45 research outputs found
Variation Propagation in Multistage Machining Processes Using Dual Quaternions
No full textThe application of rigid transformations matrices in variation propagation has a long tradition in manufacturing community. However, the matrix-based modeling of variation propagation in multistage machining processes is complicated. Moreover, there is a need to improve the computational efficiency of manipulation of geometrical models for variation analysis purposes. This paper introduces the representation of rigid transformation by dual quaternions, which have a computational advantage and mathematical elegance compared to matrices. In comparison to a commercial tool, the implementation of the purposed method predicted parallelism with an average error of 4.3 % in a hypothetical two stage machining process. The proposed approach has a potential to be an alternative to matrix based rigid transformation practices in variation and tolerance analysis.QC 20200226</p
Model-Based Virtual Commissioning of a Manufacturing Cell Control System
No full textOne of the preferred options of manufacturing organizations to survive in today’s market is to automate, increase flexibility and configurability of manufacturing lines. Control systems and Programmable Logic Controller (PLC) programming are quintessentially important part of an automation system. However, PLC programming and debugging takes time and is error prone; consequently, there has being a growing need for quick development of PLC programs and inexpensive code verification and validation methods. To meet the needs, this thesis paper presents a method of virtual development of control system for a fully automated manufacturing cell. The cell has two robots and five other machines, which each machine operating modes are modeled in MATLAB environment and PLC code is generated from the developed models. The model and their associated PLC code are verified and validated in virtual environments. Step-by-step development, verification and validation approaches are presented and argued. Results show that few hours of modeling efforts can generate thousands of lines of code; hence this method is expected to significantly reduce time, development efforts and costs associated with verification and validation of PLC code. Keywords: PLC Code Generation, Simulink/Stateflow Model, Verification and Validation, Manufacturing Cell.Ett alternativ som föredras för tillverkning organisationer att överleva i dagens marknad är att automatisera, öka flexibiliteten och konFigurering av tillverkningslinjer. Kontrollsystem och Programmable Logic Controller (PLC) programmering är en grundlägande viktig del av ett automatiseringssystem. Dock tar PLC-programmering och debugging tid och är felbenäget, och därför har det funnits ett växande behov av snabb utveckling av PLC-program, billiga kodverifiering och valideringsmetoder. För att möta behoven, presenterar denna avhandling papper en metod för virtuell utveckling av styrsystem för en helt automatiserad tillverkning cell. Cellen har två robotar och fem andra maskiner, som varje maskin driftlägen modelleras i MATLAB miljö och PLC-kod genereras från de utvecklade modellerna. Modellen och tillhörande PLC-kod verifieras och valideras i virtuella miljöer. Steg-för-steg utveckling, verifiering och validering metoder presenteras och argumenteras. Resultaten visar att några timmars modelleringsinsatser kan generera tusentals rader kod, och därmed förväntas av denna metod att minska tiden, utvecklingsinsatser och kostnader förknippade med verifiering och validering av PLC-kod. Nyckelord: PLC Code Generation, Simulink/ Stateflow Modell, Verifiering och validering, tillverkning Cel
Part Quality Prediction and Variation Reduction in Multistage Machining Processes Based on Skin Model Shapes
No full textAll manufacturing processes inevitably induce variations into manufactured parts that may result in nonconformance. Nonconforming parts incur costs due to the additional process required for rework or scrap loss. Hence, methodical efforts to reduce these variations are necessary for competitive manufacturing. To achieve this, effective variation reduction strategies have to be in place. In a multistage machining context, this could mean robust, rapid, and accurate approaches for representation and prediction of variations, change detection, variations source identification, and compensation. Moreover, the approaches used should be capable of handling all forms of errors contributing to the propagation of variations and nonconformance. Existing part variation and variation propagation analysis methods for multistage machining are limited to orientation and position errors, neglecting form errors. Form errors can be captured by utilizing the concept of Skin Models Shapes (SMSs). The application of SMSs for multistage machining and variation reduction strategies has been limited and not established yet. This thesis contributes to developing and demonstrating the use of SMSs for part quality prediction and variation reduction in multistage machining processes. The specific contribution of the thesis can be summarized as (i) the derivations of variation propagation models using dual quaternions; (ii) part quality prediction considering fixtures with locating surfaces, 3-2-1, and N-2-1 (N>3) locators; (iii) Octrees based method for performing statistical shape analysis; (iv) change and anomaly detection using machine learning classifiers; (v) variation source identification using pattern matching technique; (vi) and estimation of variation compensation values using dual quaternions.Alla tillverkningsprocesser orsaker oundvikligen variationer på den tillverkade detaljen. Detta medför kostnader i form av kassationer eller att artikeln måste ombearbetas. För att minska bearbetningsvariationerna, erfordras ett metodiskt arbetssätt för att kunna erhålla en konkurrenskraftig tillverkning. För att uppnå detta måste det finnas effektiva strategier så att variationerna kan reduceras till ett minimum. I en flerstegsbearbetningskontext måste robusta och exakta metoder finnas, så att detektering av avvikelser och variationer kan korrigeras och kompenseras. Befintliga analysmetoder som idag används för att behandla spridningsvariation av form och geometrifel i flerstegsbearbetningsoperationer, är begränsade till orienterings- och positionsfel, och behandlar inte formfel. Formfel kan behandlas med Skin Model Shapes (SMSs) konceptet. Tillämpning av SMSs för flerstegsbearbetning har hitintills varit begränsad, och metoder och strategier för reduktion av variationerna, är inte färdigutvecklat. Bara ett fåtal forskningsarbeten rapporterar studier inom området. Avhandling bidrar till att skapa ny kunskap och utveckla användningen av SMSs för och flerstegsbearbetningsprocesser och kan sammanfattas som: i. härledning till variationerna i propageringsmodeller med dual quaternions, ii. variation förutsägelse med beaktande av fixturer med lokaliseringsytor, 3-2-1 och N-2-1 (N> 3) lokalisatorer, iii. Octree-baserad metod för att utföra statistisk formanalys. iv. detektering och analys av förändringar och anomalier med hjälp av maskininlärning klassificering, v. identifiering av variationskällor med hjälp av mönstermatchningsteknik, vi. uppskattning av variationskompensationsvärden med dual quaternions
Variation compensation in machining processes using dual quaternions
No full textThe classical variation compensation methods that apply variation modeling are often based on homogenous transformation matrices and coordinate systems. However, the mathematical models are complicated, require large matrices and do not consider form errors. This paper presents a reformulation of the variation compensation techniques using dual quaternions. The compensation values are obtained from the difference between locators’ initial positions and their projected points on the part’s datum features, whose nominal machining feature is aligned with the toolpath plane. The proposed approach, besides its capability to include form errors, provides the same result with the prediction made using a classical method, while maintaining the mathematical conciseness.QC 20201130</p
Variation propagation modelling in multistage machining processes using dual quaternions
No full textVariation propagation models play an important role in part quality prediction, variation source identification, and variation compensation in multistage manufacturing processes. These models often use homogenous transformation matrix, differential motion vector, and/or Jacobian matrix to represent and transform the part, tool and fixture coordinate systems and associated variations. However, the models end up with large matrices as the number features and functional element pairs increase. This work proposes a novel strategy for modelling ofvariation propagation in multistage machining processes using dual quaternions. The strategy includes representation of the fixture, part, and toolpath by dual quaternions, followed by projection locator points onto the features, which leads to a simplified model of a part-fixture assembly and machining. The proposed approach was validated against stream ofvariation models and experimental results reported in the literature. This paper aims to provide a new direction of research on variation propagation modelling ofmultistage manufacturing processes.QC 20201130</p
Part quality prediction in multistage machining processes with fixtures based on locating surfaces using dual quaternions
No full textThe mathematical modelling of variation propagation in multistage machining processes helps to perform a quick analysis and diagnosis of the processes. The models for part quality prediction, such as Stream of Variation, include homogeneous transformations of the vectorial representations of parts and fixtures. However, these prediction models are complex when considering fixtures with locating surfaces and the associated matrix size is large. Towards mitigating the mathematical complexity, dual quaternions are proposed in representing and transforming a virtual part and fixture. To achieve this, the primary feature datum is assembled to the primary locating surface, followed by sliding the part to secondary and tertiary locating surfaces by reducing the distance between the vertices of the part and the locating surface. The prediction following the proposed approach gave a result within 0.36 % of the prediction made using CAD/CAM models and maintained the largest matrix size of 9 by 8 for a part with 9 features.</p
A multilayer shallow learning approach to variation prediction and variation source identification in multistage machining processes
No full textVariation propagation modelling in multistage machining processes through use of analytical approaches has been widely investigated for the purposes of dimension prediction and variation source identification. Yet the variation prediction of complex features is non-trivial task tomodel mathematically.Moreover, the application ofthevariation propagation approaches and associated variation source identification techniques using SkinModel Shapes is unclear. This paper proposes amultilayer shallow neural network regression approach to predict geometrical deviations of parts given manufacturing errors. The neural network is trained on a simulated data, generated from machining simulation of a point cloud of a part. Further, given a point cloud data of a machined feature, the source of variation can be identified by optimally matching the deviation patterns of the actual surface with that of shallow neural network generated surface. To demonstrate the method, a two-stage machining process and a virtual part that has planar, cylindrical and torus features was considered. The geometric characteristics of machined features and the sources variation could be predicted at an error of 1% and 4.25%, respectively. This work extends the application of Skin Model Shapes in variation propagation analysis in multistage manufacturing.QC 20201130</p
Part Quality Prediction in Multistage Machining Processes with Fixtures Based on Locating Surfaces Using Dual Quaternions
No full textThe mathematical modelling of variation propagation in multistage machining processes helps to perform a quick analysis and diagnosis of the processes. The models for part quality prediction, such as Stream of Variation, include homogeneous transformations of the vectorial representations of parts and fixtures. However, these prediction models are complex when considering fixtures with locating surfaces and the associated matrix size is large. Towards mitigating the mathematical complexity, dual quaternions are proposed in representing and transforming a virtual part and fixture. To achieve this, the primary feature datum is assembled to the primary locating surface, followed by sliding the part to secondary and tertiary locating surfaces by reducing the distance between the vertices of the part and the locating surface. The prediction following the proposed approach gave a result within 0.36 % of the prediction made using CAD/CAM models and maintained the largest matrix size of 9 by 8 for a part with 9 features. </p
A multilayer shallow learning approach to variation prediction and variation source identification in multistage machining processes
No full textVariation propagation modelling in multistage machining processes through use of analytical approaches has been widely investigated for the purposes of dimension prediction and variation source identification. Yet the variation prediction of complex features is non-trivial task to model mathematically. Moreover, the application of the variation propagation approaches and associated variation source identification techniques using Skin Model Shapes is unclear. This paper proposes a multilayer shallow neural network regression approach to predict geometrical deviations of parts given manufacturing errors. The neural network is trained on a simulated data, generated from machining simulation of a point cloud of a part. Further, given a point cloud data of a machined feature, the source of variation can be identified by optimally matching the deviation patterns of the actual surface with that of shallow neural network generated surface. To demonstrate the method, a two-stage machining process and a virtual part that has planar, cylindrical and torus features was considered. The geometric characteristics of machined features and the sources variation could be predicted at an error of 1% and 4.25%, respectively. This work extends the application of Skin Model Shapes in variation propagation analysis in multistage manufacturing.</p
A multilayer shallow learning approach to variation prediction and variation source identification in multistage machining processes [Elektronisk resurs]
No full textVariation propagation modelling in multistage machining processes through use of analytical approaches has been widely investigated for the purposes of dimension prediction and variation source identification. Yet the variation prediction of complex features is non-trivial task tomodel mathematically.Moreover, the application ofthevariation propagation approaches and associated variation source identification techniques using SkinModel Shapes is unclear. This paper proposes amultilayer shallow neural network regression approach to predict geometrical deviations of parts given manufacturing errors. The neural network is trained on a simulated data, generated from machining simulation of a point cloud of a part. Further, given a point cloud data of a machined feature, the source of variation can be identified by optimally matching the deviation patterns of the actual surface with that of shallow neural network generated surface. To demonstrate the method, a two-stage machining process and a virtual part that has planar, cylindrical and torus features was considered. The geometric characteristics of machined features and the sources variation could be predicted at an error of 1% and 4.25%, respectively. This work extends the application of Skin Model Shapes in variation propagation analysis in multistage manufacturing.</p
