs associated with welding data annotation quality, and the risk of knowledge forgetting during the continual welding process. To tackle these issues, we developed an Adaptive Domain-Enhanced Transfer Learning (ADETL) framework that integrates self-supervised and continual learning strategies. This framework is adept at using incremental and unlabeled data for pre-training, in which we analyze the parameter space, loss landscape, and make the model understand the behaviour of knowledge transfer from diverse source domains. The ADETL framework improves the performance of defect classification, offering a promising solution to the challenges inherent in automatic, continuous welding operations.

Geometric and dimensional variations in objects are caused by inevitable uncertainties in manufacturing processes and often lead to product quality challenges. Failing to model the effect of object shape errors, i.e., geometric and dimensional errors of parts, early during the design phase inhibits the ability to predict such quality challenges. This consequently leads to expensive design changes after freezing of design. State-of-art methodologies for modelling and simulating object shape error have limited defect fidelity, data versatility, and designer centricity that prevent their effective application during the early design phase. To overcome these limitations, this paper presents a novel Morphing Gaussian Random Field (MGRF) methodology for object shape error modelling and simulation. The MGRF methodology models the spatial correlation in the deviations of the part from its nominal design using Gaussian Random Fields and then, utilises the modelled spatial correlations to generate non-ideal parts by conditional simulations. The MGRF methodology has (i) high defect fidelity enabling it to simulate various part defects including local and global deformations, and technological patterns; (ii) high data versatility allowing it to simulate non-ideal parts under the constraint of limited data availability and to utilise historical non-ideal part data of similar parts; (iii) designer centric capabilities such as performing ‘what if?’ analysis of defects of practical importance; and; (iv) the ability to generate non-ideal parts conforming to statistical form tolerance specification without additional modelling effort. The aforementioned characteristics enable the MGRF methodology to accurately model and simulate the effect of object shape variations on product quality during the early design phase. Practical applications of the developed MGRF methodology and its advantages are demonstrated using sport-utility-vehicle door parts and compared against state-of-art methodologies.

This article aims to resolve the three major issues of fault tolerant control (FTC) for robot manipulators: 1) the faster response, lower tracking errors, lower chattering, and higher robustness of the FTC, 2) the requirement of partial or full knowledge of robot dynamics for the design of model-based FTC, and 3) the global fixed-time convergence of the system. First, a fixed-time controller based on a backstepping control is designed and its disadvantages are analyzed. Then, an adaptive fuzzy backstepping control is developed to enhance the tracking performance of the system. The proposed approach does not require the full prior knowledge of robot dynamic model, thus facilitating implementation of the controller in practical applications. In addition, the tracking errors of the system will be practically convergent within a fixed-time, which provides additional system information in advance. The fixed time convergence of the system is mathematically proved and the performance of the system is demonstrated for FTC of a PUMA560 robot.

The paper proposes a novel approach, Object Shape Error Correction (OSEC), to determine corrective action in order to mitigate root cause(s) (RCs) of dimensional and geometric product shape errors. It leverages Deep Deterministic Policy Gradient (DDPG) algorithm to learn optimal process parameters update policies based on high dimensional state estimates of multi-station assembly systems (MAS). These policies can be interpreted in engineering terms as sequential corrective adjustments of process parameters that are necessary to mitigate RCs of product shape errors. The approach has the capability to estimate adjustments of process parameters related to fixturing and joining while simultaneously accounting for (i) RC uncertainty estimation, (ii) Key Performance Indicator (KPI) improvement, (iii) MAS design architecture; and, (iv) MAS inherent stochasticity. In addition, the OSEC methodology leverages a reward function parameterized by user interpretable functional coefficients for optimal tradeoff involving various corrections requirements. Benchmarking using an industrial, automotive cross-member assembly system demonstrates a 40% increase in the effectiveness of corrective actions when compared to current approaches.

Compliant, nonrigid parts are widely used in many industries today. Existing variation simulation analysis on the manufacturing part focuses on orientation and position deviation with part shape errors being largely omitted. This is a valid approach for the rigid part, but it is unrealistic and can be problematic for the compliant part. In this study, a new methodology has been introduced to compliant assembly early phase design to generate various probable variated manufacturing parts that conform to predefined tolerance specification or meet the certain industrial requirement. The proposed method is based upon the novel idea of a parametric space envelope, a purpose-designed variation tool constructed from parametric curves. Variation of embedded manufacturing part is linked to and controlled by a compact set of envelope’s boundary control points. Part variation instances are generated by simulating the control points’ movement in a systematic and efficient way. Importantly, simulated variations can be visualized in 3-D virtual space to provide user insight into part variation. The proposed methodology can help identify assembly high-risk regions, select proper fixtures, guide assembly engineering changes, and optimize assembly operations. An industrial case study on a deformable vehicle door hinge plate is presented to illustrate the methodology.Note to Practitioners—This article is motivated by two acute problems encountered in geometric variation simulation analysis for compliant parts in the early design stage. First, form errors of the nonrigid part are not fully captured in existing methods. Second, variation visualization can significantly enhance understanding of the geometric variation effects, but it is difficult to achieve under existing approaches. Inspired by the idea of a parametric space envelope, this article proposes a new methodology by building a variation tool to aid the task. Under the proposal, the geometric variation of the compliant part is indirectly modeled through the constructed variation tool. This indirect modeling enables capturing intrapart interactions that are the major source of the inaccuracy of existing methods. The simulated geometric variation can be visualized through the variation tool. In addition, the developed method does not rely on historical manufacturing experience. This can be valuable for early stage designer when such production information is not available or expensive to get. Furthermore, the proposed method can be integrated into existing computer-aided design (CAD), computer-aided manufacturing (CAM), and computer-aided engineering (CAE) systems to improve overall design quality and reduce reliance on multiple physical prototyping.

This article proposes a novel object shape error response (OSER) approach to estimate the dimensional and geometric variation of assembled products and then, relate these to process parameters, which can be interpreted as root causes (RC) of the object shape defects. The OSER approach leverages Bayesian 3-D convolutional neural networks integrated with computer-aided engineering simulations for RC isolation. Compared with the existing methods, the proposed approach: 1) addresses a novel problem of applying deep learning for object shape error identification instead of object detection; 2) overcomes fundamental performance limitations of current linear approaches for RC analysis (RCA) of assembly systems that cannot be used on point cloud data; and 3) provides capabilities for unsolved challenges such as ill-conditioning, fault-multiplicity, RC prediction with uncertainty quantification, and learning at design phase when no measurement data are available. Comprehensive benchmarking with existing machine learning models demonstrates superior performance with R2= 0.98 and MAE = 0.05 mm, thus improving RCA capabilities by 29%.

The paper proposes a novel Object Shape Error Response (OSER) approach to estimate the dimensional and geometric variation of assembled products and then, relate, these to assembly process parameters, which can be interpreted as root causes (RC) of the object shape defects caused by the assembly system. The OSER approach leverages Bayesian 3D-Convolutional Neural Networks for RC isolation. Compared with the existing methods, the proposed approach (i) addresses a novel problem of applying deep learning for object shape error identification instead of nominal shape identification (object detection); and, (ii) overcome significant limitations in current approaches for Root Cause Analysis (RCA) of assembly systems which are frequently ill-conditioned (collinear fault patterns) with high fault-multiplicity (frequently encountered in 6-sigma strategy) while quantifying the prediction uncertainty. The verification of the proposed methodology is conducted based on the CAE model of a multi-stage assembly process for automotive SUV door made of compliant parts. The results demonstrate performance with R-Squared (\pmbR2)=0.98 and Mean Absolute Error (MAE)=0.05 mm, thus improving RCA capabilities by 29% as compared to state-of-the-art methods.

Tolerance is an essential part of design and manufacturing. It plays a key role in product quality and manufacturing costs. Understanding and controlling production variations on key geometric features can provide firms with a competitive edge. A model to link production variation to tolerance is highly desirable but difficult to build, especially for deformable parts with complex surfaces. Inspired by an innovative idea of volumetric space envelope (constructed from a base parametric curve), this paper proposes a novel spatial tolerance model. In this proposal, a volumetric envelope is superimposed onto the target manufacturing part, whose deformation and deviation (during manufacturing or assembly) are viewed as spatial variation, and this variation is modeled and linked to movements of envelope's control points. This unique model design bypasses direct modeling of complex intrapart interactions, which is nonlinear in general and a major source of inaccuracy and low efficiency of many existing methods. The adopted indirect modeling brings many benefits. It can handle complex shapes and surfaces, and is able to take into account form errors. Also, it is capable of modeling both global and local variations observed in many practical cases. The new method is illustrated and verified through an example on a deformable vehicle door hinge plate. The proposed model shows application potential in every major stage of production, and makes possible to build a coherent cross-production life-cycle tolerancing framework from the early stage design, to manufacturing, to postproduction quality inspection.

This study develops a fault estimation and accommodation scheme for the image-based visual servoing system to eliminate the effects of the faults due to the image feature extraction task, which is named as bias virtual sensor fault. First, a bias virtual sensor fault in visual servoing is declared. Then, fault diagnosis, which includes fault detection, isolation, and estimation, is designed based on the means of particle filter. Finally, a fault accommodation law is developed based on the information obtained from the fault estimation to compensate for the effects of the fault in the system. The proposed fault estimation and accommodation is verified through simulation and experimental studies, and the results show that the system can estimate and eliminate the unknown fault effects effectively.

Recently, the modeling of variation propagation in complex multistage manufacturing processes has drawn significant attention. In this paper, a linear model is developed to describe the dimensional variation propagation of machining processes through kinematic analysis of the relationships among fixture error, datum error, machine geometric error, and the dimensional quality of the product. The developed modeling technique can handle general fixture layouts rather than being limited to a 3-2-1 layout case. The dimensional error accumulation and transformation within the multistage process are quantitatively described in this model. A systematic procedure to build the model is presented and validated. This model has great potential to be applied toward fault diagnosis and process design evaluation for complex machining processes. Note to Practitioners-Variation reduction is essential to improve process efficiency and product quality in order to gain a competitive advantage in manufacturing. Unfortunately, variation reduction presents difficult challenges, particularly for large-scale modern manufacturing processes. Due to the increasing complexity of products, modern manufacturing processes often involve multiple stations or operations. For example, multiple setups and operations are often needed in machining processes to finish the final product. When the workpiece passes through multiple stages, machining errors at each stage will be accumulated onto the workpiece and could further influence the subsequent operations. The variation accumulation and propagation pose significant challenges to final product variation analysis and reduction. This paper focuses on a systematic technique for the modeling of dimensional variation propagation in multistage machining processes. The relationship between typical process faults and product quality characteristics are established through a kinematics analysis. One salient feature of the proposed technique is that the interactions among different operations with general fixture layouts are captured systematically through the modeling of setup errors. This model has great potential to be applied to fault diagnosis and process design evaluation for a complex machining process

Manufacturing systems are inherently imperfect both statically and dynamically. Tolerance and maintenance design are two major tools to address the static and dynamic imperfection of manufacturing processes (i.e., inherent process imperfection and tooling deterioration, respectively). Yet, traditionally, tolerance and maintenance designs have been studied separately to address these two critical areas of manufacturing systems. This paper presents an integrated framework of tolerance and maintenance design for multistation manufacturing processes. Two nonlinear optimization problems are formulated to minimize the overall average production cost in the long run, which includes the tolerance cost of tooling fabrication, maintenance cost, and the overall loss of quality (as a part of the objective function or as a constraint function). The proposed methodology is illustrated, analyzed, and further discussed in the context of a multistation automotive body assembly process. Extensive numerical analyses are conducted to demonstrate the efficiency of the developed methodology. Given various cost components and time horizons, the integrated design scheme is compared with traditional design schemes in terms of cost efficiency, offering new insights into the interrelation between manufacturing process maintenance and tolerancing in the context of the product life cycle. Note to Practitioners-With intensified competition as a result of economic globalization, quality and cost have become crucial factors to the success of any manufacturing industry. Decisions in the process design phase, such as process tolerance assignment and maintenance planning, play a substantial role for overall manufacturing quality and costs. Tolerance of process variables determines the inherent variation level of a manufacturing process. Preventive maintenance oversees and controls process degradation and its resulting deterioration on product quality. Significant tooling and operational costs result from both tolerancing and maintenance activities. Traditionally, tolerancing and maintenance decision-making have been studied separately. Tolerancing was mainly conducted during the design stage; while maintenance policy was often determined after a manufacturing system was designed and installed. However, tolerancing of process variables and maintenance decision-making policy are interconnected in modern manufacturing systems. Intuitively, tight initial tolerances specified on process variables are able to reduce the frequency of conducting maintenance during production, since the process can accommodate more deterioration to reduce maintenance cost; but they take a toll on tolerance cost. On the other hand, loose initial tolerances specified on process variables can lower design cost but increase the frequency of maintenance during production. Hence, there is a critical need to strike a balance between the tolerance cost of tooling fabrication and the maintenance cost of tooling replacement. This paper presents a new framework to integrate tolerance design and maintenance planning for multistation manufacturing processes. Optimization problems are formulated to minimize the overall production costs including tooling costs, maintenance costs, and quality loss. The proposed framework is illustrated in the context of automotive body assembly processes. When compared to other separated designs, this integrated design methodology leads to more desirable system performance with a significant reduction in production cost

Customer feedback in the form of warranty/field performance is an important direct indicator of quality and robustness of a product. Linking warranty information to manufacturing measurements can identify key design and process variables that are related to warranty failures. Warranty data have been traditionally used in reliability studies to determine failure distributions and warranty cost. This paper proposes a novel fault region localization methodology to link warranty failures to manufacturing measurements (hence, to design and process parameters) for diagnosing warranty failures and to perform tolerance revaluation. The methodology consists of identifying relations between warranty failures and design/process variables using rough sets-based analysis on training data consisting of warranty information and manufacturing measurements. The methodology expands the rough set-based analysis by introducing parameters for inclusion of noise and uncertainty of warranty data classes. Based on the identified parameters related to the failure, a revaluation of the original tolerances can be performed to improve product robustness. The proposed methodology is illustrated using case studies of two warranty failures from the electronics industry. Note to Practitioners—Warranty failures are indicative of the performance and robustness of the product. Warranty failures, especially those that occur early (e.g., within six months after sale), can be caused by interactions between various design and process characteristics of the individual components of the product. Due to the large number of components and the interactions between them, it is difficult to identify all of these relations during design. Furthermore, it is difficult to replicate actual product usage in the field during the design stage. The methodology proposed in this paper integrates a product's warranty failure information with that of measurement data collected during manufacturing, to identify relevant design and process variables related to the failures. It also identifies the warranty fault region within the original design tolerance window for the parameters. This can help in avoiding warranty failure(s) through design changes and/or tolerance revaluation. The methodology was applied in the electronics and semiconductor industries.

This paper presents a methodology for optimal allocation of sensors in a multistation assembly process for the purpose of diagnosing in a timely manner variation sources that are responsible for product quality defects. A sensor system distributed in such a way can help manufacturers improve product quality while, at the same time, reducing process downtime. Traditional approaches in sensor optimization fall into two categories: multistation sensor allocation for the purpose of product inspection (rather than diagnosis); and allocation of sensors for the purpose of variation diagnosis but at a single measurement station. In our approach, sensing information from different measurement stations is integrated into a state-space model and the effectiveness of a distributed sensor system is quantified by a diagnosability index. This index is further studied in terms of variation transmissibility between stations as well as variation detectability at individual stations. Based on an understanding of the mechanism of variation propagation, we develop a backward-propagation strategy to determine the locations of measurement stations and the minimum number of sensors needed to achieve full diagnosability. An assembly example illustrates the methodology.