Engineering Change Research Articles

Evaluating Early Predictive Performance of Machine Learning Approaches for Engineering Change Schedule – A Case Study Using Predictive Process Monitoring Techniques

By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering change (EC) process. Here, failing to adhere to planned implementation dates can have severe impacts on assembly lines, and it is paramount that potential negative cases are identified as early as possible. Current PBPM research, however, has seldomly investigated the predictive performance of machine learning approaches and their applicability at early process steps, let alone for the EC process. In our research, we show that given adequate feature encoding, shallow learners can accurately predict schedule adherence after process initialisation. Based on EC data from an automotive manufacturer, we provide a case sensitive performance overview on algorithm-encoding combinations. For that, three algorithms (XGBoost, Random Forest, LSTM) were combined with four encoding techniques. The encoding techniques used were the two common aggregation-based and index-based last state encoding, and two new combinations of these, which we term advanced aggregation-based and complex aggregation-based encoding. The study indicates that XGBoost-index-encoded approaches outclass regarding average predictive performance, whereas Random-Forest-aggregation-encoded approaches perform better regarding temporal stability due to reduced influence by dynamic features. Our research provides a case-based reasoning approach for deciding on which algorithm-encoding combination and evaluation metrics to apply. In doing so, we provide a blueprint for an early warning and monitoring method within the EC process and other similarly complex processes.

Innovations in Engineering and Technology of Worm-Type Gears

Relatively small changes in engineering and technology of traditional established products turn over time into a series of milestones that determine both technical characteristics and production methods, and as a consequence - the competitiveness of the final product. Accordingly, it is relevant to periodically look at the evolution of the accumulated solutions and make some conclusions on it. For this purpose, the paper reviews innovations in design and production of worm-type gears obtained in Izhevsk scientific school in the field of gears for the last two decades: spiroid gears operating at low rotational speeds and transmitting high loads, leading to plastic deformations due to compactness of gears; spiroid gears with small gear ratios (3...8) competing with more expensive in production bevel and hypoid ones; worm gears with steel hardened gearwheels with increased strength and manufacturability; planetary spiroid gears providing extremely high gear ratio, improved methods and techniques of design and preproduction of gear cutting. The paper presents the review of innovations in design of worm-type gears made in Izhevsk scientific school in the field of gears for the last two decades: spiroid gears for cases of low speeds and small gear ratios, worm gears with steel hardened gearwheels, planetary and spiroid gears, improved methods and techniques of design and preparation of gear cutting.

Electric-field nanobubbles: A step change in nanobubble engineering, and its “coming of age”

Electric-field nanobubbles: A step change in nanobubble engineering, and its “coming of age” Niall J. English, from Chemical Engineering at University College Dublin, discusses how electric-field nanobubbles have displaced their mechanically-generated counterparts in performance and sustainability. Limited solubility of gases in water, such as oxygen, is a fundamental challenge. In ecosystems and the environment, lack of dissolved oxygen (DO) is a major reason for fish kills and water bodies being blighted by algal blooms, in addition to lack of effectiveness of activated-sludge processes in water treatment or poorer-than-hoped results in irrigation. Nanobubbles (NBs) are tiny gas bubbles on the nanometre (nm) scale. They may be thermodynamically metastable for up to many months, or sometimes even longer, and have enhanced gas-transfer properties and substantial industrial potential. The concept of NBs versus traditional, coarser bubbles – with the latter subject to buoyancy phenomena, while NBs are relatively impervious to rising – in essence, “subverts” Stokes’ Law of bubble rising – and have an excellent area-to-volume ratio. If NBs are generated well – indeed, the art of “nanobubble engineering”, as it were – NBs lead to important and useful applications.

Effects of Magnetic Fields and Nanoparticle Additives on Diesel Engine Emissions and Performance: A Comprehensive Experimental Analysis

T. In the present study, performance and emission changes in a compression ignition engine were investigated by combining two methods. The first method involves adding nanoparticle additives to diesel fuel. Titanium dioxide (TiO2) with a particle size of 21 nm was used as nanoparticle. TiO2 was added to diesel fuel at doses of 50 mg and 100 mg per 1 kg (50 and 100 ppm). After adding the nanoparticle to the diesel fuel, each mixture was stirred with a mechanical stirrer for one hour. In the second method, a magnetic field of 1 tesla was created around the fuel. Neodymium magnets were placed circularly around the diesel fuel line to create the magnetic field. The experiments were carried out at 660 RPM engine speed and 100% torque. During the experiments, data on engine performance, in-cylinder pressure and emissions were recorded. This study aims to contribute to the development of alternative fuel applications to improve performance and emissions in compression ignition engines.

Explanatory frameworks in complex change and resilience system modelling

Abstract Heterogenous flows across system boundaries continue to pose significant problems for efficient resource allocation especially with respect to long term strategic planning and immediate problems about allocation to address particular resource shortages. The approach taken here to modelling such flows is an engineering change prediction one. This enables margin modelling by producing system models in dependency matrices with different linkage types. Change prediction approaches from engineering design can analyse where these bottlenecks in integrated systems would be so that resources can be deployed flexibility to avoid them and address them when they occur. Current state of the art of margin research can be furthered by identifying margins on multiple levels of system composition. It can usefully be complemented by a category theory based approach which allows representation of variable and constant properties of models under changing conditions, and the identification of flows within models. Category theory is useful for formalising such explanatory frameworks as it can both structure systems and permit analysis of their applications in a complementary way.

Converting compression ignition engine to dual-fuel (diesel + CNG) engine and experimentally investigating its performance and emissions

Abstract One of the suitable solutions for burning natural gas in diesel engines is the use of dual-fuel technology. In this study, the MT440C compression ignition engine has been converted to dual fuel (diesel + CNG) simultaneously combustion of diesel fuel and natural gas, with the least amount of engine changes and using the most amount of natural gas. The ignition of the engine was in the range of the governor. Experiments in stable conditions for the working modes of the engine were performed with pure diesel fuel and mixed gas diesel fuel. The effects of natural gas fuel as the main fuel and diesel fuel as the spark ignition on a 4-cylinder compression ignition engine were investigated on the performance and emissions. According to the engine speed and load, the amount of diesel-fuel injection was adjusted by making mechanical changes in the governor, while the ignition system was not used. These tests were performed at engine speeds of 1,200, 1,400, 1,600, 1,800, and 2,000 rpm, using single diesel fuel and dual fuel (diesel + CNG). These data were collected in the Engine Research Center of Tabriz Motorsazan Company, and experimental runs were repeated three times One of the goals of this research is to reduce the consumption of diesel fuel, and in the current study, compressed natural gas (CNG) is 72% and diesel is 28% of the dual fuel in idling. This study showed that the emission of some pollutants increased and some decreased in the dual-fuel mode. Therefore, more research is needed on modifying the diesel injection system as a spark plug or the CNG injection system to reduce the emission of greenhouse gases.

Anthropogenic activities have accelerated the restoration of carbon sequestration services in the upper Yellow River

Carbon sequestration services stemming from ecosystems facilitate the absorption of CO2 and mitigation of greenhouse effects. Thus, investigating the spatiotemporal changes of carbon sequestration services and their response patterns to human activities is essential in relation to achieving the strategic carbon peak and carbon neutrality (“double carbon”) goal in a region. In this study, the spatiotemporal carbon sequestration patterns in the upper reaches of the Yellow River from 1985 to 2020 were assessed based on measured sample points and spatial modeling combined with multi-source remote sensing data. Specifically, the impacts of human activities on the carbon sequestration services in the area were quantitatively analyzed. The results showed that, for the past 35 years, carbon sequestration in the upper reaches of the Yellow River ranged from 80.09 Tg to 98.48 Tg, with lower levels in the northeast and southwest, and higher ones in the northwest and southeast. From 1985 to 1998, carbon sequestration in the upper reaches of the Yellow River was mainly affected by the natural climate and showed a fluctuating upward trend. From 1998 to 2001, carbon sequestration declined sharply due to the influence of human activities and the natural climate, whereas it showed a significant increasing trend from 2001 to 2020, affected by the combined effects of ecological engineering and climate change. In 1998–2001, the degree of human influence was −5.92% to approximately −11.68%, and from 2001 to 2020, it was approximately 2.32% to 6.78%. This study shows that while human social development can negatively affect the carbon sequestration services of ecosystems, ecological engineering can accelerate its recovery, recovery trends and recovery endpoints are constrained by natural factors.

Intermittent Electrification with Battery Locomotives and the Post-Diesel Future of North American Freight Railroads

Changing attitudes, regulations, public policy, and international treaties concerning fossil fuels are likely to lead freight railroads toward carbon-neutral technologies, yet only electric traction matches the performance of diesel-electric locomotives. The tremendous power requirements of freight trains make efforts to reduce greenhouse gas (GHG) emissions challenging, but the prospect of 7.2 megawatt-hour battery-electric locomotives (BELs) offers promise, and intermittent electrification can facilitate battery charging. Train performance calculations under simulated real-world conditions show four 7.2 megawatt-hour BELs can power 8,000-ton trains up to 230 mainline miles unassisted, with average energy consumption of 12.5 watt-hours/ton-mile. This permits discontinuous electrification of major freight lines, leaving “gaps” of up to 200 mi to reduce capital costs, especially in rugged terrain or where the power grid is sparse. Massive onboard battery arrays and intermittent access to the electrical grid for traction power and recharging provide great energy savings by recycling energy now lost when traveling downhill or braking. A case study of a hypothetical Class I railroad found intermittent electrification with BELs more than 60% more cost-effective than contiguous electric districts, and dramatically reduced engine changes. To reduce railroad GHG emissions, governments must support technical development, show that electrification works in various North American settings, and develop institutional-financial frameworks to incentivize intermittent electrification with BELs, all in the context of massive environmental and capacity upgrades to electrical networks. Given proper assistance and incentives, early 21st century railroads may find discontinuous overhead-wire electrification offers great promise in operating terms.

TSTL-GNN: Graph-Based Two-Stage Transfer Learning for Timing Engineering Change Order Analysis Acceleration

Timing Engineering Change Order (ECO) is time-consuming in IC design, requiring multiple rounds of timing analysis. Compared to traditional methods for accelerating timing analysis, which focus on a specific design, timing ECO requires higher accuracy and generalization because the design changes considerably after ECO. Additionally, there are challenges with slow acquisition of data for large designs and insufficient data for small designs. To solve these problems, we propose TSTL-GNN, a novel approach using two-stage transfer learning based on graph structures. Significantly, considering that delay calculation relies on transition time, we divide our model into two stages: the first stage predicts transition time, and the second stage predicts delay. Moreover, we employ transfer learning to transfer the model’s parameters and features from the first stage to the second due to the similar calculation formula for delay and transition time. Experiments show that our method has good accuracy on open-source and industrial applications with an average R2score/MAE of 0.9952/13.36, and performs well with data-deficient designs. Compared to previous work, our model reduce prediction errors by 37.1 ps on the modified paths, which are changed by 24.27% on average after ECO. The stable R2 score also confirms the generalization of our model. In terms of time cost, our model achieved results for path delays consuming up to 80 times less time compared to open-source tool.

The Nuts and Bolts of Developing a Sustainable, Collaborative Network for STEM Transformation

The (STEM)2 Network (Sustainable, Transformative Engagement across a Multi-Institution/Multidisciplinary STEM Network) is a National Science Foundation Research Coordination Network-Undergraduate Biology Education funded project intended to bridge disciplinary and institutional silos that function as barriers to systemic change in science, technology, engineering, and mathematics (STEM) in higher education. We utilized three foundational frameworks to develop an adaptable model that we posit is applicable across contexts. The model includes a core infrastructure that, combined with intentional self-reflection, results in an adaptable design that can be tailored to individual institutions, contexts, and goals. Herein, we describe the inception of the network, the foundational theoretical frameworks that guide network development and growth, and detail network structure and operations with the intention of supporting others in creating their own networks. We share the nuts and bolts of how we developed the (STEM)2 Network, and include a supplemental network development planning guide to support others in utilizing the (STEM)2 Network model to reach their own objectives.

Climate concepts for supporting political goals of mitigation and adaptation: The case for “climate crisis”

AbstractClimate concepts are crucial to understand the effects of human activity on the climate system scientifically, and to formulate and pursue policies to mitigate and adapt to these effects. Yet, scientists, policymakers, and activists often use different terms such as “global warming,” “climate change,” “climate crisis,” or “climate emergency.” This advanced review investigates which climate concept is most suitable when we pursue mitigation and adaptation goals in a scientifically informed manner. It first discusses how survey experiments and social science reviews on climate frames draw normative recommendations about which terms to use for public climate communication. It is suggested that such normative claims can be refined by including the scientific alongside lay uses of a climate concept, and by using explicit assessment conditions to evaluate how suitable a concept is for formulating mitigation and adaptation goals. Drawing on philosophical theories of conceptual change in science and conceptual engineering, a novel framework with two assessment conditions is introduced and then applied to “global warming,” “climate change,” “climate emergency,” and “climate crisis.” The assessment suggests that currently, “climate crisis” is most suitable to formulate and pursue climate mitigation and adaptation goals. Using this concept promotes the epistemic goals of climate science to a high degree, bridges scientific, political, and activist discourse, and fosters for democratic participation when articulating climate policies.This article is categorized under: The Social Status of Climate Change Knowledge > Climate Science and Decision Making The Social Status of Climate Change Knowledge > Knowledge and Practice Perceptions, Behavior, and Communication of Climate Change > Communication

KEYS TO CREATING AN EFFECTIVE REGIONAL INNOVATION INFRASTRUCTURE

Integration processes in science, education and production are driven by the acceleration of scientific and technological progress, the introduction of innovative scientific developments into mass production and the informatisation of the economy. The development of knowledge-intensive industries places new demands on the training and retraining of innovation-sensitive personnel, as well as on science, education and the economy as a whole, which are currently unable to develop effectively independently and adapt to changes in technology and engineering. The purpose of the article is to find promising areas for the development of education and science based on their integration with business entities not only within the limited space of regions, but also on the basis of spatial integration of territories with a special geographical location. Methodology. The necessary and socially significant conditions for the integration of science, education and business are the achievement of knowledge and skills, goals and values related to the acceleration of the reproduction of innovation potential in society, awareness of the role of science, education and business in solving problems of innovation development, and also their integration interaction in this area. Practical implications. Three main levels of economic systems are allocated: individual, organisation, and region. It has been determined that the degree of influence of innovation processes on them is determined by the presence and condition of three factors necessary for the formation of innovation susceptibility (stabilising, activating and structural). Value/Оriginality. Based on the concepts of the main innovation systems and the necessary conditions for innovative economic growth, the authors have clarified the content of the category "innovation receptivity of the region". The paper also reveals the basic principles of building a model of innovation receptivity and determines the degree of interdependence and interdependence between its constituent elements based on a systematic approach.

Performance assessment and comparison of a catalytic steam reforming enhanced closed-brayton-cycle power generation system for hypersonic vehicles

Hypersonic vehicles as next-generation aircraft have broad applications, but existing technologies of onboard power generation are hardly applicable due to the change of engines. In this research, a catalytic steam reforming enhanced closed-Brayton-cycle power generation system based on is developed. This system combines the closed Brayton cycle (CBC) and catalytic steam reformed fuel vapor turbine (CSRFVT) to supply electric power. The zero-dimensional models of CBC and the CSRFVT are established to evaluate system performance. Results indicate the products with a water content of 30% have the strongest working ability, which can reach 330 kJ/kg. Moreover, the thermal efficiency and electric power of the parallel arrangement are higher than that of the series arrangement. Besides, the lower pressure of water is beneficial to the electric power. However, the difference in electric power between various pressures is less than 1 kW. In addition, under the same heat absorption, the total electric power of the system based on fuel and water is higher, and the former's outlet temperature of cooling channels is lower. The maximum total electric power of the novel system is about 120 kW. This research provides an innovative technical solution for the power generation of hypersonic vehicles.

In Situ Performance Monitoring of Electrochemical Oxygen and Hydrogen Peroxide Sensors in an Additively Manufactured Modular Microreactor.

Miniaturized and microstructured reactors in process engineering are essential for a more decentralized, flexible, sustainable, and resilient chemical production. Modern, additive manufacturing methods for metals enable complex reactor-geometries, increased functionality, and faster design iterations, a clear advantage over classical subtractive machining and polymer-based approaches. Integrated microsensors allow online, in situ process monitoring to optimize processes like the direct synthesis of hydrogen peroxide. We developed a modular tube-in-tube membrane reactor fabricated from stainless steel via 3D printing by laser powder bed fusion of metals (PBF-LB/M). The reactor concept enables the spatially separated dosage and resaturation of two gaseous reactants across a membrane into a liquid process medium. Uniquely, we integrated platinum-based electrochemical sensors for the online detection of analytes to reveal the dynamics inside the reactor. An advanced chronoamperometric protocol combined the simultaneous concentration measurement of hydrogen peroxide and oxygen with monitoring of the sensor performance and self-calibration in long-term use. We demonstrated the highly linear and sensitive monitoring of hydrogen peroxide and dissolved oxygen entering the liquid phase through the membrane. Our measurements delivered important real-time insights into the dynamics of the concentrations in the reactor, highlighting the power of electrochemical sensors applied in process engineering. We demonstrated the stable continuous measurement over 1 week and estimated the sensor lifetime for months in the acidic process medium. Our approach combines electrochemical sensors for process monitoring with advanced, additively manufactured stainless steel membrane microreactors, supporting the power of sensor-equipped microreactors as contributors to the paradigm change in process engineering and toward a greener chemistry.

Energetic and exergetic metrics of a cargo aircraft turboprop propulsion system by using regression method for dynamic flight

Nowadays, the development of aviation in line with sustainability goals is one of the current challenges. Being a source of environmental impact, aero-engines have been the main research and design object for achieving these aims. In this study, thermodynamic analysis involving exergy and sustainability computations is performed at different flight conditions where Mach varies between 0 and 0.7 and altitude changes between 0 and 7.7 km. Based on this analysis, modeling exergy parameters for each component are carried out with multiple regression approach. The exergy efficiency of turboprop engine at dynamic flight conditions varies between 15% and 25.9% whereas it is measured between 76 and 77% for the combustor and between 93 and 99% for gas turbine. Moreover, exergy destruction of the turboprop engine changes between 2.15 MW and 7.55 MW. Exergetic improvement potential rate (IPR) of the combustor resides between 0.4 MW and 1.21 MW throughout flight conditions whereas it is found at orders of 0.1 MW or less for other components. On the other hand, linear and quadratic modelings are performed for several parameters of turboprop engine components including exergy efficiency, exergy destruction and IPR under dynamic flight conditions. The determination coefficient (R2) of the models changes between 0.69 and 0.98 in linear modeling, whereas R2 in quadratic modeling improves to 0.97 and 0.99 ranges. It is thought that component-based modeling by considering different flight conditions could contribute to determining points where component efficiency is the highest.

Mathematical modeling of rocks fracture within local structures

Soil massif fracturing has a significant impact on change in engineering and geological conditions and, as a result, on stability of structures. Development of tectonic fracturing of local structures, taking into account the history of the process, its mechanism, resulting stresses in the massif and subsequent deformations of the rocks, led to a change in their structure, composition and strength characteristics, activation of hypergenesis and exogenous processes. The above circumstances require careful attention to identification of areas of increased fracturing, as the most dangerous in terms of risks during the construction of engineering structures. Field methods for assessing the fracturing of rock masses are laborious. It is not always possible to conduct instrumental surveys that allow solving the final problem – establishing patterns and sizes of damaged areas within local structures. The existing mathematical models for assessing fracturing, as a rule, are used to solve local problems: assessing the stability of developed pits, water content of rock masses, degree of fragmentation of individual blocks, etc. This information is not sufficient when assessing the areal distribution of weakened zones and clarifying their boundaries, since it does not take into account the history of the development of the structure, its parameters (dimensions, amplitude of the foundation block uplift, deformation properties of rocks). Aim. To develop a mathematical model of formation of the red-colored strata tectonic fracturing zones based on deformation criterion of destruction and mechanism of development of local structures. Results. The authors have developed a new mathematical model for predicting damage (fracturing) of terrigenous rocks of the red-colored strata that make up local structures, based on the mechanism of formation of local tectonic structures of the 3rd order and the deformation criterion of destruction. The paper introduces the mathematical dependencies that make it possible to predict the size (area) of taxa based on the data on the uplift amplitude of local structures. The results of the research can be used in assessing the fracturing of massifs composed of terrigenous rocks, and make it possible to judge the regularities in distribution of weakened zones within the entire massif being assessed.

Thermodynamic responses of bleed air on separated flow high by-pass turbofan engine under constant speed and constant thrust cases

Gas turbine engines, especially turbofan, have been famous for their fuel economy and reliability attributes. Therefore, these have continued to be manufactured by updating their features. Therefore, thermodynamic analyses related to turbofan engines have drawn great attention, providing deeper insight into potential improvements. New generation aircrafts have been conceived to draw bleed air as little as possible from their aero-engines so as to provide lower fuel consumption. In this study, influences of bleed air ratio (BAR) ranging from 1 % to 6 % on high by-pass turbofan (HBT) engine are handled by considering constant speed (CS) and constant thrust (CT) cases. Specific fuel consumption of HBT engine changes between 20.68 g/kN and 22.25 g/kN at CT whereas varies between 20.63 g/kN and 21.9 g/kN at CS owing to variation of BAR. Moreover, exergy efficiency of HBT resides between 26.92 % and 25.02 % at CT while changes 26.99 % and 25.43 % at CS. It shows that to maintain thrust constant could slightly penalty the engine performance in terms of fuel efficiency. On the other hand, the higher BAR leads to lower exergy efficiency of the combustor, which changes 80.95 %–82.33 % ranges at CT and varies 80.77 %–82.31 % intervals at CS. Finally, improvement potential rate for the combustor is found higher 1 MW and increases with elevated BAR whereas that for other five components is measured less than 0.04 MW. It could be underscored that adverse effect of BAR for both cases is observed. It is thought that this study helps in observing what extent the engine is affected from usage of bleed air in terms of thermodynamic metrics.

Mining LDA topics on construction engineering change risks based on graded evidence.

Engineering change (EC) risk may negatively impact project schedule, cost, quality, and stakeholder satisfaction. However, existing methods for managing EC risk have certain shortcomings in evidence selection and do not adequately consider the quality and reliability of evidence associated with EC risks. Evidence grading plays a crucial role in ensuring the reliability of decisions related to EC risks and can provide essential scientific and reliability support for decision-making. In order to explore the potential risks associated with architectural engineering changes (ECs) and identify the most significant ones, this study proposed a methodology that combines evidence grading theory and Latent Dirichlet Allocation (LDA) topic analysis means. Initially, the evidence-based grading theory served as the creation of a grading table for evidence sources related to EC risk. Specifically, we categorized the evidence sources into three levels based on their credibility. Subsequently, we selected evidence with higher credibility levels for textual analysis, utilizing the LDA topic model. This involved analyzing regulations, industry standards, and judgment documents related to EC, ultimately identifying the themes associated with EC risks. In addition, by combining EC risk topics with relevant literature, we identified factors influencing EC risks. Subsequently, we designed an expert survey questionnaire to determine the key risks and important risk topics associated with potential risks. The results show that by synthesizing information from both Class A and B evidence, a total of five prominent risk themes were identified, namely contract, technology, funds, personnel, and other hazards. Among them, the technical risk has the highest value, so it implies that the risk is the most important, and the key risks are engineering design defects, errors, and omissions.

Application of Lean Six Sigma to Reduce Delays in Engineering Changes

Quality methodologies have provided improvements for the organizations, increasing their competitive nature. Therefore, the present article was developed toward the reduction in the incidence of delays while developing engineering changes in an agricultural machine factory through the application of Lean Six Sigma concepts. An engineering change consists of a multitasking process for a product change. The article stages were developed following the sequence proposed by the DMAIC methodology. Several tools associated with Lean Six Sigma, such as process mapping, Ishikawa diagram, brainstorming, and Kanban were used in this article. Also, statistical tests were carried out to check the consistency of the goals established by the organization regarding the deadline for carrying out the analyzed tasks and for proposing new goals. The development of this article allowed the identification and elimination of root causality which culminated in delays during the implementation of engineering changes, and in turn, reduced the incidence of delays by 95%. The article shows the importance of companies applying scientific methodologies and statistical analysis in their processes to improve their results. Finally, it contributes to the scientific community, exposing an adaptation of tools and showing that the implementation of a set of management tools can contribute to process improvements.

Framework for Propagating Product Variability to Digital Assembly Instructions

As the amount of product iterations within high variety low volume assembly environments increases, the need for efficient management of variability and engineering changes within assembly instructions rises. This work proposes a methodology and a system architecture that facilitates the interplay between product lifecycle management (PLM) systems and digital assembly instructions, using an ISA-95 based data model. The framework consists of three main components: (1) a product variability model that integrates PLM information, (2) a digital instructions authoring tool, and (3) an assembly instruction variability model that connects to instruction platforms. Finally, the approach is applied to a validation case.