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The purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality performance in both assembly and disassembly operations. This topic has not been extensively investigated in previous research.

An extensive experimental campaign involving 84 operators was conducted to repeatedly assemble and disassemble six different products of varying complexity to construct productivity and quality learning curves. Data from the experiment were analysed using statistical methods.

The human learning factor of productivity increases superlinearly with the increasing architectural complexity of products, i.e. from centralised to distributed architectures, both in assembly and disassembly, regardless of the level of overall product complexity. On the other hand, the human learning factor of quality performance decreases superlinearly as the architectural complexity of products increases. The intrinsic characteristics of product architecture are the reasons for this difference in learning factor.

The results of the study suggest that considering product complexity, particularly architectural complexity, in the design and planning of manufacturing processes can optimise operator learning, productivity and quality performance, and inform decisions about improving manufacturing operations.

While previous research has focussed on the effects of complexity on process time and defect generation, this study is amongst the first to investigate and quantify the effects of product complexity, including architectural complexity, on operator learning using an extensive experimental campaign.

Despite the significance of the construction industry to the nation's economic growth, there is empirical evidence that the sector is lagging behind other industries in terms of productivity growth. The need for improvements inspired the industry's stakeholders to consider using emerging technologies that support the enhancement. This research aims to report augmented reality applications essential for contractors' productivity improvement.

This study systematically reviewed academic journals. The selection of journal articles entailed searching Scopus and Web of Science databases. Relevant articles for reviews were identified and screened. Content analysis was used to classify key applications into six categories. The research results were limited to journal articles published between 2010 and 2021.

Augmented reality can improve construction productivity through its applications in assembly, training and education, monitoring and controlling, interdisciplinary function, health and safety and design information.

The research provides a direction for contractors on key augmented reality applications they can leverage to improve their organisations' productivity.

This study aims to solve the problem of weld quality inspection, for the aluminum alloy profile welding structure of high-speed train body has complex internal shape and thin plate thickness (2–4 mm), the conventional nondestructive testing method of weld quality is difficult to implement.

In order to solve this problem, the ultrasonic creeping wave detection technology was proposed. The impact of the profile structure on the creeping wave detection was studied by designing profile structural test blocks and artificial simulation defect test blocks. The detection technology was used to test the actual welded test blocks, and compared with the results of X-ray test and destructive test (tensile test) to verify the accuracy of the ultrasonic creeping wave test results.

It is indicated that that X-ray has better effect on the inspection of porosities and incomplete penetration defects. However, due to special detection method and protection, the detection speed is slow, which cannot meet the requirements of field inspection of the welding structure of aluminum alloy thin-walled profile for high-speed train body. It can be used as an auxiliary detection method for a small number of sampling inspection. The ultrasonic creeping wave can be used to detect the incomplete penetration welds with the equivalent of 0.25 mm or more, the results of creeping wave detection correspond well with the actual incomplete penetration defects.

The results show that creeping wave detection results correspond well with the actual non-penetration defects and can be used for welding quality inspection of aluminum alloy thin-wall profile composite welding joints. It is recommended to use the echo amplitude of the 10 mm × 0.2 mm × 0.5 mm notch as the criterion for weld qualification.

This paper explores the quality and flow of facade product information and the capabilities for avoiding the risk of facade fires early in the design process.

A qualitative case study using the process tracing method is conducted in two stages. First, a thematic analysis of reports and literature identified two categories for the problems that caused fast fire spread across the Grenfell Tower facade. This enabled classifying the identified problems into four stages of a facade life cycle: product design and manufacturing, procurement, facade design and construction. Second, the capabilities for avoiding the problems were explored by conducting in-depth interviews with 18 experts in nine countries, analyzing design processes and designers' expertise and examining the usability of three digital interfaces in providing required information for designing fire-safe facades.

The results show fundamental flaws in the quality of facade product information and usability of digital interfaces concerning fire safety. These flaws, fragmented design processes and overreliance on other specialists increase the risk of design defects that cause fast fire spread across facades.

The findings have implications for standardization of building product information, digitalization in industrialized construction and facade design management.

This research adds to the body of knowledge on sustainability in the built environment. It is the first study to highlight the fundamental problem of facade product information, which requires urgent attention in the rapid transition toward digital and industrialized construction.

This paper aims to present the mathematical foundation of so-called advance algorithms, developed to compensate for defects during acceleration and deacceleration of the print head in filament-based melt extrusion additive processes. It then investigates the validity of the mathematical foundation, its performance on a low-cost system and the effect of changing layer height on the algorithm’s associated process parameter.

This study starts with a compilation and review of literature associated with advance algorithms, then elaborates on its mathematical foundation and methods of implementation. Then an experiment displaying the performance of the algorithm implemented in Marlin machine firmware, Linear Advance 1.0, is performed using three different layer heights. The results are then compared with simulations of the system using Simulink.

Findings suggests that advance algorithms following the presented approach is capable of eliminating defects because of acceleration and deacceleration of the print head. The results indicate a layer height dependency on the associated process parameter, requiring higher compensation values for lower layer heights. It also shows higher compensation values for acceleration than deacceleration. Results from the simulated mathematical model correspond well with the experimental results but predict some rapid variations in flow rate that is not reflected in the experimental results.

As there are large variations in printer design and materials, deviation between different setups must be expected.

To the best of authors’ knowledge, this study is the first to describe and investigate advance algorithms in academic literature.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

This study aims to analyze how the interplay between hard and soft elements of total quality management (TQM) produces the conditions for sustaining success in the quest for quality.

A qualitative analysis (Gioia method) was carried out on an original dataset collected through both direct and indirect methods (i.e. archival sources, interviews and observations) to generate a new interpretive framework.

The interpretative framework identifies four categories of elements: trigger elements create the starting conditions for a quality virtuous cycle; benchmarking tools set the standards of performance; improvement tools enable exploration of the space of possible alternative practices and finally, catalytic forces allow the institutionalization of effective techniques discovered in this search process into new standards.

The findings the authors present in this paper are derived by a single case study, limiting the generalizability of our results in other settings.

This study has three implications: first, the design of trigger elements is critical for the success of any TQM initiative; second, the interplay of improvement and benchmarking tools at several levels should be coherent and third, to exploit the potential of TQM, efforts should be devoted to the dissemination of new effective practices by means of catalyzing elements.

The model provides a more specific understanding of the nature and purpose of the hard and soft elements of TQM and the dynamic interaction between the two classes of elements over time.

The aim of this study is to develop an in-depth case study on the implementation on Lean six sigma (LSS) in Schnell S.p.A., Italian company leader of an important multinational industrial group, highlighting the benefits that can be achieved from a careful application of this method, the main challenges and organizational learning from its implementation.

The study has been developed with a qualitative approach, creating a single in-depth case study, with the participant observation of researchers in the project which lasted 4 months. Periodic weekly meetings were done with the working group to exchange feedback on the development of the project to share opinions and data.

A project has been developed to stabilize the procurement process of a pull-type production cell, which experienced delays in supply lead times. The causes of the problems in their process of managing the supply of the production cell were found and some inefficiencies in the internal process of fulfillment of supply orders have been intercepted, the optimization of which has allowed the generation of an automatic system for sending supply orders, coming directly from the production line.

This study described the path and dynamics of the transformation process that business organizations undertake for optimizing their profitability and competitive advantage, placing emphasis on an innovative methodology for conducting business process improvement projects, which constitutes its operating philosophy on the effective and efficient use of company resources and skills, to guarantee to the company the achievement of a lasting and defensible competitive advantage over time.

Exoskeletons are mechanical structures that humans can wear to increase their strength and endurance. The purpose of this paper is to explain how exoskeletons can be used to improve performance across five phases of manufacturing.

Multivocal literature review, encompassing scientific literature and the grey literature of online reports, etc., to inform comprehensive, comparative and critical analyses of the potential of exoskeletons to improve manufacturing performance.

There are at least eight different types of exoskeletons that can be used to improve human strength and endurance in manual work during different phases of production. However, exoskeletons can have the unintended negative consequence of reducing human flexibility leading to new sources of musculoskeletal disorders (MSD) and accidents.

Findings are relevant to function allocation research concerned with manual production work. In particular, exoskeletons could exacerbate the traditional trade-off between human flexibility and robot consistency by making human workers less flexible.

The introduction of exoskeletons requires careful health and safety planning if exoskeletons are to improve human strength and endurance without introducing new sources of MSD and accidents.

The originality of this paper is that it provides detailed information about a new manufacturing technology: exoskeletons. The value of this paper is that it provides information that is comprehensive, comparative and critical about exoskeletons as a potential alternative to robotics across five phases of manufacturing.

This paper examines the decline of the largest working parliamentary democracy in India overtime, but accelerating since 2014 as the Bharatiya Janata Party (BJP), under the leadership of Prime Minister Narendra Modi at the Center (as the federal government is commonly known).

It is eclectic. Original constitution, along with commentaries, are studied. News outlets, government pronouncements, journal articles, and other media outlets — electronic and print — are also sourced.

The findings show how three important features in democracy — elections and their outcomes, control of information, and suppression of dissent are widely used to undermine constitutional democracy.

Democracy can be undermined without altering the Constitution itself. It also explains the irony of Modi’s popularity, given the undemocratic practices. As he may continue in office for some time to come, commanding a log-rolling majority, the need of the hour is a united, constructive and effective opposition to ensure a healthy working democracy.