This document provides details for an engine module project including main deliverables, customers, scope, structure, required resources, product mix, line design, assembly flowchart, feeding process, and manpower needs. The project aims to supply engine modules to a production line to meet demand. Key aspects include building C9 and C13 engine modules, maintaining a 1-3 unit buffer, and satisfying a 5 minute takt time. Russell Sims is the project manager overseeing assembly with a team of 9 across subassembly, two stations, testing, and maintenance of a 5 unit/hour line speed.
Electronic simulated smoking devices, commonly known as e-cigarettes or e-cigs, came into being in the early 1960's. An electronic cigarette is smoked by heating the liquid nicotine to create a tobacco-flavored aerosol or the real tobacco to volatilize tobacco flavors. These simulated smoking devices have grown in acceptance and popularity because it is believed that they are less toxic to the user than the conventional method of inhaling a desired active ingredient through burning a source of that ingredient and inhaling the products of that combustion, including carcinogens. Without the toxic products of combustion being present, there is a greatly reduced concern about "secondhand smoke," as well. They have also grown in popularity due to people's fascination with gadgetry.
US20150347850 illustrates an IoT (Internet of Things) AR (Augmented Reality) application in a smart home. A smart home IoT device communicates via a local network to a user AR device (e.g., smartphone) for providing the tracking data. The tracking data describes the smart home IoT device. The AR devices can recognize the smart home IoT device in the camera view based on the tracking data. Once the smart home IoT device is identified in the camera view, the AR application can augment the camera view with additional information and control interface about the smart home IoT device. The user can control the smart home IoT device using the AR device.
Hotseat joined the Games for Health Conference's 3rd Annual Out & About Mobile Serious Games Conference to share what we're doing to turn short social breaks into meaningful activity.
Patents are a good information resource for obtaining IoT (Internet of Things) R&D status in a company. Followings are some examples of patents that provide Samsung IoT R&D status: Energy Harvesting Sensor Device, Smart Lighting, Smart Appliance, Smart TV and Smart Healthcare.
Large Scale Feature Aggregation Using Apache Spark with Pulkit Bhanot and Ami...Databricks
Aggregation based features account for a quarter of the several 1000s features used by the ML-based decisioning system by the Risk team at Uber. We observed several repetitive, cumbersome steps needed for onboarding a feature, every single time. Therefore, to accelerate developer velocity, and to enable Feature Engineering at scale, we decided to develop a generic spark based infrastructure to simplify the process to no more than a simple spec file, containing a parameterized query, along with some metadata on where the feature should be aggregated and stored.
In the presentation, we will describe the architecture of the final solution, highlighting some of the advanced capabilities like backfill support and self-healing for correctness. We will showcase how, using data stored in Hive and using Spark, we developed a highly scalable solution to carry out feature aggregation in an incremental way. By dividing data aggregation responsibility across the realtime access layer, and the batch computation components, we ensured that only entities for which there is actual value changes are dispersed to our real-time access store (Cassandra). We will share how we did data modeling in Cassandra using its native capabilities such as counters, and how we worked around some of the limitations of Cassandra. We will also cover the details about the access service how we do different types of feature stitching together. How, based on our data model we were able to ensure that all the feature for an entity with the same aggregation window, were queried via a single query. Finally, we will cover some of the details on how these incremental aggregated features have enabled shorter turnaround times for the models using such features.
HISTORY. I was the author of this Public Deliverable for the OMI/MODES Project (ESPRIT 20.592 - TR124) in 1998. It is an interesting to see what has changed in 15years. (See http://cordis.europa.eu/esprit/src/omi20592.htm)
Electronic simulated smoking devices, commonly known as e-cigarettes or e-cigs, came into being in the early 1960's. An electronic cigarette is smoked by heating the liquid nicotine to create a tobacco-flavored aerosol or the real tobacco to volatilize tobacco flavors. These simulated smoking devices have grown in acceptance and popularity because it is believed that they are less toxic to the user than the conventional method of inhaling a desired active ingredient through burning a source of that ingredient and inhaling the products of that combustion, including carcinogens. Without the toxic products of combustion being present, there is a greatly reduced concern about "secondhand smoke," as well. They have also grown in popularity due to people's fascination with gadgetry.
US20150347850 illustrates an IoT (Internet of Things) AR (Augmented Reality) application in a smart home. A smart home IoT device communicates via a local network to a user AR device (e.g., smartphone) for providing the tracking data. The tracking data describes the smart home IoT device. The AR devices can recognize the smart home IoT device in the camera view based on the tracking data. Once the smart home IoT device is identified in the camera view, the AR application can augment the camera view with additional information and control interface about the smart home IoT device. The user can control the smart home IoT device using the AR device.
Hotseat joined the Games for Health Conference's 3rd Annual Out & About Mobile Serious Games Conference to share what we're doing to turn short social breaks into meaningful activity.
Patents are a good information resource for obtaining IoT (Internet of Things) R&D status in a company. Followings are some examples of patents that provide Samsung IoT R&D status: Energy Harvesting Sensor Device, Smart Lighting, Smart Appliance, Smart TV and Smart Healthcare.
Large Scale Feature Aggregation Using Apache Spark with Pulkit Bhanot and Ami...Databricks
Aggregation based features account for a quarter of the several 1000s features used by the ML-based decisioning system by the Risk team at Uber. We observed several repetitive, cumbersome steps needed for onboarding a feature, every single time. Therefore, to accelerate developer velocity, and to enable Feature Engineering at scale, we decided to develop a generic spark based infrastructure to simplify the process to no more than a simple spec file, containing a parameterized query, along with some metadata on where the feature should be aggregated and stored.
In the presentation, we will describe the architecture of the final solution, highlighting some of the advanced capabilities like backfill support and self-healing for correctness. We will showcase how, using data stored in Hive and using Spark, we developed a highly scalable solution to carry out feature aggregation in an incremental way. By dividing data aggregation responsibility across the realtime access layer, and the batch computation components, we ensured that only entities for which there is actual value changes are dispersed to our real-time access store (Cassandra). We will share how we did data modeling in Cassandra using its native capabilities such as counters, and how we worked around some of the limitations of Cassandra. We will also cover the details about the access service how we do different types of feature stitching together. How, based on our data model we were able to ensure that all the feature for an entity with the same aggregation window, were queried via a single query. Finally, we will cover some of the details on how these incremental aggregated features have enabled shorter turnaround times for the models using such features.
HISTORY. I was the author of this Public Deliverable for the OMI/MODES Project (ESPRIT 20.592 - TR124) in 1998. It is an interesting to see what has changed in 15years. (See http://cordis.europa.eu/esprit/src/omi20592.htm)
Toyota Motor Corporation's vehicle production system is a way of "making things" that is sometimes referred to as a "lean manufacturing system" or a "Just-in-Time (JIT) system," and has come to be well known and studied worldwide.
OnAndroidConf 2013: Accelerating the Android Platform BuildDavid Rosen
Presented at the OnAndroidConf, October 22 2013, http://www.onandroidconf.com/sessions.html
Abstract:
Optimizing the Android build environment to perform at world-class level is a big challenge for many Android device and chipset makers today. Churning through thousands of platform builds per week requires laser-focus on high-performance infrastructure and tooling. If you’re looking at improving your overall engineering and developer productivity, the software build use case is an obvious area to prioritize.
This technical talk will focus on the following aspects of the Android platform build:
Common Android platform build challenges and opportunities with real-life production references
The various Android build use cases and their needs – full integration and release builds, developer incremental builds
Evolution of the Android build and codebase with trends and statistics
Detailed technical analysis of the Android platform build, highlighting opportunities for improvements
Proposed solutions and technical tricks to optimize an Android software build environment
These are the slides from my workshop at IoTFuse on April 24 2019 where I talked about how uTensor enables Machine Learning inference on deeply embedded Cortex M edge IoT devices. Of course it works first and best with Mbed OS and Pelion.
Credit to Neil Tan for original creation of the slides and the uTensor Project.
Fiatech 2014 - Computer Simulation of Pipe Fabrication, Ramzi LabbanCCT International
Pipe spool fabrication is major component of construction operations on large industrial projects. The nature of spooling is relatively short term involving complex construction process and riddled with uncertainty due to the intrinsic unique nature of its outputs and the numerous factors affecting its activities.
With this in mind, it is important for all stakeholders to have a good grasp of the performance of pipe fabrication shops and their ability to meet the site pipe installation schedules.
The standard disc brake of a 4-wheeler model was done using Autodesk Mechanical Simulation through which the properties like deflection, heat flux and temperature of disc brake model were calculated. It is important to understand action force and friction force on the disc brake new material, how disc brake works more efficiently, which can help to reduce the accident that may happen at anytime.
1. Engine Unit Module Project 1/11
author: Russell Sims
date: May 28, 2008
Project Description
Main Deliverables of the Project
1. To provide C9 and C13 engine modules to the SpeedLine at a rate to satisfy takt
time during maximum customer demand cycles.
2. Engine Unit shall maintain a buffer to feed the SpeedLine of not less than 1 and
no more than 3 engine unit of each type.
Customer of the Project
1. Customer of this project is the SpeedLine in general and specifically station 2 of
the SpeedLine.
Scope of the Project
1. This project shall encompass all engine models produced for Lokotracks in the
Columbia Manufacturing facility.
2. Current product mix consists of C13 Modules for LT200HP and C9 Modules for
LT106. We are currently forecast to build LT1213 and LT1213S Lokotracks which
will take a slightly Modified C13 Module.
(Remainder of page intentionally blank)
2. Engine Unit Module Project 2/11
author: Russell Sims
date: May 28, 2008
Structure of the Project
Time
Study
Make/Buy
Anaysis
Layouts Sub
and assembly
Balancing breakouts
Project Classification
1. This Project is unclassified and contains no confidential data.
Project Owner
1. LT Manufacturing Engineer is the owner of this project
Project Manager
1. Russell Sims is the LT Implementation Project Manager.
Required Human Resources (Internal)
1. Engine Module Assembly Team consisting of 1 team leader, five team members
and a material services person.
2. Supervisor element
3. Engine Unit Module Project 3/11
author: Russell Sims
date: May 28, 2008
Required Human Resources (External)
1. Intern for data collection
Other Required Resources
1. Material handling equipment, IT services, tools and tool boxes, communications
devices, maintenance support.
Product Mix
Monthly Monthly Daily
Annual average max. max.
demand demand Daily average demand demand demand
LT106 C-9 140 11.7 0.56 20 0.95
LT1213 / -S C-13 110 9.2 0.44 15 0.71
LT200HP C-13 100 8.3 0.40 25 1.19
Total demand 350 29.2 60
days
Annual time available 220 hours
Monthly time available 21 Total assembly process time 24
Planned takt timemin 5.03
hrs/month
Time available in 1
shift 168 Calculated number of stages 4.8
Time available in 2 Selected number of stages
shifts 336 (takts) 5
Time available in 3
shifts 504
hours
Annual time available 1760
Monthly time available 168
Line takt timemin 5.03
unit/hour
Line speedmax 0.20
4. Engine Unit Module Project 4/11
author: Russell Sims
date: May 28, 2008
Product Architecture Review
Demand Rates & Available Time
Monthly Daily Monthly Daily
Annual average average max. max.
demand demand demand demand demand
LT106 C-9 140 11.7 0.56 20 0.95
LT1213/LT1213S C-13 110 9.2 0.44 15 0.71
LT200HP C-13 100 8.3 0.40 25 1.19
Total demand 350 29.2 60
days
Annual time available 220
Monthly time available 21
hrs/month
Time available in 1 shift 168
Time available in 2 shifts 336
Time available in 3 shifts 504
5. Engine Unit Module Project 5/11
author: Russell Sims
date: May 28, 2008
Line Design : Takt timemin and Line speedmax
1. Takt time is the pace of production to match the rate of customer demand.
2. Takt time is the heartbeat of the whole production system
Formula:
Monthly
Annual average Takt timemin = Time available/Total demand
demand demand
LT106 C-9 140 11.7 Line Speedmax =1/Takt time
LT1213/ LT1213S C-13 110 9.2
LT200HP C-13 100 8.3
Example:
Total demand 350 29.2 Takt timemin = 1760 h/350 units =5.02 h
hours Line Speedmax =1/2.03 h = 0.20 unit/h
Annual time available 1760
Monthly time available 168 (Note) This line design calculation is based on
production mainly in one shift)
Line takt timemin 5.03
unit/hour Line Design
Line speedmax 0.20
Product-specific high season demand rates
Product-specific demand rate for materials
Monthly
Annual max. planning formula:
demand demand
LT106 C-9 Rate = Product Demand/days of planning
140 20 period
LT1213/ LT1213S C-13
110 15 Example High Season Rate for LT106 C-9
LT200HP C-13
100 25
Rate = 20 pcs/21 days = 0.95 pcs/day
Total demand
350 80
6. Engine Unit Module Project 6/11
author: Russell Sims
date: May 28, 2008
Annual Monthly average
demand demand
LT106 C-9 140 11.7
LT1213 / -S C-13 110 9.2
LT200HP C-13 100 8.3
Total demand 350 29.2
unit/hour
Line speedmax 0.20
hrs/month unit/month
Time available in 1 shift 168 33 Line outputmax in 1 shift
Time available in 2 Line outputmax in 2
shifts 336 67 shifts
Time available in 3 Line outputmax in 3
shifts 504 101 shifts
Formula:
Line outputmax = Available timemax x Line Speedmax
Example: In 2 shift time available 336 h/month.
Line speedmax=0, 20 unit/h (takt time 5.03 h)
Max. Line output in 2-shift= 336 x 0, 20 = 67 units/month
Needed daily run hours = Total demand / Line Speedmax
Example: Demand 50 unit/month
Needed run hours = 50/0.20=250 h/month = 11.9 h/day
Two Shifts Needed During High Season
7. Engine Unit Module Project 7/11
author: Russell Sims
date: May 28, 2008
Fast and Slow Options
1. There are no fast or slow options currently available for the engine modules. We
will revisit this subject if the need arises.
Consumption Rate for Fast Options
1. There are no fast options currently available for the engine modules. We will
revisit this subject if the need arises.
Minimum Lead Time from Work Order to Assembly
Work Order
Recieved Engine Mounted on
(SpeedLine) Lokotrack
Minimum Lead Time 2.5 D
Minumum Customer response time 3.5 D
Split Minimum SpeedLine Lead Time into Sub-process Lead Times
Work Order
Recieved Engine Mounted on
(SpeedLine) Lokotrack
Minimum Lead Time 2.5 D 3 Stage production needed
based on lead times and
Minumum Customer response time 3.5 D
throughput calculations
Sub assembly Station 2 Testing
Station 3
1.05 D .66 D .5 D
.71 D
8. Engine Unit Module Project 8/11
author: Russell Sims
date: May 28, 2008
Preliminary Line Balancing
500
Engine warm-up/Hydraulic Test
450 Electrical Test
Electrical connection
400
Hydraulic connection
350 Priming Deisel
Hydraulic oil
300 Bottom Plates (2)
Ball Valves (2)
250
Exhaust
200 Door Work
Fuel Water Sep
150 Fuel Cooler
Turbo Tube
100
Hood Bar
50 Hood Prep
Radiator Bar/Light Mast
0 Oil Fill **
Sub Assembly Sta 2 Man 1 Sta 2 Man 2 Sta 3 Man 1 Sta 3 Man 2 Testing Required Time
(Remainder of page intentionally blank)
9. Engine Unit Module Project 9/11
author: Russell Sims
date: May 28, 2008
Preliminary Assembly Flowchart
•Mechanical Assembly 3.12h
Sub •Electrical Assembly .58h
•Coolant Assembly 2.48h
Assembly •Hood Assembly .53h
•Tank Assembly 1.72h
•Wiring 3.03h Meeting
Station 2 •Hose Assembly 1.06h
•Tank Installation .52h Customer
•Exhaust .48h
Demand
•Bottom Plates .5h
•Platforms .33h
Station 3 •Turbo Tube .63h
•Oil Fill 1h
•Intake Radiator .72h
•Test the Engine Unit 2.5h
Testing •Minor Adjustments .5h
10. Engine Unit Module Project 10/11
author: Russell Sims
date: May 28, 2008
Feeding process flowchart (synchronization)
Click here to follow link to actual data.
11. Engine Unit Module Project 11/11
author: Russell Sims
date: May 28, 2008
Needed manpower for each station and cell
Hours
Planned Takt
Time 7.167
Total
work
content Capacity Balancing
time Resource Resource available loss
[hours] needed allocated [hours] [hours]
Sub Assembly 6.7 0.9 2 0.0 -6.7
Station 2 11.5 1.6 4 0.0 -11.5
Station 3 11.2 1.6 1 0.0 -11.2
Test 1.9 0.3 2 0.0 -1.9
Total 31.3 4.4 9.0 0.0 -31.3