Moving Assembly Line Research Articles

The assembly line at Ford and transportation platforms: A historical comparison of labour process reorganisation

AbstractThis paper provides a historical comparison of the introduction of the moving assembly line at Ford Motor factories in the early 20th century, with the development of transportation platforms in the 21st century. The paper pushes back against the argument that either of these cases was based upon technological innovation, arguing that in neither case was new technology developed, but rather existing machinery was made use of. The paper argues that each case was built upon the reorganisation of the labour process, to increase the control that capital held over the labour process, increase the intensity of work and increase the levels of exploitation and surplus value extraction that workers are subjected to. By demonstrating this, it removes the technological façade that contemporary platforms hide behind, showing that they do not represent a qualitative break from the past, but rather the repackaging of old forms of work intensification.

Mixed-model moving assembly line material placement optimization for a shorter time-dependent worker walking time

Car mass production commonly involves a moving assembly line that mixes several car models. This requires plenty of material supplies at the line side, but available space is scarce. Thus, material is placed apart from ideal positions. Then, picking it up involves walking along the line. This time is non-productive and can encompass 10–15% of total production time. Thus, it is important to estimate and minimize it during production planning. However, the calculations are difficult because the conveyor continuously moves. Therefore, most literature bounds walking time by a constant, but this discards valuable potential. To better approximate it, we use a time-dependent V-shaped function. A comparison indicates that for a majority of instances, constant walking time estimates of 95% confidence are at least 51% higher. Then, we introduce a model to optimize material positions such that the model-mix walking time is minimized. This poses an NP-hard sequencing problem with a recursive and nonlinear objective function. Our key discovery is a lower bound on the objective of partial solutions, established by a Lagrangian relaxation that can be solved in quadratic time. Resulting branch and bound based algorithms allow to quickly and reliably optimize up to the largest real-world sized instances.

Selection of Industrial Robots Problem Using Complex Proportional Assessment (COPRAS) Method

Industrial Robot is continuously moving assembly line Intensive production required by to automate tasks A created one. Big, as heavy robots, they are a At standard conditions within an industrial plant are placed, and other All labour works Processes also revolve around them. Obvious robots too Common industrial robots. They are like human hands Looks, that's what they are Robotic arm or manipulator Also called hand. Many There with degree of independence Accents are obvious weapons Allow a wide range of motion.Industrial robots functional quality, Safety and productivity Improve almost every production Also widely used in companies. Nature of work to be done Depending, there are many types of robots now Commercially from different manufacturers are available. analysis of the COPRAS method in this complex proportional estimation (COPRAS) system in 1994 zavadskas, kaklauskas and sarka introduced by this time index values increase and decrease used to estimate. Alternative: ASEA-IRB 60/2, Cincinnati Milacrone T3-726, Cybotech V15 Electric Robot, Unimation PUMA 500/600, United States Robots Maker 110, Yaskawa Electric Motoman L3C.Evaluation preference: repeatability, memory capacity, manipulator reach, load capacity, maximum tip speed. The result it is seen that Cybotech V15 Electric Robot is got the first rank where as is the ASEA-IRB 60/2 is having the lowest rank.

Installation of Assembly Line in Most Economic Manner in Industry

An assembly line is a production process that divides labour by breaking up the manufacture of a product into steps that are completed in a pre-defined sequence Assembly lines were used in the late 1800s when workers used pulley systems to move products from one station to the next Henry Ford created the first moving assembly line, combining Olds' idea with conveyor belts, to mass-produce the Model T in 1913. The modern assembly line is used in many different industries, increasing output, cutting down costs, and boosting profits.

A reactive scheduling method for disturbances in aircraft moving assembly line

Aircraft assembly requires a large number of materials from hundreds of suppliers, and the uncertainty in material delivery has a negative impact on the assembly schedule. Existing researches stop short of introducing how to reschedule assembly activities in this context, so this paper addresses a reactive scheduling problem of aircraft moving assembly line with uncertain material delivery, and a bi-objective model is established. To absorb the advantage of machine learning-based method, we present a SVDD-based reactive scheduling method (SVDD-RS). Firstly, the models under different settings of disturbances in material delivery are solved, and the obtained policies are used to train the SVDD classification model in the offline training phase. In the online reactive scheduling phase, the trained SVDD classification model is used to make a preliminary decision for unstarted activities, and exact start-times are further determined by the local forward-looking algorithm. Computational experiments are carried out over practical cases generated from an aircraft assembly line to evaluate the performance of SVDD-RS. The results show that the SVDD classification model can quickly select policies with reasonable accuracy, and SVDD-RS can guarantee a quick response to the disturbance and produce a high-quality solution, compared to other existing reactive scheduling methods.

A branch-and-bound embedded genetic algorithm for resource-constrained project scheduling problem with resource transfer time of aircraft moving assembly line

Motivated by the resource transfer time between different stations in the aircraft moving assembly line, this study addresses the resource-constrained project scheduling problem with resource transfer time, which aims at minimizing the makespan of the project while respecting precedence relations and resource constraints. We assume that the resource transfer time is known and deterministic in advance. The resource transfer time and the precedence of activities are coupled with each other, which means that the transfer time of resource changes according to the precedence of activities, while the transfer time affects the decision of the precedence of activities at the same time. We present a linear mathematical model for the problem and propose a branch-and-bound embedded genetic algorithm with a new precedence-based coding method which adapts to the structure of the problem. A series of experimental tests reveal that the branch-and-bound embedded genetic algorithm outperforms the existing algorithm proposed in the literature in finding high quality solutions.

Automating nut tightening using Machine Learning

At the Volvo Truck assembly plant the repetitive task of nut tightening is not ideal regarding quality and ergonomic. The solution to both these issues would be to significantly increase the level of automation. However, automating this specific station requires solutions to two specific problems. The first problem is to find and identify what nuts that need to be tightened, since they are not always on the same position for this highly customized product. The second problem is that the automated solution needs to accommodate the working space which is a moving assembly line with human operators. This paper investigates how these two problems ban be solved using machine learning and collaborative robots. A realistic mockup of the assembly station has been created at Stena Industry Innovation Laboratory (SII-Lab) where all the testing has been done. The problem to identify the nuts to tighten is further complicated by the fact that some nuts are placed backwards for future further assembly which must be avoided. Therefore, the selected solution is to use supervised machine learning for object recognition. This way, the system can be trained to recognize both nuts that need to be tightened and those mounted backwards, and possible other objects needed. Tests have been conducted with different types of CNN (Convolutional Neural Network) algorithms. Results have been very successful, and the test setup has successfully managed to connect the whole task of identifying the correct nuts and move the collaborative robot to that specific position.

Line side placement for shorter assembly line worker paths

Placing material containers at moving assembly lines is an intriguing problem because each container position influences worker paths. This optimization is relevant in practice as worker walking time accounts for about 10–15% of total work time. Nonetheless, we find few computational approaches in the literature. We address this gap and model walking time to containers, then optimize their placement. Our findings suggest this reduces walking time of intuitive solutions by an average of 20%, with considerable estimated savings. To investigate the subject, we formulate a quintessential optimization model for basic sequential container placement along the line side. However, even this core problem turns out as strongly NP-complete. Nonetheless, it possesses several polynomial cases that allow to construct a lower bound on the walking time. Moreover, we discover exact and heuristic dominance conditions between partial placements. This facilitates an exact and a truncated branch-and-bound solution algorithm. In extensive tests, they consistently deliver superior performance compared to several mixed integer programming and metaheuristic approaches. To aid practitioners in quickly recognizing instances with high optimization potential even before performing a full optimization, we provide a criterion to estimate it with just few measurements.

A flexible resource investment problem based on project splitting for aircraft moving assembly line

PurposeIn response to the station design and flexible resources allocation of the aircraft moving assembly line, a new problem named flexible resource investment problem based on project splitting (FRIP_PS), which minimizes total cost of resources with a given deadline are proposed in this paper.Design/methodology/approachFirst, a corresponding mathematical model considering project splitting is constructed, which needs to be simultaneously determined together with job scheduling to acquire the optimized project scheduling scheme and resource configurations. Then, an integrated nested optimization algorithm including project splitting policy and job scheduling policy is designed in this paper. In the first stage of the algorithm, a heuristic algorithm designed to get the project splitting scheme and then in the second stage a genetic algorithm with local prospective scheduling strategy is adopted to solve the flexible resource investment problem.FindingsThe heuristic algorithm of project splitting gets better project splitting results through the job shift selection strategy and meanwhile guides the algorithm of the second stage. Furthermore, the genetic algorithm solves resources allocation and job schedule through evaluation rules which can effectively solve the delayed execution of jobs because of improper allocation of flexible resources.Originality/valueThis paper represents a new extension of the resource investment problem based on aircraft moving assembly line. An effective integrated nested optimization algorithm is proposed to specify station splitting scheme, job scheduling scheme and resources allocation in the assembly lines, which is significant for practical engineering applications.

Analysis of Henry Ford’s contribution to production and management

Henry Ford is widely known as the car constructor, the founder of the Ford Motor Company, the pioneer of mass production and the inventor of the moving assembly line, which many consider as the world’s greatest contribution to manufacturing. In 1908, Ford started production of the Ford Model T, which has become one of the most successful automobile in automotive history. But his contribution far surpasses these excellent accomplishments. What are not well known are Ford’s contributions to the just-in-time production, product platforming, mass customization, vertical integration, designs for maintainability, ergonomic considerations, employee management and other features of the manufacture. The Ford’s production system has become the characteristic American mode of production widespread all over the world.

A resource investment problem based on project splitting with time windows for aircraft moving assembly line

The aircraft moving assembly line is considered to be a large complex project consisting of some sub-projects with corresponding jobs and each sub-project corresponds to each workstations of the assembly line. This paper presents a new extension of the project scheduling problem, Resource Investment Problem based on Project Splitting with Time Windows (RIPPS_TW), in view of the characteristics of simultaneous execution of multiple workstations based on line-side shared resources and the situation of unavailable resource periods known in advance. A new concept named project splitting is proposed to divide a project into multiple sub-projects by changing the precedence constraints of jobs during scheduling, which is beneficial for improving the scheduling efficiency of the problem. To solve RIPPS_TW, we design a new mathematical optimization model and a two-stage iterative loop algorithm in this paper. In the first stage, a heuristic algorithm is applied to get the project splitting plan, then in the second stage an improved genetic algorithm combined with branch-and-bound is used to schedule jobs affected by resource time windows. In addition, a series of computational experiments are carried out, and the effectiveness of the algorithm is verified by comparison with other literature algorithm.

Integrated modelling and algorithm of material delivery and line-side storage for aircraft moving assembly lines

This paper considers the material supply problem for aircraft moving assembly lines. Distinguished from general automobile assembly lines, multiple parallel jobs are assembled concurrently and durations of assembly jobs are quite long, thus amounts of materials are orderly stored at the line-side space at the same time. In addition, the line-side space should be reused in the time dimension. With these characteristics, decisions on line-side storage of materials were introduced on the basis of material batching and tow-trains scheduling problems. An integrated decision–making mathematical model with the objective of minimising the number of deliveries was established. A hybrid endocrine-immune algorithm (HEIA) was proposed to jointly make decisions on the delivery batch, delivery time and storage positions of each job’s materials. Numerical experiments with the real-world data and randomly generated instances validate the effectiveness and efficiency of HEIA.

Mixed-Model Assembly Lines and Their Effect on Worker Posture and Recovery Time

Aim: The aim of this study is to describe the relationship between cycle-to-cycle task variations in mixed-model assembly lines and workers’ exposure to poor posture and insufficient recovery time and provide guidelines for mixed-model assembly line (MMAL) design. Background: In the US, 17.6 out of 100,000 manufacturing workers suffer musculoskeletal injuries while conducting tasks in assembly lines which may be paced (e.g. continuously moving) or self-paced (e.g. asynchronous indexing). MMALs produce multiple products in the same line. Pacing must be carefully considered to achieve productivity targets while providing workers sufficient time to complete the tasks and recover. The effect of pacing on worker fatigue and exertions is not well understood. In MMALs, the complexity of completing multiple work methods may increase exposure to these risk factors. These concerns also affect hospital kitchens. While kitchen workers don’t have standardized work cycles, the work tasks (e.g. get, move, put) resemble the cyclic nature of manufacturing plants. Studies have been conducted to describe the effect of cycle-to-cycle task variations on worker posture and recovery time in MMALs (Carrasquillo, Armstrong, and Hu 2011, 2016, 2017). Methods: Three studies were conducted: (1) field observation of continuous moving assembly line - hospital kitchen, continuous moving conveyor; (2) conveyor type, product mix/sequence laboratory experiment - simulated assembly task conducted under 9 conveyor and product mix/sequence configurations; (3) field observation of on demand, self-paced assembly line with varying demand - hospital kitchen where patients placed orders at any time. Workers were video recorded and worker posture and recovery time were assessed. Posture tracking was used in laboratory study (2). Results: The self-paced assembly lines (asynchronous indexing, and manual), provided workers control over their work pace. They reached within the reach envelop more frequently than in the paced, continuous moving conveyor (43% vs. 86% –99%). The workers on the self-paced assembly line had the greatest average recovery time (31.8%), the worker on the continuous moving conveyor had 9.5% and the subjects on the asynchronous indexing conveyor had 0.7% recovery time. Discussion and conclusions: In study (1) increases in variations increased the frequency of workers reaching outside the reach envelope and reduced recovery time. In Study (2), the self-paced, asynchronous indexing mixed-model assembly line was the most effective configuration in reducing the frequency of reaching unnecessarily to complete the job. However, subjects didn’t reduce the pace to recover; instead, they worked continuously. The workers’ pace in the Study (3) assembly line was constrained by the process time. In it, workers, chose to reach outside the reach envelop 14% of the time; less frequently than in Study (1). The process time provided time for recovery. It exceeded 10%, more than in any of the other assembly line configurations. These findings demonstrate that as work variations increase, so does the frequency of reaching while the recovery time is. Self-pacing reduced but did not eliminate awkward postures. Participants reached ahead and completed the assigned tasks before the work objects were within the reach envelope. Ensuring that a minimum work time is maintained for each product type is essential to providing workers with sufficient time for recovery.

The rise of 3-D printing: The advantages of additive manufacturing over traditional manufacturing

The use of additive manufacturing technologies in different industries has increased substantially during the past years. Henry Ford introduced the moving assembly line that enabled mass production of identical products in the 20th century. Currently, additive manufacturing enables and facilitates production of moderate to mass quantities of products that can be customized individually. Additive manufacturing technologies are opening new opportunities in terms of production paradigm and manufacturing possibilities. Manufacturing lead times will be reduced substantially, new designs will have shorter time to market, and customer demand will be met more quickly. This article identifies additive manufacturing implementation challenges, highlights its evolving technologies and trends and their impact on the world of tomorrow, discusses its advantages over traditional manufacturing, explores its impact on the supply chain, and investigates its transformative potential and impact on various industry segments.

Should Robotics Need Ethical Codes? Robotics and ERP

The world of manufacturing has changed since Henry Ford first utilized a moving assembly line for the mass production of an entire automobile on December 1st, 1913. His innovation reduced the time it took to build a car from more than 12 hours to two hours and 30 minutes. Ford attested that the increased efficiency and reduced labor cost associated with the compartmentalization of production was almost unstoppable. Since that time, manufacturing robots handle a huge variety of practical tasks using much greater speed and efficiency than human workers.

Model Predictive Control Approach for assembling Large Components in Motion

The assembly of large components is characterized by a large number of varieties and a high process influence by environmental factors. For this reason the assembly in the field of truck manufacturing is in most cases carried out as a moving line with a high percentage of manual processes. Automation of these assembly process, albeit desired from a quality viewpoint, is presently limited as it requires the workpiece to be stationary. The ability to keep the workpiece on the moving assembly line while keeping the used handling systems flexible can enable business benefits. By assembling for example the windshield of a truck in motion, lead times can be shortened and floor space requirements can be reduced while at the same time the quality can be increased. To enable assembly in motion of large components, it must be possible to synchronize the movement between the handling system and the moving workpiece placed on an unguided transport system. Within this article we describe a Model Predictive Control Approach for robotic assembly of large components on moving assembly lines. One approach to synchronize the motion between product and robot is a direct metrological reference between them. This can be done by using an iGPS metrology system in which every handling system can be referenced to every work piece throughout the whole production line. The measurement system must be able to detect sufficiently small movements to ensure a reliable synchronization and thereby a high quality of the assembly process. The interaction of several systems makes for a number of restrictions. Here especially the delay times of the measuring system and the preset position for the robot have to be taken into consideration. To control the assembly process, a model predictive control loop is necessary. Only this control approach can meet the restrictions which are strictly necessary for the assembly. This article shows the first results and describes a demonstrator which has been set up to mount a windshield on a moving truck cabin with the help of a robot.

Optimization of aircraft moving assembly line scheduling problem considering material delivery

Optimization of aircraft moving assembly line scheduling problem considering material delivery

Effect of customer demand in a self-paced mass customization assembly line on worker posture and recovery time

Aims: This study aimed to evaluate worker posture, hand activity level (HAL), and recovery time on workers of a hospital kitchen with an asynchronous indexing assembly line during periods of different customer demand. Background: Workers in hospital kitchens use their hands to prepare and transport food around the kitchen. Unsteady customer demand leads to times when workers are continuously busy with little recovery. These working conditions result in high joint loads and insufficient recovery, causes of work-related musculoskeletal disorders (WMSDs) (NCRC, 2001). Injuries caused by repetitive motion and overexertion are the greatest source of reported injuries (Bureau of Labor and Statistics, 2014). In spite of this, studies have not examined the effect of workplace design, production volume variations, and product complexity and their effect on workers’ exposure to these risk factors. A study at a hospital kitchen mixed-model continuous moving assembly line found that the frequency of reaching increased and the recovery time decreased as variations increased (Carrasquillo, Armstrong, and Hu, 2011). Methods: An observation study was conducted at a hospital kitchen with 2 principal areas: food preparation and tray assembly. High and low demand periods were identified from historical data. Workers were video recorded during each demand period. Videos were used to calculate frequency of reaching and recovery time, and to rate the hand activity level (HAL). Reaches were categorized as: (1) Lower arm, (2) Upper arm, (3) Torso twist/bent. The upper arm and torso twist/bent reaches were further categorized into necessary and unnecessary. Results: HAL – Overall, it increased by 1.2 (from 5 to 6.2) as the demand increased (p < 0.05). Reach - The proportions of unnecessary upper arm and torso reaches were not significantly affected by changes in demand (p > 0.05). Recovery time – Increase in customer demand resulted in a decrease in the proportion of recovery time (p < 0.05). Overall, recovery decreased from 40% to 22%. Discussion and conclusion: Results show that HAL increased as the customer demand increased except for the worker on tray station 2, who had no significant difference between the two periods. This worker was required to wait for meals to be cooked before her job could be completed. This resulted in a greater amount of recovery (37% - 53%). In contrast, the proportion of unnecessary reaches did not change significantly with demand. However, the upper arm and the torso to bend/twist reaches represented more than half of the reaches. While many of them were necessary, >20% were unnecessary. In this assembly line, workers were in control of the pace and recovery time was available more than 10% of the time, factors that reduce workers’ exposure to risk of WMSDs. Although previous studies have found self-paced work increased risk due to decreased recovery (Dempsey, Mathiassen, Jackson, and O’Brian, 2010), this work demonstrated that it might not be the sole factor affecting risk. When products and production time vary and workers must wait for process (cooking), self-pacing appears to provide workers with some control over the frequency of unnecessarily reaching and recovery time.

America's Assembly Line [Book Reviews

That the Russians, the Germans, French, English, Japanese, and Danes all showed intense interest in making Ford's cars and in Fordism (his practices) testifies to not only the importance of this subject of the American assembly line production process, but to the appropriateness of this book published by M.I.T. Press in 2013. Fordism came into its first major employment one hundred years earlier in 1913. This book is about more than Fordism; it's concerned with the history of the assembly line, of which Fordism is, historically, its major manifestation. Indeed, the author is at pains to point out that Ford did not invent the assembly line method of production. But he did bring it to a level of popularity and employment in the production of the Model T Ford such that his name is inextricably connected with it. The book reduces Fordism to a set of conditions: 1) There is division of labor - each worker has a set of well-defined relatively simple tasks to be performed on a car being moved along on a moving assembly line. 2) The parts that the laborer applies in the assembly process are interchangeable. There is no ad hoc filing, grinding, etc., to make them fit in place. 3) Manufacturing employs specialized machines that have a single function. 4) Machines are not grouped by type but are placed where they are needed. 5) Parts and assemblies are moved automatically from one stage of production to the next - they're not shifted by workers whose job is assembly. There is no wearisome heavy lifting or towing. 6) There is enhancement of production by electrification and good lighting. The author shares with most historians of technology an evolutionary theory of invention, which holds that new devices and practices emerge gradually. The assembly line is an example - it was developing before Ford. The book is roughly divided into two epochs, one of 60 years from 1913 to 1973, the other of 40 years from then to 2013. In 1973 American auto workers saw wages reach an all time (inflation adjusted) high - a level to which it hasn't ever returned. But it became evident by the early 1970s that Detroit was losing market share to not only European makers like Volkswagen but most especially to the Japanese .Part of the answer - an ironic one-is that the Japanese success derives dramatically from the work of W. Edwards Deming, an American, who was an expert on the use of statistics in wartime quality control.

Constraint identification techniques for lean manufacturing systems

The first step in the Theory of Constraints (TOC) methodology is to identify the constraint. Several methods have been recommended in literature, such as looking for a backup of inventory (i.e., the operation that the inventory is waiting for is the constraint), or using linear programming or other analytical models. Yet, these methods may not be useful in a matured lean environment, which may have moving assembly lines where constraints are not obvious. This paper proposes three new methods for this purpose. The first method, Flow Constraint Analysis, takes a holistic view and evaluates whether the customer's demand is being satisfied. This evaluation is made by comparing the takt times and the cycle times of resources in the manufacturing system in order to identify the constraint(s). The second method, Effective Utilization Analysis, can be employed to pinpoint the location of the system constraint to a specific process or station. The actual production throughput is compared against the ideal capacity of the system to locate the bottleneck. This method is based on the relationship between WIP, bottleneck rate and lead time for a constant work in process (CONWIP) system. The third method, Quick Effective Utilization Analysis, can be used when there is little or no historical line performance data available. A case study of these methods applied to an actual production facility is presented.