Search results

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

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.

Thermomechanical behavior of intermediate-size beam-to-wall assemblies including Glulam-beams connected to cross-laminated timber (CLT) walls with T-shape steel doweled connections was investigated at ambient temperature (AT) and after and during non-standard fire exposure.

Three AT tests were conducted to evaluate the load-carrying capacity and failure modes of the assembly at room temperature. Two post-fire performance (PFP) tests were performed to study the impact of 30-min (PFP30) and 60-min (PFP60) partial exposure to a non-standard fire on the residual strength of the assemblies. The assemblies were exposed to fire in a custom-designed frame, then cooled and loaded to failure. A fire performance (FP) test was conducted to study the fire resistance (FR) during non-standard fire exposure by simultaneously applying fire and a mechanical load equal to 65% of the AT load carrying capacity.

At AT, embedment failure of the dowels followed by splitting failure at the Glulam-beam and tensile failure of the epoxy between the layers of CLT-walls were the dominant failure modes. In both PFP tests, the plastic bending of the dowels was the only observed failure mode. The residual strength of the assembly was reduced 14% after 30 min and 37% after 60 min of fire exposure. During the FP test, embedment failure of timber in contact with the dowels was the only major failure mode, with the maximum rate of displacement at 51 min into the fire exposure.

This is the first time that the thermomechanical performance of such an assembly with a full-contact connection is presented.

The purpose of this paper is to review cases of artificial reefs built through additive manufacturing (AM) technologies and analyse their ecological goals, fabrication process, materials, structural design features and implementation location to determine predominant parameters, environmental impacts, advantages, and limitations.

The review analysed 16 cases of artificial reefs from both temperate and tropical regions. These were categorised based on the AM process used, the mortar material used (crucial for biological applications), the structural design features and the location of implementation. These parameters are assessed to determine how effectively the designs meet the stipulated ecological goals, how AM technologies demonstrate their potential in comparison to conventional methods and the preference locations of these implementations.

The overview revealed that the dominant artificial reef implementation occurs in the Mediterranean and Atlantic Seas, both accounting for 24%. The remaining cases were in the Australian Sea (20%), the South Asia Sea (12%), the Persian Gulf and the Pacific Ocean, both with 8%, and the Indian Sea with 4% of all the cases studied. It was concluded that fused filament fabrication, binder jetting and material extrusion represent the main AM processes used to build artificial reefs. Cementitious materials, ceramics, polymers and geopolymer formulations were used, incorporating aggregates from mineral residues, biological wastes and pozzolan materials, to reduce environmental impacts, promote the circular economy and be more beneficial for marine ecosystems. The evaluation ranking assessed how well their design and materials align with their ecological goals, demonstrating that five cases were ranked with high effectiveness, ten projects with moderate effectiveness and one case with low effectiveness.

AM represents an innovative method for marine restoration and management. It offers a rapid prototyping technique for design validation and enables the creation of highly complex shapes for habitat diversification while incorporating a diverse range of materials to benefit environmental and marine species’ habitats.

Polymer laser powder bed fusion (PBF-LB/P) is an additive manufacturing technology that is sustainable due to the possibility of recycling the powder multiple times and allowing the fabrication of gears without the aid of support structures and subsequent assembly. However, there are constraints in the process that negatively affect its adoption compared to other additive technologies such as material extrusion to produce gears. This study aims to demonstrate that it is possible to overcome the problems due to the physics of the process to produce accurate mechanism.

Technological aspects such as orientation, wheel-shaft thicknesses and degree of powder recycling were examined. Furthermore, the evolving tooth profile was considered as a design parameter to provide a manufacturability map of gear-based mechanisms.

Results show that there are some differences in the functioning of the gear depending on the type of powder used, 100% virgin or 50% virgin and 50% recycled for five cycles. The application of a groove on a gear produced with 100% virgin powder allows the mechanism to be easily unlocked regardless of the orientation and wheel-shaft thicknesses. The application of a specific evolutionary profile independent of the diameter of the reference circle on vertically oriented gears guarantees rotation continuity while preserving the functionality of the assembled mechanism.

In the literature, there are various studies on material aging and reuse in the PBF-LB/P process, mainly focused on the powder deterioration mechanism, powder fluidity, microstructure and mechanical properties of the parts and process parameters. This study, instead, was focused on the functioning of gears, which represent one of the applications in which this technology can have great success, by analyzing the two main effects that can compromise it: recycled powder and vertical orientation during construction.

Recently, there has been a shift toward the embodied energy assessment of buildings. However, the impact of material service life on the life-cycle embodied energy has received little attention. We aimed to address this knowledge gap, particularly in the context of the UAE and investigated the embodied energy associated with the use of concrete and other materials commonly used in residential buildings in the hot desert climate of the UAE.

Using input–output based hybrid analysis, we quantified the life-cycle embodied energy of a villa in the UAE with over 50 years of building life using the average, minimum, and maximum material service life values. Mathematical calculations were performed using MS Excel, and a detailed bill of quantities with >170 building materials and components of the villa were used for investigation.

For the base case, the initial embodied energy was 57% (7390.5 GJ), whereas the recurrent embodied energy was 43% (5,690 GJ) of the life-cycle embodied energy based on average material service life values. The proportion of the recurrent embodied energy with minimum material service life values was increased to 68% of the life-cycle embodied energy, while it dropped to 15% with maximum material service life values.

The findings provide new data to guide building construction in the UAE and show that recurrent embodied energy contributes significantly to life-cycle energy demand. Further, the study of material service life variations provides deeper insights into future building material specifications and management considerations for building maintenance.

Although the number of blue-collar industrial workers has been declining, manufacturing jobs continue to have considerable importance, even in technologically advanced economies. This study gives a voice to this often-overlooked group of workers, focusing on the Finnish vehicle industry.

The study assessed how manual workers in automotive manufacturing describe their job pride and how their accounts were related to conceptualizations of work orientation. The data included semi-structured interviews and an open-ended survey question on situations in which the respondents had felt proud of their work. The data were analysed using thematic analysis.

According to the results, the respondents had a high level of job pride in general, but the meaning given to this attitude varied considerably, depending on the situation. The study participants' work orientation was a mixture of instrumental and intrinsic traits. However, there were also respondents who did not experience job pride or who had lost it because of the work circumstances.

Relatively little research has assessed the importance of job pride in the context of industrial manufacturing. Recognizing job pride in its variety is crucial information for employers who aim to develop working conditions and employee retention.

Many additively manufactured parts suffer from reduced interlayer strength. This anisotropy is necessarily tied to the orientation during manufacture. When individual features on a part have conflicting optimal orientations, the part is unavoidably compromised. This paper aims to demonstrate a strategy in which conflicting features can be functionally separated into “co-parts” which are individually aligned in an optimal orientation, selectively reinforced with continuous fiber, printed simultaneously and, finally, assembled into a composite part with substantially improved performance.

Several candidate parts were selected for co-part decomposition. They were printed as standard fused filament fabrication plastic parts, parts reinforced with continuous fiber in one plane and co-part assemblies both with and without continuous fiber reinforcement (CFR). All parts were loaded until failure. Additionally, parts representative of common suboptimally oriented features (“unit tests”) were similarly printed and tested.

CFR delivered substantial improvement over unreinforced plastic-only parts in both standard parts and co-part assemblies, as expected. Reinforced parts held up to 2.5x the ultimate load of equivalent plastic-only parts. The co-part strategy delivered even greater improvement, particularly when also reinforced with continuous fiber. Plastic-only co-part assemblies held up to 3.2x the ultimate load of equivalent plastic only parts. Continuous fiber reinforced co-part assemblies held up to 6.4x the ultimate load of equivalent plastic-only parts. Additionally, the thought process behind general co-part design is explored and a vision of simulation-driven automated co-part implementation is discussed.

This technique is a novel way to overcome one of the most common challenges preventing the functional use of additively manufactured parts. It delivers compelling performance with continuous carbon fiber reinforcement in 3D printed parts. Further study could extend the technique to any anisotropic manufacturing method, additive or otherwise.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

This paper presents Quick Changeover Design (QCD), which is a structured methodological approach for Original Equipment Manufacturers to drive and support the design of machines in terms of rapid changeover capability.

To improve the performance in terms of set up time, QCD addresses machine design from a single-minute digit exchange of die (SMED). Although conceived to aid the design of completely new machines, QCD can be adapted to support for simple design upgrades on pre-existing machines. The QCD is structured in three consecutive steps, each supported by specific tools and analysis forms to facilitate and better structure the designers' activities.

QCD helps equipment manufacturers to understand the current and future needs of the manufacturers' customers to: (1) anticipate the requirements for new and different set-up process; (2) prioritize the possible technical solutions; (3) build machines and equipment that are easy and fast to set-up under variable contexts. When applied to a production system consisting of machines subject to frequent or time-consuming set-up processes, QCD enhances both responsiveness to external market demands and internal control of factory operations.

The QCD approach is a support system for the development of completely new machines and is also particularly effective in upgrading existing ones. QCD's practical application is demonstrated using a case study concerning a vertical spindle machine.