Design Model of an Ecosystem for Resilient and Sustainable Value Creation of SMEs in Single and Small Batch Production

Today's markets are increasingly dynamic, not only due to shorter product development times and fast changing customer requirements but also unforeseen events. Contemporary crises and wars disrupt entire supply chains and can have existential consequences for manufacturing companies. In these times of uncertainty, it is essential for SMEs to have a resilient business orientation while at the same time fulfil the sustainability aspects demanded by their stakeholders. This paper provides a design model for an ecosystem for a resilient and sustainable value creation of SMEs in single and small batch production to increase competitiveness and to gain a better response to market dynamics. The developed model comprises the elements of ecosystem strategy, configuration and coordination. An adequate partner matching and the underlying business model complement the approach. The model is intended to assist practitioners as a reference framework in developing and managing ecosystems for value creation

Classification of product data for a Digital Product Passport in the manufacturing industry

The European Commission set out the goal of carbon neutrality by 2050, which shall be achieved by fostering the twin transition - sustainability through digitalization. A keystone in this transition is the implementation of a prospering Circular Economy (CE). However, product information required to establish a flourishing CE is hardly available or even accessible. The Digital Product Passport (DPP) offers a solution to that problem but in the current discussion, two separate topics are focused on: its architecture and its application on batteries. The content of the DPP has not been an essential part of the discussion, although access to high-quality data about a product's state, composition and ecological footprint is required to enable sustainable decision-making. Therefore, this paper presents a classification of product data for circularity in the manufacturing industry to emphasize the discussion about the DPP's content. Developed through a systematic literature review combined with a case-study-research based on common operational information systems, the classification comprises three levels with 62 data points in four main categories: (1) Product information, (2) Utilization information, (3) Value chain information and (4) Sustainability information. In this paper, the potential content structure of a DPP is demonstrated for a use case in the machinery sector. The contribution to the science and operations community is twofold: Building a guideline for DPP developers that require scientific input from available real-world data points as well as motivating manufacturers to share the presented data points enabling a circular product information management

Additive Manufacturing Production Shops: A Requirements Analysis

Additive Manufacturing (AM) technologies become increasingly relevant for manufacturing companies. Despite having the highest share of AM applications, end-use parts are mostly used for spare or special parts and rarely within series applications. This paper addresses the challenge of practically implementing AM series production into industrial environments by means of a requirements analysis. It proposes a methodology on how to record, prioritize and meet requirements for AM production shops. Successful implementation demands understanding of the requirements for AM production shops from both a factory and an AM perspective. Quality Function Deployment (QFD) is chosen as a methodology for the requirements analysis. It offers a framework for structured collection and weighting of the requirements identified through expert interviews with AM users and system manufacturers. Subsequently, measures and a basic plan of action on how to implement AM series production into production shops are defined. The analysis reveals seven requirements for AM production shops within the categories spatial organization, process chains and flow systems. Most of them concern process chains, making these primary obstacles towards additive series production on the technical side. Substantial requirements are high process stability, fast process chains and the reduction of manual post-processing. Different advancements are necessary on the AM and the factory side. On the factory side, measures that form synergies to conventional manufacturing technologies, such as cross-usable quality assurance systems, are favorable. On the AM side, focus lies on the enhancement of physical and digital process chains. The results show that implementing AM production shops requires joint and interdisciplinary developments by AM users and system manufacturers. Further research and a larger sample are needed for validation as well as practical realization and advancement of the identified measures

Synthetic Data-Enhanced Deep Learning For Quality Control Of Automated Welding Processes

Automotive production systems are designed to produce large quantities in high quality and short throughput times and are therefore organized as line production. This places high quality requirements on the joining processes in automotive body shops, in which automated, robot-guided welding is a key process. The quality of these thermal joining processes depends on various physical and chemical influencing factors, whose interactions cannot be explicitly modelled. This leads to enormous quality assurance efforts in several quality control loops, which may include visual inspections, non-destructive testing of samples to assess the internal structure and destructive testing of samples for the assessment of mechanical properties such as tensile strength. Due to the increasing availability of data in automated processes and the complexity of welding processes, the application of Deep Learning has a great potential to reduce quality control efforts in automotive body shops. Using Deep Learning to leverage process data and accurately predict quality parameters in welding processes is investigated in research, yet model training requires a large, balanced and annotated dataset, whose generation is time and cost intensive, particularly for production data. However, there are generative AI methods such as Generative Adversarial Networks (GANs) that are able to generate synthetic data and thus offer the potential to generate a large amount of annotated production data with relatively little effort. This paper presents a systematic approach to evaluate the potential of incorporating synthetic data in a real-world production dataset to improve quality control using Deep Learning. The approach is validated for the analysis of real-world ultrasound images of resistance spot welding (RSW) processes from the automotive industry. Different Deep Learning architectures to generate synthetic data are compared. Results show that adding synthetic data to the training dataset can improve the accuracy of Deep Learning models for quality monitoring in welding processes

Applicability of Advanced Manufacturing Technologies for Agile Product Development in the Internet of Production: A Strategic Framework

Evolving product complexities and customer demands in an increasingly unstable environment are challenging companies worldwide. Agile product development can help to overcome these challenges but originates in software development. It is argued whether it is completely transferable towards the build-up of physical products. This paper aims to support agile product development for physical products by classifying appropriate advanced manufacturing technologies (AMTs) and identifying their demand for further research and development. A framework - the agile readiness level (ARL) for AMTs - is derived. It is consisting of five main factors of agile product development: testing & self-improvement, distribution & availability, accessibility for non-experts, time from idea to product, and overall flexibility. The ARL is evaluated for eight AMTs which are developed within the research cluster “Internet of Production” (IoP) at RWTH Aachen University. It is found that the ARL helps to identify similarities of diverse AMTs as well as research directions that need to be taken. It therefore contributes to the transfer of agile development methodologies from software to hardware products with the use of AMTs. Differences in technological feasibility for agile prototyping arise due to safety and complexity, targeted user group, and varying demands for support by artificial intelligence (AI) solutions

Scalable high-repetition-rate sub-half-cycle terahertz pulses from spatially indirect interband transitions

Intense phase-locked terahertz (THz) pulses are the bedrock of THz lightwave electronics, where the carrier field creates a transient bias to control electrons on sub-cycle time scales. Key applications such as THz scanning tunnelling microscopy or electronic devices operating at optical clock rates call for ultimately short, almost unipolar waveforms, at megahertz (MHz) repetition rates. Here, we present a flexible and scalable scheme for the generation of strong phase-locked THz pulses based on shift currents in type-II-aligned epitaxial semiconductor heterostructures. The measured THz waveforms exhibit only 0.45 optical cycles at their centre frequency within the full width at half maximum of the intensity envelope, peak fields above 1.1 kV cm−1 and spectral components up to the mid-infrared, at a repetition rate of 4 MHz. The only positive half-cycle of this waveform exceeds all negative half-cycles by almost four times, which is unexpected from shift currents alone. Our detailed analysis reveals that local charging dynamics induces the pronounced positive THz-emission peak as electrons and holes approach charge neutrality after separation by the optical pump pulse, also enabling ultrabroadband operation. Our unipolar emitters mark a milestone for flexibly scalable, next-generation high-repetition-rate sources of intense and strongly asymmetric electric field transients

Efficacy of bendamustine and rituximab as first salvage treatment in chronic lymphocytic leukemia and indirect comparison with ibrutinib: A GIMEMA, ERIC and UK CLL FORUM study

We performed an observational study on the efficacy of bendamustine and rituximab (BR) as first salvage regimen in chronic lymphocytic leukemia (CLL). In an intention-to-treat analysis including 237 patients, the median progression-free survival (PFS) was 25 months. The presence of del (17p), unmutated IGHV and advanced stage were associated with a shorter PFS at multivariate analysis. The median time-to-next treatment was 31.3 months. Front-line treatment with a chemoimmunotherapy regimen was the only predictive factor for a shorter time to next treatment at multivariate analysis. The median overall survival (OS) was 74.5 months. Advanced disease stage (i.e. Rai stage III-IV or Binet stage C) and resistant disease were the only parameters significantly associated with a shorter OS. Grade 3-5 infections were recorded in 6.3% of patients. A matched-adjusted indirect comparison with ibrutinib given second-line within Named Patient Programs in the United Kingdom and in Italy was carried out with OS as objective end point. When restricting the analysis to patients with intact 17p who had received chemoimmunotherapy in first line, there was no difference in OS between patients treated with ibrutinib (63% alive at 36 months) and patients treated with BR (74.4% alive at 36 months). BR is an efficacious first salvage regimen in CLL in a real-life population, including the elderly and unfit patients. BR and ibrutinib may be equally effective in terms of OS when used as first salvage treatment in patients without 17p deletion. (Registered at clinicaltrials.gov identifier: 02491398)

Measurement of the production of a W boson in association with a charm quark in pp collisions at √s = 7 TeV with the ATLAS detector

The production of a W boson in association with a single charm quark is studied using 4.6 fb−1 of pp collision data at s√ = 7 TeV collected with the ATLAS detector at the Large Hadron Collider. In events in which a W boson decays to an electron or muon, the charm quark is tagged either by its semileptonic decay to a muon or by the presence of a charmed meson. The integrated and differential cross sections as a function of the pseudorapidity of the lepton from the W-boson decay are measured. Results are compared to the predictions of next-to-leading-order QCD calculations obtained from various parton distribution function parameterisations. The ratio of the strange-to-down sea-quark distributions is determined to be 0.96+0.26−0.30 at Q 2 = 1.9 GeV2, which supports the hypothesis of an SU(3)-symmetric composition of the light-quark sea. Additionally, the cross-section ratio σ(W + +c¯¯)/σ(W − + c) is compared to the predictions obtained using parton distribution function parameterisations with different assumptions about the s−s¯¯¯ quark asymmetry

Evidence for the Higgs-boson Yukawa coupling to tau leptons with the ATLAS detector

Results of a search for H → τ τ decays are presented, based on the full set of proton-proton collision data recorded by the ATLAS experiment at the LHC during 2011 and 2012. The data correspond to integrated luminosities of 4.5 fb−1 and 20.3 fb−1 at centre-of-mass energies of √s = 7 TeV and √s = 8 TeV respectively. All combinations of leptonic (τ → `νν¯ with ` = e, µ) and hadronic (τ → hadrons ν) tau decays are considered. An excess of events over the expected background from other Standard Model processes is found with an observed (expected) significance of 4.5 (3.4) standard deviations. This excess provides evidence for the direct coupling of the recently discovered Higgs boson to fermions. The measured signal strength, normalised to the Standard Model expectation, of µ = 1.43 +0.43 −0.37 is consistent with the predicted Yukawa coupling strength in the Standard Model

Measurements of fiducial and differential cross sections for Higgs boson production in the diphoton decay channel at s√=8 TeV with ATLAS

Measurements of fiducial and differential cross sections are presented for Higgs boson production in proton-proton collisions at a centre-of-mass energy of s√=8 TeV. The analysis is performed in the H → γγ decay channel using 20.3 fb−1 of data recorded by the ATLAS experiment at the CERN Large Hadron Collider. The signal is extracted using a fit to the diphoton invariant mass spectrum assuming that the width of the resonance is much smaller than the experimental resolution. The signal yields are corrected for the effects of detector inefficiency and resolution. The pp → H → γγ fiducial cross section is measured to be 43.2 ±9.4(stat.) − 2.9 + 3.2 (syst.) ±1.2(lumi)fb for a Higgs boson of mass 125.4GeV decaying to two isolated photons that have transverse momentum greater than 35% and 25% of the diphoton invariant mass and each with absolute pseudorapidity less than 2.37. Four additional fiducial cross sections and two cross-section limits are presented in phase space regions that test the theoretical modelling of different Higgs boson production mechanisms, or are sensitive to physics beyond the Standard Model. Differential cross sections are also presented, as a function of variables related to the diphoton kinematics and the jet activity produced in the Higgs boson events. The observed spectra are statistically limited but broadly in line with the theoretical expectations