The University of Luxembourg has recently launched its learning factory, the “Lean Manufacturing Laboratory”. With the help of this manual assembly line, students gain valuable insights in the ...operation of a manufacturing line as well as in buffer, waste and congestion management.
Currently, one of the main research topics at the University of Luxembourg in the field of Lean Management is the further development of the method Value Stream Management (VSM). The application of VSM in the “Lean Manufacturing Laboratory” with a projected focus on industry and service sectors reveals the need for a standardized VSM approach. Thus, one of the research objectives is the development of a common VSM method accompanied by standardized software and process interfaces to ensure robust product and information flows within a company and also throughout supply chains.
On the way towards a VSM method as standard, existing VSM approaches have to be investigated and validated. By a detailed comparison of existing VSM approaches, all necessary fields of action for the development of a standardized Value Stream Management approach are shown
The usage of Augmented Reality (AR) in industrial and modern manufacturing is more and more growing since the fourth industrial revolution. Using AR boost the digitization of the industrial ...production lines, gain time and money and improve maintenance tasks as well as the human-machine interaction. This paper is a literature review of the use of AR in industries including the use cases in different type of application such as design, simulation, maintenance, remote assistance, human-robot interaction and robot programming.
Artificial Intelligence (AI) based algorithms are being used increasingly to support industrial robots in the automation of assembly processes. The objective of this work is to detect bores of ...different geometry, appearance, and inner structure for automating a high-mix low-volume assembly line. Two most widely used AI algorithms namely, Single Shot Detection (SSD) and You Only Look Once (YOLO) have been used to perform the bore detection process. The results obtained by these algorithms have been compared with the conventional detection algorithm (Gradient filter) using the standard metrics used for evaluating the performance of the detection algorithms. The obtained results demonstrate the efficiency and robustness of AI-based algorithms for the detection as they exhibit better performance than the conventional detection method.
Cross-enterprise value stream assessment Oberhausen, Christof; Plapper, Peter
Journal of advances in management research,
05/2017, Letnik:
14, Številka:
2
Journal Article
Recenzirano
Purpose
In most cases, the conventional assessment of value streams is based on key performance indicators (KPIs) like the share of added value, the degree of flow or a comprehensive lead time ...analysis. To evaluate cross-enterprise value streams of manufacturing, business or service processes in detail, a holistic methodology is needed. The paper aims to discuss this issue.
Design/methodology/approach
In this research paper, the assessment of value streams within complex cross-company networks is described. After a presentation of relevant KPIs in the fields of value stream management (VSM) and supply chain management (SCM), an approach for a cross-enterprise evaluation of value streams on different levels of detail is shown. In addition, the use of an absolute VSM evaluation, in contrast to a relative VSM assessment, is examined.
Findings
Based on a uniform and well-balanced set of KPIs and other VSM and SCM parameters, a performance assessment on different levels of value stream detail is enabled. Further investigations reveal the advantages of a relative compared with an absolute VSM assessment.
Research limitations/implications
In addition to a comprehensive overview of existing KPIs for a value stream assessment beyond company borders, a holistic and multi-level VSM approach is presented in this paper. In contrast to existing VSM approaches, the described method allows an evaluation and subsequent improvement of value streams within supply chain networks. Up to now, the presented approach for the assessment of cross-enterprise value streams has only been tested in specific industrial environments. In future, the proposed methodology shall also be validated for other process types like business, service or further manufacturing processes.
Practical implications
The described cross-company performance measurement approach shows a high practical relevance for organizations operating in supply chain networks. Due to the integrated use of different VSM parameters, the evaluation of highly interconnected value streams across corporate boundaries is facilitated. By means of a case study, the proposed methodology is validated under real industry conditions and proves its practical applicability.
Originality/value
One of the novel features of this research is the extension of the traditional VSM method with respect to a relative evaluation of value streams based on a set of significant KPIs. In addition, the allocation of these KPIs to different value stream layers and categories leads to an innovative approach for a multi-level assessment according to the needs of the specific VSM application, e.g. a more standardized use of VSM in complex supply chain networks.
As far as complex contact-based manufacturing tasks are concerned, humans outperform machines. Indeed, conventionally controlled robotic manipulators are limited to basic applications in close to ...ideal circumstances. However, tedious work in hazardous environments, make some tasks unsuitable for humans. Therefore, the interest in expanding the application-areas of robots arose. This paper employs a bottom-up approach to develop robust and adaptive learning algorithms for trajectory tracking: position and torque control in the presence of uncertainties and switching constraints. The robotic manipulators mimicking the human behavior based on bio-inspired algorithms, take advantage of their know-how. Simulations and experiments validate the concept-performance.
Laser-assisted metal–polymer joining (LAMP) is a novel assembly process for the development of miniaturized joints in hybrid lightweight products. This work adopts a design of experiments (DoE) ...approach to investigate the influence of several laser welding parameters on the strength and quality of titanium alloy (Ti-6Al-4V)–polyamide (PA6.6) assembly. Significant parameters were highlighted using the Plackett Burmann design, and process window was outlined using the Response Surface Method (RSM). A statistically reliable mathematical model was generated to describe the relation between highlighted welding parameters and joint strength. The analysis of variance (ANOVA) method was implemented to identify significant parametric interactions. Results show the prominence of focal position and laser power, as well as significant interaction between laser power and beam speed, on the joint strength. The evolution of weld defects (bubbles, excessive penetration, flashing, titanium coloring, weld pool cavities, and welding-induced deflection) along the process window was investigated using optical microscopy. The resulted deflection in titanium was quantified, and its relationship with welding parameters was mathematically modeled. Robust process window was outlined to maintain insignificant deflection in the welded joints. Results showed that the growth of weld defects correlates with a decline in joint strength. Optimal parameters demonstrated a defect-free joint, maximizing joint strength.
Robotic drilling has advantages over traditional computer numerical control machines due to its flexibility and dexterity and the potential for rapid production and process automation. The dexterity ...and reach of the robotic drill end-effector enable the efficient drilling of large composite components, such as aircraft wing structures. Due to the anisotropy and inhomogeneity of fibre-reinforced polymer composite materials, drilling remains a challenging task. Inspection of the drilled hole is required at the end of the process to ensure that the final product is free from defects. Typically, such inspections require the parts to be transferred to a dedicated inspection station, which is a time-consuming non-value-added task and impractical for large components. In the interest of an efficient and sustainable manufacturing process, this work proposes a hybrid classification model implemented with a robotic drilling system to investigate the quality of drilled holes in situ. The classifier is trained and tested with a random selection of drilled holes, and the most accurate classifier is implemented. The selected classifier returns 90% overall prediction accuracy on unseen drilled holes. This machine learning–based approach, using a convolutional neural network and support vector machine classifier, can significantly improve inspection reliability while reducing production time for drilled composite components. This is the first study that demonstrates a hole quality assessment technique for robotic drilling of composite material in situ at scale.
Variations in quantity, quality and time availability of input materials pose a major risk to circular supply chains (CSC) and require new models for creating and evaluating adaptive and resilient ...CSC in the circular economy (CE). This can be achieved through consistent modelling of the overarching relationship between resource input- and output streams, without neglecting the associated risks.
The model proposed below consists of five components based on five resilience requirements for supply-chains (SCs). It provides a data-based recommended course of action for managers with a low entry-barrier. It consists of a CSC visualization, safety stock calculation, risk monitoring for each SC node, reporting logic, and a measurement catalogue. The inspiration for this model came from an innovative case study (“Zirkelmesser”) in the metal processing industry, where secondary products and materials are used to produce new products. Here, the problem of maintaining the resource supply arose and led to resilience issues. The mentioned case study serves as an application example for the model application and contributes to making emerging circular supply chains predictable and more controllable, thus increasing their resilience.
Position uncertainty is inevitable in many force-guided robotic assembly tasks. Such uncertainty can cause a significant delay, extra energy expenditure, and may even results in detriments to the ...mated parts or the robot itself. This article suggests a strategy for identifying the accurate hole position in force-guided robotic peg-in-hole assembly tasks through employing only the captured wrench (the Cartesian forces and torques) signals of the manipulated. In the framework of using the Contact-State (CS) modeling for such robotic tasks, the identification of the hole position is realized through detecting the CS that corresponds for the phase of the peg-on-hole, that is the phase in which the peg is located precisely on the hole. Expectation Maximization-based Gaussian Mixtures Model (EM-GMM) CS modeling scheme is employed in detecting the CS corresponding for the peg-on-hole phase. Only the wrench signals are used in modeling and detecting the phases of the assembly process. The considered peg-in-hole assembly process starts from free space and as soon as the peg touches the environment with missing the hole, a spiral search path is followed that would survey the whole environment surface. When the CS of the peg-on-hole is detected, the hole position is identified. Experiments are conducted on a KUKA Lightweight Robot (LWR) doing typical peg-in-hole assembly tasks. Multiple hole positions are considered and excellent performance of the proposed identification strategy is shown.
PDF
